# Payerset — Full Content Export
> Payerset is a healthcare price transparency data company. We turn payer
> Transparency in Coverage (TiC) files and Hospital Price Transparency
> machine-readable files (MRFs) into clean, analyzable contracted-rate and claims
> data so health systems, providers, health-tech teams, employers, and researchers
> can benchmark reimbursement, prepare for managed care contract negotiations, and
> understand competitive rates in their market.
This file (llms-full.txt) is a full-text export of Payerset's reference material
and articles, intended for AI and retrieval engines. For a curated index of the
site, see https://www.payerset.com/llms.txt. Content is generated from the live site and updated on
every deploy.
---
# Glossary
## Allowed Amount
The allowed amount (or allowed charge) is the maximum dollar amount a health plan considers payable for a covered service. For in-network care it equals the plan's negotiated rate; for out-of-network care it is a plan-determined amount. The allowed amount — not the provider's list price — is the basis for calculating plan payment and patient cost-sharing.
## Billing Code
A billing code is a standardized identifier for a medical service, procedure, drug, or supply used on healthcare claims and in price transparency files. Common code systems include CPT, HCPCS, ICD-10, DRG, and NDC. Negotiated rates in machine-readable files are reported per billing code, which is why accurate code mapping is essential to comparing rates.
## CPT and HCPCS Codes
Current Procedural Terminology (CPT) codes, maintained by the American Medical Association, describe medical, surgical, and diagnostic services. The Healthcare Common Procedure Coding System (HCPCS) extends CPT to cover items such as drugs, supplies, and equipment. Together they are the primary code sets used to report and price professional and outpatient services.
## Diagnosis-Related Group (DRG)
A Diagnosis-Related Group (DRG) is a classification that groups inpatient hospital stays with similar clinical characteristics and resource use into a single payment category. Payers often reimburse inpatient care as a fixed amount per DRG rather than per individual service, making DRG-based rates a key benchmark for hospital inpatient contracting.
## Hospital Price Transparency
Hospital Price Transparency is a U.S. federal rule, effective January 1, 2021, that requires every hospital to publish a machine-readable file of standard charges — gross charges, discounted cash prices, and payer-specific negotiated rates — along with a consumer-friendly display of shoppable services. CMS has progressively strengthened the required file format and enforcement.
## In-Network Rate
An in-network rate is the price a health plan and a provider have contractually agreed to for a covered service when the provider participates in the plan's network. In-network rates are typically lower than a provider's billed charges and are disclosed in Transparency in Coverage machine-readable files.
## Machine-Readable File (MRF)
A machine-readable file (MRF) is a structured data file — usually JSON or CSV — that payers and hospitals publish to comply with price transparency rules. MRFs list negotiated rates by payer, provider, and billing code. Their large size, inconsistent schemas, and data-quality issues make them difficult to analyze without significant normalization and enrichment.
## Negotiated Rate
A negotiated rate is the dollar amount a health plan and a healthcare provider have agreed to as payment for a specific covered service. These rates were historically confidential but are now disclosed under federal price transparency rules, forming the basis for reimbursement benchmarking and managed care contract negotiations.
## National Provider Identifier (NPI)
A National Provider Identifier (NPI) is a unique 10-digit number assigned to U.S. healthcare providers — both individuals and organizations — by CMS. NPIs identify which providers a negotiated rate applies to in machine-readable files, making them essential for linking rates to specific physicians, facilities, and health systems.
## Out-of-Network Rate
An out-of-network rate refers to the amounts payers report for care delivered by providers outside their contracted network. Under Transparency in Coverage, plans publish historical out-of-network allowed amounts and billed charges, which help reveal payment patterns where no negotiated contract exists.
## Payer
A payer is an organization — typically a health insurance company, group health plan, or third-party administrator (TPA) — that finances or reimburses the cost of healthcare services. Payers negotiate rates with providers and are required to publish those rates under the Transparency in Coverage rule.
## Reimbursement Benchmarking
Reimbursement benchmarking is the practice of comparing a provider's negotiated rates against those of peers and competitors across payers, services, and geographies. Using price transparency data, organizations benchmark rates to see where they stand, identify underpaid services, and build evidence for contract negotiations.
## Taxpayer Identification Number (TIN)
A Taxpayer Identification Number (TIN) is the IRS-issued number that identifies the business entity billing for healthcare services. In price transparency files, TINs help associate negotiated rates with the organization — such as a medical group or hospital — that holds the payer contract, complementing the provider-level NPI.
## Transparency in Coverage (TiC)
Transparency in Coverage (TiC) is a U.S. federal rule requiring health insurers and group health plans to publish their in-network negotiated rates and out-of-network allowed amounts as machine-readable files, updated monthly. Effective for plan years beginning in 2022, TiC files cover nearly every commercial plan and are the most comprehensive public source of payer rate data.
---
# The Price Transparency Project — Articles
---
## Same Surgery, Different Price: Who Owns the ASC Predicts What the Payer Pays
- URL: https://www.payerset.com/pricetransparencyproject/blog/same-surgery-different-price
- Published: July 13, 2026
- Author: Payerset Team
- Section: The Price Transparency Project
In metro Atlanta, the same total knee replacement, performed in the same kind of facility, carries very different prices depending on who owns the surgery center. Across the market's freestanding ambulatory surgery centers (ASCs), the average commercial institutional (facility) reimbursement for the procedure runs about $12,200 at independently owned centers, about $15,100 at private equity owned centers, and about $21,200 at system-owned centers. The operation is identical, the type of facility is identical, and the institutional rate is measured on the same procedure. **What moves the price is ownership.**
Holding the procedure and the ASC setting constant turns ownership into the variable under test. In Atlanta, the negotiated rate moves with the market power of the owner.
## The question on cost
When one facility is paid more than another for the same service, the explanation offered is cost. In any industry, understanding what it costs to run an operation is difficult without being on the inside, and healthcare is no different. The usual version of that explanation points to the setting: a hospital outpatient department carries standby capacity and institutional overhead that a freestanding office does not, so it is paid more.
That explanation cannot do the work here, because the setting is held constant. Every facility in this analysis is a freestanding ambulatory surgery center, billing the institutional facility rate for the same procedure. The buildings are comparable and the procedure is identical. **The difference that remains is ownership, and the question is what ownership is worth in the negotiated rate.**
Site of service still sets the floor and the ceiling. The same procedure moves up a predictable ladder, from an independent office with no facility fee, to a freestanding surgery center, to a hospital outpatient department, to a full inpatient stay, with the institutional component growing at each step. **This analysis holds at one rung of that ladder, the ambulatory surgery center, and varies the owner instead.** It isolates the part of the rate that the building cannot explain.
## What determines the value
A rate that tracks cost and a rate set by negotiation behave differently. When a hospital system or a private equity platform acquires an independent surgery center, the building, the staff, and the procedure can stay the same while the negotiated rate moves. What changes is the owner, and the negotiating position the owner brings to the contract.
The research on consolidation points in the same direction. Vertical acquisitions of physician practices by hospitals have been associated with price increases near 14 percent for the same services. [1] Prices at facilities with no local competition run about 12 percent above markets with several rivals, after adjusting for the care provided. [2] Outpatient prices at system-affiliated facilities have historically run above independent ones. [3] In each case, what changed was market position and billing classification.
**A larger owner also has more purchasing power with suppliers and a broader base to spread fixed costs across.** If the rate tracked cost, that scale should push it down. The Atlanta data moves in the other direction, with the largest owners commanding the highest rates, which is difficult to reconcile with a purely cost-based explanation. How much of the difference is margin could be measured against public cost-report data.
## The data: ownership in one market
Payerset analyzed commercial institutional rates at freestanding ambulatory surgery centers in metro Atlanta for three orthopedic procedures: rotator cuff repair, billed as CPT 23410 and 23412, and total knee replacement, CPT 27447. Each center was sorted into one of three ownership categories.
1. **System centers** are owned by a large hospital system.
2. **Private equity centers** are owned by a private equity backed platform.
3. **Regional centers** are independently held, owned by a single practice or group.
Across all three codes, the average rate rose in the same order: regional lowest, private equity in the middle, system-owned highest.
**The pattern is consistent across procedures, and the gap widens with the size of the bill.** On rotator cuff repair, the system average runs about 40 percent above the independent average. On total knee replacement, where the dollars are larger, the system average of about $21,200 is roughly 73 percent above the independent average of about $12,200, with the private equity average sitting between them. Each category contains named owners that a reader in this market would recognize.
## How this was measured
The figures are average commercial institutional rates, the facility side of the bill, with no professional or modifier components, drawn from Payerset claims and rate data across UnitedHealthcare, Aetna, and Elevance. System ownership is identifiable directly in the data. Private equity and regional ownership were confirmed through secondary research on portfolio holdings and practice records. Rates below the Medicare outpatient allowable were excluded as likely misreported professional fees, and rates above 500 percent of Medicare were excluded as likely hospital rates misattributed to surgery centers. Claims data was used to identify Atlanta as a strong market for the analysis and to confirm that the selected procedures are ones these surgery centers bill, so the comparison reflects services performed at each facility.
## Reading the market
Two things stand out inside the averages. **The system category is not uniform, and rates within it range widely, which reflects the different market positions of the systems involved.** The private equity category contains at least one platform whose knee replacement rates approach the system level, well above the other private equity owners in the sample. Ownership type tracks the rate, and so does the scale and reach of the specific owner.
When a small number of systems and platforms own most of the surgery centers in a market, the variation does not describe open competition. It describes a market where a few owners set the price, and where the procedure rate follows the market position of whoever owns the room it is performed in. [7] **The fewer the participants, the wider the possible range of outcomes, and the harder it is for an outside party to tell whether a given rate reflects the cost of care or the standing of its owner.**
If the rate measured the value of the surgery, it would not change with the name on the building. The procedure is the same in all three settings. What changes is the negotiating position behind it, and the size of the gap is the distance between what the surgery is worth and what it is paid.
## What policy reaches
Policy has mostly approached this through site of service. **Federal site-neutral rules, which aim to pay the same for a service regardless of where it is delivered, sit inside Medicare and address the gap between hospital and non-hospital settings.** [4] They do not reach commercial contracts, and they do not address variation among facilities of the same type. New York's Fair Pricing Act takes a different approach, capping a set of routine commercial services at 150 percent of the Medicare non-hospital rate and targeting the rate itself. [5] A Brown University analysis estimated a cap of that kind could have saved more than $1 billion in a single year across part of the state's commercial population. [6] Neither framework directly addresses the ownership-based variation visible in the Atlanta data, where every facility is already a surgery center.
## Reading the rate before the next contract
For anyone benchmarking these rates, ownership is a variable to track. Two surgery centers a few miles apart, performing the same procedure, can carry rates that differ by half or more, and the difference follows who owns them. Benchmarking that accounts for ownership, alongside site of service and code, surfaces a part of the rate that a blended average will bury.
**The rate a facility commands for a standard procedure can be read as a signal of its owner's market power.** It does not prove cost or position to the dollar, and the data has limits. The regional sample is small, ownership of independent centers is harder to confirm because system ownership shows up directly in the data and independent ownership does not, and a single owner's rates can swing the average in a thin category. What the data shows plainly is that in one concentrated market, for three standard procedures, the price of surgery rose with the market power of whoever owned the center. **For a team preparing for the next contract, that is the place to start.**
## Notes
1. Yale News. Hospital takeovers of physician practices drive up health care prices, study finds. August 2025.
2. Cooper, Craig, Gaynor, and Van Reenen. The Price Ain't Right? Hospital Prices and Health Spending on the Privately Insured. *Quarterly Journal of Economics*, 2019.
3. Health Care Cost Institute. Outpatient hospital prices are higher among system-affiliated, for-profit, and urban hospitals.
4. Congressional Research Service. Medicare's Site-Neutral Payment Policy. In Focus IF13233. 2026.
5. New York State Senate. Bill S705 / Assembly A2140 (Fair Pricing Act), 2025-2026.
6. Brown University, Center for Advancing Health Policy through Research. Estimating savings from the Fair Pricing Act and commercial site-neutral payment.
7. MedPAC. Hospital consolidation and its implications for Medicare.
---
## Updates to Hospital Price Transparency Requirements and the April 1st Enforcement Deadline
- URL: https://www.payerset.com/pricetransparencyproject/blog/updates-to-hospital-price-transparency-requirements-and-the-april-1st-enforcement-deadline
- Published: April 3, 2026
- Author: Andrew Gordon
- Section: The Price Transparency Project
The Price Transparency Requirements for Hospitals to Make Standard Charges Public is the CMS rule implementing Section 2718(e) of the Public Health Service Act, enacted as part of the Affordable Care Act and codified at [45 CFR Part 180](https://www.ecfr.gov/current/title-45/subtitle-A/subchapter-E/part-180). It has been updated with a meaningful set of changes.
If your team works with [hospital machine-readable files](/post/making-sense-of-hospital-price-transparency-data) for benchmarking, contract analysis, or price transparency research, these updates are in effect now and worth understanding before you draw conclusions from the data.
CMS finalized a new round of updates with a January 1, 2026 effective date. Enforcement was delayed to give hospitals more time to comply. That window closes April 1, 2026. Here is what changed.
For the primary source documents, see the [CMS Federal Register notice](https://www.federalregister.gov/documents/2025/11/25/2025-20907/medicare-program-hospital-outpatient-prospective-payment-and-ambulatory-surgical-center-payment) and the [CMS GitHub repository for the hospital price transparency schema](https://github.com/CMSgov/hospital-price-transparency?tab=readme-ov-file).
## From estimated amounts to actual allowed amounts
The biggest structural change in version 3.0.0 of the standardized template is replacing the "estimated allowed amount" with actual dollar-based data. Under the prior framework, when a hospital's contract with a payer was expressed as a percentage of charges rather than a flat fee schedule rate, the hospital was required to publish an estimated allowed amount. Those estimates were often inconsistent, hard to validate, and varied depending on the methodology the hospital used to generate them.
The new requirement is more specific. For any payer-specific negotiated charge that cannot be expressed as a single dollar amount because it is based on a percentage or formula, hospitals must now publish three figures: the median allowed amount, the 10th percentile allowed amount, and the 90th percentile allowed amount. They also have to include the total number of remittance observations used to calculate those figures.
All three values must come from actual EDI 835 electronic remittance advice data, covering a lookback period of at least 12 months and no more than 15 months before the file is posted.
### Why three numbers instead of one
A reasonable question when you first see this requirement is: why a distribution? If a contract says "pay 42% of charges," shouldn't the allowed amount be consistent for a given service at a given hospital?
In theory, yes. In practice, the dollar output tends to vary even under a fixed percentage contract, because the charges themselves vary by encounter. Billed amounts vary because hospitals set their own chargemaster rates for each code, those rates can change over time, and the mix of services billed across claims for the same procedure is not always consistent. Add in coordination of benefits situations, secondary payer scenarios, and outlier provisions, and you end up with a distribution of outcomes rather than a single repeatable figure.
The percentile structure is CMS acknowledging that reality and requiring hospitals to represent it directly rather than collapse it into a single estimate.
From an analytical standpoint, the three-number format gives you more to work with than a point estimate does. A tight band between the 10th and 90th percentile suggests a relatively stable, predictable contract. A wide band signals that something more complex is happening underneath, whether that is charge variation, secondary billing patterns, or contract provisions that produce meaningfully different outcomes depending on the case. That context is helpful when you are using MRF data to benchmark or validate a rate.
One definition worth keeping in mind: the "total allowed amount" in these calculations is the hospital's gross charge minus all contractual adjustments, and it includes both what the health plan paid and any patient cost-sharing. It reflects total reimbursement received, not just the payer's portion. When comparing figures across hospitals, keep in mind that the allowed amount includes both the insurer's payment and any patient cost-sharing; a split that can vary considerably from claim to claim.
## Two other requirements in the update
**Revised attestation language.** The prior template included an affirmation statement. The new version requires a formal attestation confirming that the hospital has included all applicable payer-specific negotiated charges that can be expressed as a dollar amount, and that for charges that cannot be expressed as a dollar amount, it has included all information necessary for the public to derive one. The hospital must also name the CEO or other designated senior official responsible for overseeing the accuracy of the data.
**Organizational NPI encoding.** Hospitals must now encode their Type-2 (organizational) NPI associated with an active hospital taxonomy code directly in the MRF. This is a standardization measure intended to improve comparability across files and connect hospital MRF data more reliably to other provider datasets.
The NPI requirement addresses one of the more persistent friction points in price transparency work. Identifying which entity in a payer's MRF corresponds to a specific hospital facility has been genuinely difficult, particularly for large health systems that appear across dozens of NPIs. Having the organizational NPI encoded in the hospital's own file creates a cleaner bridge between data sources.
## For hospitals preparing for April 1
From the provider side, compliance with the version 3.0.0 template means more than reformatting a file. Hospitals need to pull 12 to 15 months of EDI 835 remittance data, calculate median, 10th percentile, and 90th percentile allowed amounts for each payer-specific negotiated charge that is expressed as a percentage or formula, and encode those figures accurately alongside remittance observation counts. For hospitals with large payer mixes and many percentage-based contracts, that is a meaningful operational lift.
The attestation requirement adds another layer. By signing off with a named senior official, the hospital is taking on formal accountability for the accuracy of what is published. That changes the internal calculus around data quality review before the file goes out.
## Using the new data in practice
The estimated allowed amount was a real limitation in the prior framework. Its value depended entirely on the methodology the hospital used to produce it, which was applied inconsistently and was difficult to audit. The shift to percentile-based actual remittance data closes that gap considerably for percentage-based contracts, and it makes [benchmarking work more reliable](/pricetransparencyproject/blog/a-practical-look-at-reimbursement-benchmarking-with-price-transparency-data) when you are comparing reimbursement across hospitals.
That said, there are some real limits to what the new data can tell you. The percentile values are backward-looking. They reflect what was actually paid over the prior 12 to 15 months, not what a future claim will pay. For contracts that have been renegotiated or for hospitals that have updated their chargemasters recently, the historical distribution may not reflect current economics.
The observation count field matters a lot here. When a hospital publishes P10/median/P90 figures based on a small number of remittances, those figures carry more uncertainty than figures backed by hundreds or thousands of observations. We look at volume alongside the rate itself when drawing conclusions from this data, and that practice becomes even more relevant under the new framework.
One scoping note worth keeping in mind: these requirements apply to hospitals as defined under 45 CFR Part 180, not to all provider types. Ambulatory surgery centers, physician groups, and diagnostic labs that operate under separate licenses and billing structures are not subject to the same MRF template requirements, regardless of ownership. This is a meaningful distinction for managed care teams doing multi-setting analysis. The percentile data and attestation requirements described here are specific to the hospital MRF. For teams doing multi-setting analysis that includes ASCs, physician groups, or other non-hospital providers, payer MRFs are the more comprehensive source. They cover all in-network providers regardless of facility type and often carry more rate detail.
For fee-schedule-based contracts, the changes are less dramatic. A flat dollar rate is still a flat dollar rate, and the single-value reporting structure remains. The percentile requirement applies specifically to charges that cannot be expressed as a direct dollar amount.
## Enforcement begins April 1
The original hospital price transparency rule took effect in January 2021, requiring hospitals to publish machine-readable files for the first time. Since then, CMS has progressively tightened the requirements through multiple rulemaking cycles, adding the standardized template format in 2024, strengthening the affirmation requirement, and now replacing estimated amounts with actual remittance-based data. Each update has pushed the data closer to reflecting what hospitals are actually paid rather than what they estimate they might be paid. The version 3.0.0 changes are the most significant methodological update to the hospital MRF since the rule's inception, and they set a higher floor for what rigorous price transparency analysis can do.
CMS delayed enforcement of the January 1 effective date to give hospitals additional time to update their files. That window closes April 1, 2026. Hospitals that have not yet moved to the version 3.0.0 template and encoded the required percentile data, attestation language, and organizational NPIs will be out of compliance. Non-compliance carries real financial consequences. CMS can issue civil monetary penalties of up to $300 per day for smaller hospitals and up to $5,500 per day for larger ones, and the agency has been actively issuing penalties since late 2023. Enforcement activity has continued to increase since then.
For anyone consuming hospital MRF data for benchmarking or analysis, April 1 is also a useful moment to audit your sources. Files published before that date may still reflect the old format. After enforcement begins, you should start seeing the new structure more consistently, though file quality and completeness will continue to vary across hospitals as it always has. We have written about [what payer compliance looks like when a major schema update takes effect](/pricetransparencyproject/blog/transparency-in-coverage-tic-schema-2-0-is-live-updates), and the same dynamic applies here on the hospital side.
We are watching how the new fields roll out across the files we work with and will share what we find.
---
## Part 2: A Practical Look at Reimbursement Benchmarking with Price Transparency Data
- URL: https://www.payerset.com/pricetransparencyproject/blog/a-practical-look-at-reimbursement-benchmarking-with-price-transparency-data
- Published: March 16, 2026
- Author: Jacob Little
- Section: The Price Transparency Project
This is Part Two of a series on using price transparency data for benchmarking. You can take a look at [Part 1 here](/pricetransparencyproject/blog/how-to-analyze-hospital-price-transparency-data-for-reimbursement-benchmarking).
We recently helped a customer work through one of the most common questions we hear in managed care: am I getting paid more or less for emergency visits than the other hospital in my market?
It sounds like a simple question. It turns out to be anything but.
The conversation started because an analysis had been circulating comparing commercial emergency visit reimbursement between two competing health systems in the same city. The conclusion was clean and quotable: Hospital A receives 1.5x more than Hospital B for emergency visits from the same commercial payer. Attention-grabbing for sure. But when we worked through the methodology behind it, the numbers told a very different story.
We want to be upfront about something. We are not sharing this to point fingers at anyone. Price transparency data is genuinely new territory, and figuring out how to use it well is a challenge the whole industry is working through together. What we can offer is what we have learned from doing this work with customers over time.
## The Problem with Single-Code Comparisons
We are going to use Emergency visits as an example, but the lessons in this post apply to a variety of different services.
The original analysis pulled one billing code, 99284 (a Level 4 emergency visit), directly from hospital machine-readable files and compared the estimated reimbursement between two hospitals. That seems like a reasonable starting point. In practice, it produced a conclusion that was the opposite of what the full picture showed, for two reasons:
First, it only captured a piece of total charges.
An emergency visit generates two separate components: a facility fee and a professional fee. The facility fee covers the hospital's resources, the room, the nursing staff, the equipment. The professional fee covers the physician or clinician who evaluates and treats the patient. These can appear on separate claims, be negotiated separately, involve physician groups, and vary independently from one organization to another.
The original analysis looked only at the facility reimbursement for 99284 and left the professional component out entirely.
Second, a 99284 is rarely the only code billed for an emergency visit. It's often one line item.
When a patient comes in with chest pain, the 99284 is just the starting point. What follows is a cascade of additional services: blood draws, an EKG, imaging, IV access, medications, and more. The professional charges for a physician-patient evaluation can originate from an independent physician group partnered with the hospital. Each of those generates its own billing code, and each of those codes can be negotiated separately. The reimbursement differences across those codes, especially in labs, can end up being far larger than anything you would see on the E/M code alone.
Comparing two hospitals on 99284 in isolation is like comparing two restaurants on the price of one ingredient and calling it a full meal comparison. You will get a number, but it rarely tells you the entire story.
## What a Real Comparison Looks Like: Start with the Bundle
When we work through questions like this with customers, the first thing we do together is define what we are actually pricing before touching any data. For an emergency visit, that means building a clinically grounded bundle of billing codes that reflects how a real visit is actually billed.
For a standard Level 4 ED visit with a chest pain and cardiac workup presentation, here's an example of a core bundle we would work with:
Worth noting: several of those codes split into both a professional and a facility component, specifically the E/M visit itself. Some hospitals bill EKGs globally using 93000, while others split them into the technical (93005) and interpretation (93010) components. Understanding how each organization actually bills is part of the work, and it matters for the comparison.
Once you have your bundle, you need to price it across three separate data sources. Not one, not two. All three.
## The Three Data Sources You Need to Triangulate
**Payer Machine-Readable Files (Transparency in Coverage)**
Commercial carriers are required by law to publish negotiated rates in these files, broken out by plan. This is the closest thing we have to ground truth on what a payer has contractually agreed to pay. Payers are required to post accurate, complete data in these files, and consistent enforcement of that requirement is essential to making price transparency work the way it should.
Several challenges continue to exist as we look to make meaningful use of the information. A couple examples include massive file sizes (several Terabytes and millions of rows per file), expiring access links, API rate throttling, data complexity, and instances of data duplication.
**Hospital Machine-Readable Files (Hospital Price Transparency)**
Hospitals publish their own pricing files and are required to affirm the accuracy and completeness of their MRFs as of July 1, 2024. These include standard charges, payer-specific negotiated rates, and estimated amounts. We regularly use this data as a cross-check and a supplemental reference as opposed to a primary source. Estimated amounts in hospital MRFs can be tough to decipher, particularly when complex contract structures are involved.
For example, a complex rate analysis occurs if a hospital operates under a percent-of-billed-charges contract. In these cases, the file shows a percentage rather than a dollar amount. You see "68% of billed charges" and still cannot calculate what that means in dollars without knowing the hospital's actual charge. While these types of arrangements are becoming less common, it's during times like this that the other sources come in handy.
**Claims Data**
This is a great sounding board that strengthens hospital benchmarking. Remittance data (835 files) shows the billed amount, allowed amount, paid amount, adjustments / denials, and the patient responsibility across real transactions. When we have several observed remittances for a given set of billing codes, we have a high-confidence picture of what those codes are actually reimbursing in practice.
For percent-of-charge contracts: the payer MRF gives you the rate as a percentage, claims data tells you what the hospital typically charges, and multiplying those together gives you a reliable estimated reimbursement. You then validate that against the actual remittances you have observed. That is where the real picture comes into focus.
## What We Found With a Comprehensive Approach
Here is how this plays out with real data. We worked through a comparison of two competing hospitals in the same market: Hospital A and Hospital B, both serving a similar commercial payer mix. We focused on commercial PPO reimbursement using the bundle above.
The original analysis had claimed Hospital A receives more than Hospital B. Here is what we found when we applied the full methodology.
**Head-to-Head Bundle Comparison -- Commercial PPO**
The conclusion flips entirely. Hospital A does receive more on the headline facility fee for the 99284 code, which is exactly what the original analysis highlighted. But once you account for the full visit, Hospital B comes out ahead by roughly $250 per encounter, about 12% higher overall.
Here is the part that really matters. The 99284 facility fee, the single number the original analysis was built on, shows Hospital A higher by a good margin. But then look at what happens in the labs and the rest of the bundle. Hospital B collects significantly more across the labs and the troponin, and edges ahead on the professional fees as well. That is where the negotiating leverage actually lives, and it is easy to miss entirely if you are only looking at one code from one source.
## A Note About Percent-of-Charge Rates
This kind of analysis also surfaces a challenge worth spending more time on. It's difficult to analyze hospital reimbursement using price transparency data alone when a hospital operates under a percent-of-charge contract.
Here is how these contracts work. Rather than agreeing to pay a fixed dollar amount for a lab test, the payer agrees to pay a percentage of whatever the hospital bills. If a hospital charges $200 for a metabolic panel under a 65% of billed charges contract, the payer pays $130. If that hospital later updates its chargemaster and raises the charge to $250, the payer pays $163, and the hospital never had to reopen the contract to get there.
Understanding which structure applies to which codes is a prerequisite for meaningful analysis. If you are looking at a payer MRF and seeing a percentage instead of a dollar figure, you are dealing with a percent-of-charge contract, and you will need additional data to make it actionable.
## Final Thoughts
Claims data is becoming increasingly valuable for hospital benchmarking. Remittance data unlocks added visibility by going under the hood to help showcase billing patterns, validate reported rates, distill code cohorts, and help close the loop on percent-of-charge contracts.
Federal enforcement energy should remain consistent, focused, and constructive. With the introduction of [Schema 2.0](/pricetransparencyproject/blog/transparency-in-coverage-tic-schema-2-0-is-live-updates), this is a fantastic step forward and even more improvements are [coming in 2027 and beyond](/pricetransparencyproject/blog/cms-proposes-major-updates-to-transparency-in-coverage-rules).
Even a thorough analysis has limits. The methodology we walked through is more accurate than pulling estimated amounts from a hospital MRF for one billing code, but it still involves estimates. Every emergency visit is different. Every contract is different. We are modeling a typical clinical presentation, not a specific patient encounter. The goal is to get directionally right with reasonable confidence, not to manufacture false precision.
This work takes real expertise. We have been doing it alongside managed care professionals for years and still approach every new analysis with humility. If someone tells you price transparency benchmarking is simple or that a tool handles it automatically, that is worth questioning. It takes experienced people to apply this data well. Even with its challenges, the good news is that when you approach this the right way, with the right bundle, the right data sources, and a real understanding of the contract structures involved, you get answers that are genuinely useful. You find out where reimbursement is strong, where there is room to grow, and where to focus energy going into the next negotiation.
---
## Part 1: How to Analyze Price Transparency Data for Reimbursement Benchmarking
- URL: https://www.payerset.com/pricetransparencyproject/blog/how-to-analyze-hospital-price-transparency-data-for-reimbursement-benchmarking
- Published: March 10, 2026
- Author: Matt Phillips
- Section: The Price Transparency Project
Price transparency data is one of the most powerful tools to emerge in healthcare in recent years. It also, like so many aspects of healthcare, is full of nuance.
We have seen plenty of examples where analysts pull numbers from machine-readable files, find a striking difference between two organizations, and publish a conclusion that falls apart the moment someone with real contracting experience looks at the methodology. The challenge, in most of those cases, is not the data as much as it's the approach.
Through hundreds of customer interactions, we've been able to see how experts are using this data everyday. In this post, we take some of these learnings and attempt to lay out a framework from all these interactions that you can use when benchmarking reimbursement between hospitals or healthcare organizations.
## Start with Strategy, Not Data
Before you open a single file or run a single query, start with answering a fundamental question: what are you actually trying to accomplish?
Are you a managed care leader preparing for contract renegotiation with a specific payer? Are you a consultant helping a health system understand where they are underpaid relative to peers? Are you an employer trying to steer members to higher-value providers?
The answer shapes everything: which codes to analyze, which data sources to prioritize, how to normalize comparisons, and what conclusions you can reasonably draw.
Too often, analysts dive into the data without a clear strategy. They pull a huge portion of the data, see interesting-looking numbers, find surprising variations, and draw conclusions that may not be relevant at all.
## Understand That Rates Are as Unique as Contracts
This may be the most foundational truth around price transparency: every negotiated rate reflects a unique relationship between a specific payer, a specific plan, and a specific provider. Decades of legal back-and-forth, mergers and acquisitions, strategy shifts, policy changes and necessary responses all add up to data reflected in MRFs that attempt to codify & represent all of that complexity.
Two hospitals in the same city, with the same payer mix, serving similar patient populations, can have wildly different reimbursement structures. One might have percent-of-charge contracts. The other might have fee schedules. One might have carved out high-cost drugs and implants. The other might have bundled case rates for certain service lines.
This means you cannot simply pull two numbers from two machine-readable files and declare a winner. You have to understand the underlying contract structures and details around services to make any meaningful comparison.
## Use the Right Data Sources
Accurate reimbursement analysis requires triangulating multiple data sources. No single source tells the complete story, and each one plays a distinct role in building a reliable picture.
**Payer Machine-Readable Files (Transparency in Coverage)**
These files, published by commercial carriers as required by federal regulation, contain negotiated rates for in-network providers by plan type. They are the closest thing we have to ground truth for what a payer has contractually agreed to pay, and it is worth saying plainly: payers are required by law to publish accurate, complete data in these files. Enforcement of that requirement matters. When the files are done right, they are an incredibly powerful starting point.
Working with these files in practice comes with real challenges. File sizes can run into multiple terabytes with hundreds of millions of rows. Access links expire. APIs rate-throttle. Data can be duplicated across plan types in ways that require careful handling before any comparison is meaningful. That is part of why triangulating with the other two sources is essential, not optional.
**Hospital Machine-Readable Files (Hospital Price Transparency)**
Hospitals publish their own pricing files and are required to affirm the accuracy and completeness of their MRFs as of July 1, 2024. These files include standard charges, payer-specific negotiated rates, and estimated reimbursement amounts.
We treat hospital MRF data as a useful supplemental reference, particularly for cross-checking, but not as a primary benchmarking source. The estimated amounts can be unreliable, especially for complex contract structures. It is a piece of the puzzle, not the foundation of the analysis.
**Claims Data**
Contracted rates are one thing, but real-world claims and utilization unlocks more precise benchmarking and cohort analysis, helps you understand potential revenue impacts of higher reimbursements, and can be a useful tool in interrogating where contracts live within complex organizational structures. Claims data shows what actually happened: what was billed, what was paid, and how consistently. Remittance data (specifically the 835 files) reveals actual reimbursement amounts across countless observed transactions.
This also becomes essential when you are working with percent-of-charge contracts. The payer MRF gives you the rate as a percentage, claims data tells you what the hospital typically charges, and together they let you calculate an estimated reimbursement, which you can then validate against observed remittances (if available). That triangulation can be powerful. The organizations doing this work are using all three sources together, cross-referencing and validating rather than relying on any one of them alone.
## Build Bundles, Not Single-Code Comparisons
A hospital visit is rarely a single billing code. An emergency visit regularly includes the evaluation and management code, labs, imaging, cardiac diagnostics, IV services, and potentially dozens of other line items depending on how the patient presents. A knee surgery includes the procedure code, anesthesia, implants, facility fees, and post-operative care. An infusion visit includes the drug, administration codes, and nursing time.
Comparing a single code between two organizations tells you very little about total reimbursement for that episode of care. Remember to always build clinically appropriate bundles that reflect how services are actually delivered and billed. Define the full picture before you touch any data.
## Account for Professional and Facility Components
Many services generate both a professional component (the physician's work) and a facility component (the hospital's resources). These might appear on separate claims forms, can be negotiated separately, and can vary independently from one organization to another as well as from service to service.
Any analysis that only captures one component is going to be off. And because different organizations structure their billing differently, understanding how each organization actually bills is a big part of the effort.
## Normalize for Different Reimbursement Structures
Different contract structures, at the billing code level, require different analytical approaches, and making a small error can skew your entire comparison.
Fee schedules and specific negotiated rates are the simplest to work with. The payer MRF shows a dollar amount for each code, and you can compare directly across organizations. This shows up as "Fee Schedule" and "Negotiated" in the MRF data under the Negotiated Type field in the TiC Schema.
Percent of billed charges are more complex. The MRF shows a percentage, but you need to know what the hospital actually charges to calculate expected reimbursement. This may require claims data and hospital MRFs to estimate typical charge amounts for that organization and code combination. Even then, it will be an estimate at best to truly understand the dollar impact.
Case rates and bundles require understanding the scope of what is included in each payment. An ambulatory surgery center might receive one payment covering the facility fee, surgeon fee, anesthesia, and implants, while a hospital bills those components separately. Comparing them requires knowing what is inside each rate.
Carve-outs for high-cost items like implants, specialty drugs, or transplant services are often reimbursed separately from standard rates. Missing these can dramatically skew an analysis, particularly for service lines where carved-out items represent a significant share of total revenue. These can be classified as CSTM codes in the data, Revenue Codes (RC), or under classifications that aren't used as commonly such as an AP-DRG.
In practice, most sophisticated contracts are hybrid structures that combine several of these approaches. Knowing which structure applies to which codes is a prerequisite for any meaningful comparison.
## Validate with Claims Data
Transparency data tells you what rates were negotiated. Claims data tells you what actually gets paid. These do not always match.
Contractual adjustments, claim denials, bundling edits, and other factors can create real gaps between negotiated rates and actual reimbursement. When you have remittance data with sufficient volume (we look for at least 1,000 observations per code where possible), you can validate your transparency-based estimates against real-world outcomes.
If your transparency-derived estimate says a hospital should receive $500 for a code, but claims data consistently shows $350, something in the analysis needs revisiting before drawing any conclusions.
## Service Type Matters
The methodology above applies broadly, but the specific approach varies significantly depending on what you are analyzing.
Emergency and acute care is highly variable by patient presentation and requires flexible bundles built around common clinical scenarios. Professional and facility components need to be analyzed and compared within their respective buckets.
Inpatient reimbursement routinely involves Medicare Severity Diagnosis Related Groups, which bundle payment for an entire stay. Analyzing these requires understanding DRG weights, base rates, outlier provisions, and any carved-out services. Case mix and severity add another layer of complexity.
Outpatient procedures are often more straightforward, particularly for ambulatory surgery centers with bundled case rates, though variation in what is included versus billed separately still requires attention.
Infusion services are dominated by drug costs, which are often reimbursed on ASP or WAC-based structures with different margins than administration codes. Biosimilar substitution and 340B program participation can significantly affect comparisons.
Physician services outside of a facility setting are generally simpler, since you are usually dealing with fee schedules without facility components. Modifier usage and place-of-service variations are still worth watching.
## Caveats Worth Acknowledging
Even with the right methodology, price transparency analysis has real limits, and we think it is important to name them honestly.
Data quality varies across payers and hospitals. Some publish clean, comprehensive files. Others have gaps, formatting inconsistencies, or errors that require extra interpretation.
MRF data reflects a point in time. Contracts get renegotiated, rates change, and an analysis built on last quarter's data may not reflect what is true today.
Not everything shows up in MRFs. Value-based incentives, quality bonuses, and other arrangements can affect total reimbursement without appearing in transparency files.
Billing practices differ. Two hospitals might deliver identical clinical services and bill them differently based on their RCM systems, coding practices, and internal processes. That affects comparability even when you use the same code bundles.
And honestly, this work is just complex. We have been doing it alongside experienced managed care professionals for years and still approach every new analysis with humility. If someone tells you this is easy or that a simple tool can handle it automatically, we would encourage some skepticism.
## A Framework for Getting It Right
To pull it all together, here is how we approach reimbursement benchmarking using price transparency data:
**Start with strategy.** Know what question you are trying to answer and why it matters before you touch any data.
**Use multiple data sources.** Triangulate payer MRFs, hospital MRFs, and claims data. No single source is sufficient on its own.
**Build clinically appropriate bundles.** Never draw conclusions from single-code comparisons. Model how care is actually delivered and billed.
**Capture all components.** Include both professional and facility fees wherever applicable.
**Understand contract structures.** Know whether you are dealing with fee schedules, percent-of-charge, case rates, or hybrid arrangements, and adjust your methodology accordingly.
**Validate with claims data.** Use remittance data to confirm that your transparency-derived estimates reflect real-world reimbursement.
**Acknowledge limitations.** Be honest about what your analysis can and cannot conclude. Avoid overreach.
## The Bottom Line
Price transparency data has genuine potential to shift how decisions get made in healthcare, for providers, payers, employers, and patients. But realizing that potential requires rigorous methodology and the kind of humility that comes from working closely with the data over time.
We are still early in this journey as an industry. The data will get better. The tools will improve. Enforcement will continue to develop. And we will all be better served by a community that approaches this work carefully and shares what it learns along the way.
---
## Transparency in Coverage (TiC) Schema 2.0 Is Live - Updates
- URL: https://www.payerset.com/pricetransparencyproject/blog/transparency-in-coverage-tic-schema-2-0-is-live-updates
- Published: February 5, 2026
- Author: Matt Phillips
- Section: The Price Transparency Project
February 2nd, 2026 marked the first enforcement deadline for Transparency in Coverage Schema 2.0, a significant change to how payer data is published and accessed. We've been parsing the latest MRFs and wanted to share what we're seeing on the ground.
We plan to update this post each quarter as payers continue adapting to the new requirements.
Schema 2.0 is the first step. [We recently did a deep dive in to proposed rules for 2027 and beyond.](/pricetransparencyproject/blog/cms-proposes-major-updates-to-transparency-in-coverage-rules)
## Refresher: What's New in Schema 2.0
There are a lot of updates, but here are a few of the highlights:
**Network Name**
The common carrier network name most familiar to members and the public. No more filtering through dozens of internal plan names. BlueCard networks are now called out explicitly, which helps separate local contracted rates from BlueCard passthrough rates for benchmarking and negotiation.
**Business Name**
The common business name associated with the EIN (Tax ID). Previously, business names were only tied to NPIs via NPPES. Now we can group rates by business name and Tax ID, making it easier to determine if a rate applies to, as an example, Hospital A, Hospital B, or a physician's private practice.
**Plan Sponsor Name**
For employer plans, shows the plan sponsor's business name. Helpful for distinguishing employer-sponsored vs. commercial plans.
**Issuer Name**
More specific than before. For example, "UnitedHealthcare Florida" rather than just UnitedHealthcare.
**Setting**
Indicates whether the rate applies to inpatient, outpatient, or both (in addition to the existing billing class for facility fee and pro fee).
## What We're Actually Seeing with Compliance
We'll update this throughout the year and, as a reminder, [you can also view our public payer compliance scorecard.](https://docs.payerset.com/payers/)
### February 2026 Update
It's rare for the Transparency in Coverage schema to get a dramatic update like this. Payers are retrofitting their processes and systems to comply. It's inevitable that mistakes will happen but we're also seeing some payers aren't posting the new format at all.
**Payers not posting the new schema**
As of February 2nd (the official enforcement day for TiC 2.0), many State Blues and Cigna have not updated to the new schema. This creates a fragmented landscape where some payers are posting 2.0-compliant files while others remain on the original schema, requiring anyone working with this data to reconcile both formats.
For context, here's a screenshot showing the only Blues that are posting under the new 2.0 federal TiC rule:
**Erroneous machine-readable files (MRFs)**
UnitedHealthcare has published files with errors and typos. At Payerset, we don't blindly parse data according to the rules. We review how each payer is actually posting, then adjust our processes to handle the nuance and interpretation from payer to payer. This kind of manual remediation is necessary to ensure accuracy, but it takes additional time with such a monumental change.
**Material changes to data access methods**
Some payers have significantly changed how files are accessed. United, for example, has restructured their file access methods, requiring anyone ingesting this data to rebuild parts of their pipelines.
**Last-minute CMS updates**
At 9:52 AM on February 2nd, CMS was still updating their official schema documentation. When payers see last-minute updates, it can delay their internal processes too.
The bottom line: many payers are not currently compliant with federal law. This is a transitional period, and the industry is figuring it out in real time.
## What This Means for Anyone Working with MRF Data
If you're working with MRF data, expect some turbulence this quarter. The mix of 1.0 and 2.0 files, varying compliance levels, and payer-specific quirks means more data validation work.
Keep in mind this schema is new and everyone is figuring out compliance. This is a transitional period, but it's a transition toward better, more detailed information.
If you have questions about what you're seeing in the data or how to work with the new fields, reach out to us directly at [info@payerset.com](mailto:info@payerset.com). We're happy to help. [You can read our deeper guide to understand everything that's in Schema 2.0.](/post/transparency-in-coverage-schema-2-0)
---
## CMS Proposes Major Updates to Transparency in Coverage (TiC) Rules
- URL: https://www.payerset.com/pricetransparencyproject/blog/cms-proposes-major-updates-to-transparency-in-coverage-rules
- Published: February 3, 2026
- Author: Matt Phillips
- Section: The Price Transparency Project
## TL;DR
- CMS is proposing some of the most significant updates to TiC requirements since its inception.
- Plans would consolidate rate files by provider network instead of by individual plan, dramatically increasing usability.
- Annual Utilization File would show which providers actually received reimbursement for a given service
- In-network and out-of-network files move from monthly to quarterly updates
- Enhanced out-of-network data: Claims threshold drops from 20 to 11, reporting period extends from 90 days to 6 months.
- Drug Transparency pushed back: Initially was supposed to begin early 2026 but timeline is now TBD
## What You Need to Know
The proposed rule represents CMS's response to years of feedback about the practical challenges of working with transparency data. While the 2020 final rules succeeded in releasing an enormous amount of previously hidden pricing data, implementation still had significant gaps: massive file sizes, duplicative data, lack of context about which rates actually matter, and misalignment with hospital price transparency formats.
[We've written about these challenges plenty](/post/the-positive-future-of-price-transparency-in-the-u-s-in-2025) and these proposals aim to fix some of those friction points systematically. For CFOs and managed care leaders, this means a more reliable foundation for benchmarking rates, validating contracted amounts, and preparing for negotiations. The data won't just be more accessible—it will be more trustworthy and easier to act on.
### Timeline
The proposed rule was published December 23, 2025. The comment period closes February 23, 2026. If finalized, most provisions would apply for plan years beginning on or after January 1, 2027. You can view the official announcement [here](https://www.federalregister.gov/documents/2025/12/23/2025-23693/transparency-in-coverage).
We've summarized the highlights below.
## Network-Based Rate File Structure: Identify the Correct Network and Rates Faster
### The Problem
Under current rules, health plans and issuers must publish a separate in-network rate file for each plan or coverage option they offer. This creates massive redundancy when multiple plans share the same provider network and negotiated rates.
Consider a realistic scenario: A regional Blue Cross Blue Shield plan might offer different commercial plan variations (different metal tiers, employer groups, and benefit designs) that all use the same underlying provider network with identical negotiated rates. Under current rules, that plan must publish 50 separate rate files—each containing the exact same provider list and rate information, just with different plan identifiers in the header.
Now scale that across the BlueCross BlueCard network, where participating Blue plans across all 50 states can share network access. In addition to the complexity of managing this data, simply finding the "correct" plan in the data becomes unnecessarily complex.
### The Proposed Solution
CMS proposes requiring plans and issuers to publish one in-network rate file per provider network rather than per plan. Each file would include:
- Common provider network name for clear identification
- Enrollment totals for each plan using that network (as of the file posting date)
- All negotiated rates for that network's providers
Plans offering multiple products that use the same network would reference a single network file instead of duplicating the data dozens or hundreds of times.
This change could reduce file sizes, eliminate redundant data, and make it far easier to identify the relevant rate for a specific provider-payer combination.
### Better Alignment with Hospital Reporting
This structure also brings payer reporting into closer alignment with how hospitals report under their own price transparency rules. [Hospitals already organize their data by payer and network](/post/making-sense-of-hospital-price-transparency-data), so having insurers report the same way creates better apples-to-apples comparisons across both datasets.
## Annual Utilization File: Focus Analysis on Providers Who Perform the Service
### The Problem
Current in-network rate files contain negotiated rates for every provider with whom a plan has a contract, regardless of whether that provider ever actually submits a claim. This creates what the community have started calling "zombie rates" or "ghost rates" — contractual arrangements that exist on paper but have zero real-world utilization.
It's unclear if this is intentional obfuscation, data & system limitations on the backend, different interpretations of the rules, or a combination of all of these. But when you're benchmarking rates for a specific procedure or trying to understand the competitive landscape for a service line, these zombie rates add enormous noise to the analysis. You might see a contracted rate for a pediatric cardiologist performing total knee replacements, or negotiated amounts for providers who haven't billed the plan in years.
### The Proposed Solution
CMS proposes a new Utilization File that plans and issuers would post annually. This file would identify all providers (by NPI, TIN, and Place of Service code) who received reimbursement for at least one claim during a 12-month period that ends 6 months before the file posting date.
The Utilization File would list:
- Each covered item or service (by billing code) for which claims were reimbursed
- Each in-network provider who was reimbursed for that item or service
- The connection between what services were actually billed and which providers delivered them
This creates a useful filter for rate benchmarking. For example, when benchmarking orthopedic surgery rates in a specific market, you can now focus exclusively on providers who actually performed those procedures for that plan's members.
### Real-World Application
We pushed for full utilization & claims data from the payers but this is an important first step. There are no magic bullets here, though - we envision this being a first-pass filter and then further using real-world claims & remits data to understand marketshare, utilization, and validating rate benchmarking.
## Change-Log File: Track Rate Changes from Quarter to Quarter
### The Proposal
Plans and issuers would be required to publish a Change-log File with each quarterly in-network rate file that identifies any changes made to the required information since the last posted file. This includes additions, deletions, or modifications to provider networks, negotiated rates, or plan enrollment data.
The Change-log provides transparency into rate evolution over time. When you see a rate increase or decrease, you can track when it changed and potentially correlate it with contract renewal cycles, policy changes, or market shifts. This is particularly valuable for:
- **Rate trend analysis:** Understanding whether your contracted rates are keeping pace with market movements
- **Contract compliance monitoring:** Verifying that mid-year rate adjustments align with contract terms
- **Competitive intelligence:** Spotting when competitors renegotiate rates or enter/exit networks
We will be monitoring the implementation of this and will continue to provide feedback to CMS on what we see in the data.
## Taxonomy File: See Which Provider-Service Combinations the Plan Considers Valid
### The Problem
Plans and issuers use internal taxonomies during claims adjudication to determine if a provider is appropriately credentialed to perform a specific service. A plan might automatically deny reimbursement for a procedure if the provider's specialty doesn't match the service being billed, regardless of whether a negotiated rate exists in the rate file.
However, these internal taxonomies haven't been published.
### The Proposed Solution
Plans and issuers would be required to publish a Taxonomy File showing their internal provider taxonomy that matches items and services (by billing code) with provider specialties (using Healthcare Provider Taxonomy codes from NUCC).
This reveals which provider specialties the plan considers appropriate for which services. The Taxonomy File helps you understand the logic behind the rate file structure. When a rate doesn't appear in the data, you'll have a better idea if it's because:
- No contract exists for that provider-service combination
- The provider's specialty is deemed inappropriate for that service by the plan's rules
- The rate was excluded for other reasons
## Excluded Provider Information: Remove Clinically Inappropriate Rate Combinations from Files
### The Proposal
CMS proposes requiring plans and issuers to exclude provider-rate combinations for services that a provider is unlikely to perform based on the provider's specialty. The determination would be based on the plan's internal taxonomy used during claims adjudication.
For example, a plan would exclude the negotiated rate for an ophthalmologist performing hip replacements, even if a theoretical rate exists in their contract master file.
This directly addresses file bloat and improves data quality. Excluding implausible provider-service combinations:
- **Reduces file size significantly:** Fewer irrelevant rate entries to store, transfer, and process
- **Improves analysis accuracy:** Benchmarking focuses on clinically appropriate comparisons
- **Eliminates confusion:** No more questioning why certain absurd rate combinations exist in the data
This is complimentary to the Utilization file and a step in the right direction in reducing zombie rates and increasing usability of the price transparency data.
## Enhanced Out-of-Network Data: Access More Comprehensive OON Allowed Amounts
### Three Key Changes
CMS proposes several modifications to make out-of-network allowed amount data more comprehensive and useful:
**1. Lower Claims Threshold:** The minimum number of claims required for reporting drops from 20 to 11 different claims per item or service. This means more out-of-network utilization will be captured in the data, particularly for less common procedures or in smaller markets.
**2. Longer Reporting Period:** The reporting period increases from 90 days to 6 months, and the lookback period extends from 180 days to 9 months. More data over a longer timeframe improves the reliability of out-of-network cost estimates.
**3. Market-Level Aggregation:** Plans and issuers would organize allowed amount data by health insurance market type (individual, small group, large group, or self-funded) rather than by individual plan. This provides clearer market-wide context while maintaining appropriate aggregation for privacy protection.
These changes make out-of-network data more comprehensive and actionable for:
- **Balance billing risk assessment:** Better estimates of potential out-of-network costs for patients
- **Network adequacy analysis:** Identifying gaps in network coverage by specialty and service
- **Reference pricing strategies:** More robust data for establishing reference-based pricing programs
- **No Surprises Act compliance:** Improved data for calculating qualifying payment amounts
### The Remaining Gap
While these improvements are meaningful, the claims threshold issue persists. Even at 11 claims, many payers can still avoid posting out-of-network data for less common services. CMS continues to struggle with balancing privacy protection against the transparency goal of comprehensive out-of-network cost disclosure.
## Quarterly Reporting: Receive Updates Every Three Months Instead of Monthly
### The Change
In-network rate files and out-of-network allowed amount files would move from monthly to quarterly reporting cadence. Prescription drug files would remain monthly.
### Why This Makes Sense
Negotiated rates typically don't change monthly. Contract renewals and rate adjustments usually happen annually or semi-annually, meaning monthly updates often show no meaningful changes while creating substantial administrative burden for plans and bandwidth costs for file distribution.
Quarterly reporting provides a reasonable middle ground: frequent enough to capture meaningful changes, but not so frequent that it generates busywork. The Change-log File (discussed above) ensures that users can track exactly what changed between quarters.
- **For plans and issuers:** Reduced compliance burden, lower data storage and bandwidth costs, fewer opportunities for filing errors
- **For data users:** More stable data for quarterly analysis cycles, less frequent data ingestion overhead, clearer signals when rates actually change
## Text File and Footer Link: Locate Machine-Readable Files Faster
### The Problem
Finding a plan's machine-readable files currently requires navigating through multiple web pages, searching documentation, or using third-party tools that scrape and index file locations. There's no standardized way to locate where a plan posts its transparency files.
### The Proposed Solution
Plans and issuers would be required to:
- **Post a Text File** in the root folder of their website containing:
- URLs for all required machine-readable files
- Contact information for inquiries about the files
- A point of contact for technical questions
- **Include a footer link** on their website homepage titled "Price Transparency" or "Transparency in Coverage" that routes directly to the page hosting the machine-readable files
These simple requirements dramatically improve file discoverability and accessibility. Data teams, researchers, and third-party tool developers can programmatically locate and download files without manual searching. This enables:
- **Automated data pipelines:** Scripts can reliably find and retrieve updated files
- **Consistent file locations:** Standardized paths reduce broken links and missing data
- **Better compliance monitoring:** Regulators and watchdogs can easily verify file availability
This aligns with hospital price transparency requirements, which already use similar standardization for file locations.
## Product Type Identification: Compare Rates Across HMO, PPO, and Other Plan Types
### The Proposal
Plans and issuers would be required to include product type (HMO, PPO, EPO, POS, etc.) in both in-network rate files and out-of-network allowed amount files.
Product type is a fundamental characteristic that affects network breadth, provider access rules, and often negotiated rate levels. Including this field enables more accurate comparisons:
- **PPO vs. HMO rate analysis:** Understanding rate differences across product structures
- **Network strategy development:** Identifying which product types offer the most competitive rates
- **Member communication:** Helping members understand why rates differ across their plan options
This builds on the improvements in Schema 2.0, where plan identification fields were enhanced to enable better apples-to-apples comparisons across carriers.
## What Didn't Make It (But Should Be on Your Radar)
CMS explicitly chose not to address prescription drug transparency requirements in this proposed rule. The agency published a separate Request for Information in June 2025 seeking feedback on how to effectively implement the prescription drug machine-readable file requirement, which has been on hold since 2023.
Expect separate rulemaking on prescription drug transparency in 2026. Given the complexity of pharmacy benefit manager (PBM) relationships, formulary tiers, rebates, and manufacturer pricing, the drug transparency requirements may end up being even more significant than these updates to medical service transparency.
## Price Transparency is continuing to gain momentum
These proposed changes signal that price transparency policy is moving from "get the data out there" to "make the data actually usable." In particular, we're encouraged because a lot of these updates came directly from feedback from the price transparency community. CMS & policymakers are listening and the foundation for fair and equitable healthcare for everyone is solidifying.
## FAQ
**When does this take effect?**
Most provisions would apply for plan years (policy years in the individual market) beginning on or after January 1, 2027. However, this assumes the rule is finalized in 2026. The comment period closes February 23, 2026.
**Will this reduce file sizes significantly?**
Potentially. Depending on how payers implement the network requirement, the network-based structure alone could reduce file counts and total file sizes. We will be closely monitoring the effects of these updates.
**What happens to existing Schema 2.0 requirements?**
These proposed regulatory changes are separate from (but complementary to) the Schema 2.0 technical format updates that CMS released in 2025. If this rule is finalized, the technical schemas will be updated again to accommodate the new data elements and file structures. Both sets of requirements will apply.
**How does the Utilization File interact with the Taxonomy File?**
They serve different purposes. The Taxonomy File tells you which provider-service combinations the plan considers clinically appropriate. The Utilization File tells you which of those appropriate combinations actually happened in the real world. Together, they help you distinguish between rates that could theoretically be used versus rates that are actually driving claims volume.
**Will plans still have to post rates for every provider in their network?**
Yes, the Plan detail will still be available but the network file will add an additional layer or organization to better identify networks and subsequent plans.
## Put Price Transparency Data to Work
Payerset delivers the most comprehensive price transparency data sourced directly from health plans and enriched with all-payer claims data. Compare negotiated rates by carrier, network, service line, and product type with confidence.
- Validate contracted reimbursement with claims-backed utilization context
- Filter out zombie rates and focus on real market dynamics
- Benchmark against the providers actually delivering care in your market
- Build stronger negotiation strategies with utilization-validated rate intelligence
---
# Insights — Articles
---
## The 2026 Price Transparency Field Guide Is Here
- URL: https://www.payerset.com/post/the-2026-price-transparency-field-guide-is-here
- Published: June 21, 2026
- Author: Payerset Team
- Section: News
We're very excited to introduce you to the first flagship resource from The Price Transparency Project.
It's called **The 2026 Price Transparency Field Guide**, and we debuted it at HFMA earlier this month. It's been creating a lot of buzz.
If you work on the finance or managed care side of a health system, you've watched price transparency move from a compliance checkbox into a strategic asset. The industry changes rapidly, and the Field Guide helps leaders in healthcare finance adapt to those changes and prepare for what's coming next.
## Why we built it
**Our goal was to create the best resource a healthcare finance professional could pick up on this subject.** We wanted something short and usable: the new CMS standards and the strategies we watch leading managed care teams run, grounded in the latest data and compressed into a guide you can finish over a coffee. By the time you close it, you'll know what your peers are doing with these files, and what payers already know.
Payer files now cover roughly 1.7 million care-delivery NPIs. And as of 2026, nearly every hospital in the country has posted a machine-readable file with rates a named executive has attested are true, up from 27% full compliance in 2021. The data finally got good enough to act on, and the guide is about acting on it before your competitors and payers do.
## What's inside
A few of the things you'll find inside the guide:
- **The day the single-code comparison stopped working.** We walk through a real chest-pain ED visit where one hospital looked like it out-earned its competitor on the 99284 facility fee. Build the full clinical bundle around that visit, and the conclusion flips: the same hospital was paid about $249 less per visit once every line item was counted. We show why single-code benchmarking misleads, and how to bundle instead.
- **The maternity rates that weren't there.** One of the largest systems in the NY metro was prepping for a negotiation and found a top-five competitor with zero maternity and OB/GYN rates on file. The rates existed. They were sitting in an AP-DRG structure nobody without the contract had ever seen, running 3 to 4 times the market benchmark. We explain where the highest-dollar carve-outs hide and how to surface them before a negotiation, not after.
- **A negotiation playbook you can run before the payer does.** Payers now arrive at renewals holding their own transparency analysis. We lay out the five-step process leading managed care teams use to build the counter-narrative first, with two 2026 case studies: a hospital that turned a payer's 15% overpayment claim into a roughly 2% reality and avoided the cut, and a system that won a rate increase on a growth service line sitting 35% below market.
- **What's actually changing in the rules.** Schema 2.0 went live February 2. Attestation enforcement, where a named senior executive at every hospital signs that the rates are true, accurate, and complete, began April 1. And the proposed Schema 3.0 could collapse fifty redundant rate files into one per network, while adding utilization, taxonomy, and change-log files that don't exist anywhere today. We break down what each change means for the people who pull these files, and we look ahead to the 2028 wave of standardized drug pricing data that's already reshaping fiduciary expectations.
## Leaders are already using it
The response since we started circulating print copies at the HFMA Annual Conference has been better than we hoped. CFOs, managed care leaders, and benefits advisors have already given some incredible feedback.
The strategies and case studies inside come from the same work we do alongside leading health systems and consulting firms preparing for real negotiations. We didn't want this to live behind a paywall, so we've made it available for free.
Whoever sits across the table from you at the next renewal is already reading this data. This guide helps ensure you're reading (and using) it better.
---
## Introducing The Price Transparency Project
- URL: https://www.payerset.com/post/introducing-the-price-transparency-project
- Published: June 2, 2026
- Author: Payerset Team
- Section: News
Our favorite part of what we do at Payerset is learning from our customers and the broader ecosystem. Every day, the leading minds in healthcare are working to make the vision of a functioning healthcare market a reality. We have the honor of talking, sharing, and learning from so many of these leaders, but we've never had a good way to share that with the community.
**In that vein, we're proud to announce The Price Transparency Project**: a place where we can share practical guides and best practices, highlight the leaders working on the ground every day, and advocate for the change we all want to see. Our goal is to make this a resource for the entire community. There's a lot coming over the next couple of months: a podcast to showcase the community, research to advance price transparency, policy advocacy and updates, practical guides for leaders and analysts, and much more.
## First up: the 2026 Price Transparency Field Guide
Our first drop is the 2026 Price Transparency Field Guide. Inside, you'll find direct lessons and key themes for getting the most out of price transparency data in 2026.
We're honored to build with you all and we'll see you out there.
---
## Putting Price Transparency Data to Work: Highlights From Our Relentless Health Value Episode
- URL: https://www.payerset.com/post/putting-price-transparency-data-to-work-highlights-from-our-relentless-health-value-episode
- Published: April 9, 2026
- Author: Matt Phillips
- Section: News
Several conversations about healthcare price transparency circulate at the policy level: what the rules require, who has complied, and what enforcement looks like. Equally important is what can happen with the data once it is in your hands.
We were excited to have our CEO, Jerry DiMaso, join Stacey Richter on Relentless Health Value Episode 506 to work through exactly that. Catch the full conversation on the [Relentless Health Value podcast](https://relentlesshealthvalue.com/episode/ep506-how-other-employers-shareholders-and-clinics-are-using-price-transparency-data-and-its-an-arms-race-with-jerry-dimaso). What follows distills the most actionable parts of that conversation for plan sponsors and providers.
## Where the data comes from
In 2019, hospitals were required to publish their gross charges, discounted cash prices, and negotiated rates with carriers. In 2022, carriers had to follow, publishing their own negotiated rates through machine-readable files. The result is a dataset that did not exist a few years ago: what payers have actually agreed to pay, across carriers, plans, and provider organizations, available to anyone who knows how to access and interpret it.
For self-insured employers and union plans, this is a meaningful shift. You can search your own plan by EIN to see what your carrier has negotiated on your behalf, compare that against other employers in your industry, and for the first time, do that without asking your TPA to grade its own homework.
## What plan sponsors can do with it
### 1) Benchmark against peers
When you search by EIN, you can pull your own plan's negotiated rates and compare them directly against other companies operating in your space. The disparities can be significant. If a competitor is receiving meaningfully better rates from the same carrier, that is a data point worth knowing before your next contract discussion.
### 2) Identify high-cost codes
Not all billing codes are equal in terms of volume or spend. Infusions, musculoskeletal procedures, and a handful of other service categories often account for a disproportionate share of plan costs. Transparency data lets you pinpoint the specific codes where you are paying above market, so your TPA can go back to providers and negotiate with something more specific than a request for a better overall discount.
### 3) See through the aggregated discount conversation
A TPA showing you a 90% discount across the board may be accurate on codes that rarely get used and significantly less accurate on the codes that drive most of your spend. You can now see both the charged amount and the allowed amount at the code level, which is enough to validate whether the headline number reflects what is actually happening in your plan.
From there, the actions available to plan sponsors include directing your TPA into specific renegotiations, carving out service lines for direct contracts or centers of excellence, and modeling what a different plan structure would look like for your population. All of these become more tractable when you have the rate data to ground the analysis.
## What providers can do with it
For clinical organizations, the shift is about leveling a playing field that has historically favored payers. Carriers negotiate with thousands of providers and have a comprehensive picture of rates across the market, while independent practices have typically had access only to their own contracts. With everyone being able to access the price transparency data directly from insurance companies, the information asymmetry is closing.
### 1) Benchmark rates and defend your value in negotiations
A small practice can now see what every carrier active in their geography is paying for every procedure code they perform. They can identify where they are under-reimbursed relative to neighboring providers, go to a carrier with specific data rather than a general request, and pair that rate data with their own outcomes and quality information to make a defensible case.
Jerry shared an example of a psychiatric provider with strong patient outcomes, specifically around reintegration into the community, that was being under-reimbursed for some of the services where their performance was strongest. Having the data gave them a specific, grounded conversation with their carrier rather than a general ask for a rate increase.
### 2) Identify new revenue channels
Smaller providers were sometimes unaware of other carriers active in their geography. Transparency data opens that picture up. A practice can discover contracts or carriers they were not previously working with, evaluate whether the rates make sense for their service mix, and make an informed decision about whether to pursue those relationships.
### 3) Make the case for staying independent
There is a preservation angle here that does not come up enough. When independent practices lose ground on rates and eventually sell to larger health systems, all the prices go up. Keeping independent practices financially viable is good for the whole system, and transparency data gives smaller providers a better chance of making that case before they reach the point of having to sell.
## The realistic picture on where this goes
Stacey raised two concerns worth addressing directly. The first is whether transparency causes prices to rise as providers discover they are being paid below market. The second is whether it creates an arms race.
The more honest framing, and the one Jerry landed on, is that the most realistic outcome is a regression toward the mean. When rates are visible, extreme outliers on both ends face pressure. That is closer to how a functioning market behaves than what healthcare has had for most of the last few decades.
The bigger lever may be fiduciary accountability. Plan sponsors can no longer claim they were unaware of publicly available rate data. That changes the calculus for how employers engage with their carriers and TPAs, and the responsibility runs in both directions. Carriers that know their plan sponsor clients have visibility into their rates face a different kind of accountability than they did when that information was opaque.
Jerry also noted that his work with the current administration and CMS reflects a continued and renewed push for more transparency, and that data quality and compliance have both been improving. The organizations getting real value from this right now are the ones who have invested in the methodology to use it well, not just access it.
Let's keep building and moving price transparency forward together.
---
## Key Updates in Price Transparency: Transparency in Coverage (TiC) Schema 2.0
- URL: https://www.payerset.com/post/transparency-in-coverage-schema-2-0
- Published: October 3, 2025
- Author: Jacob Little
- Section: Industry Insights
## Transparency in Coverage Schema 2.0: What Healthcare Leaders Need to Know
**TL;DR**
- CMS released the long-awaited Transparency in Coverage (TiC) 2.0 schema on Oct. 1, 2025. Enforcement begins Q1 2026.
- Updates and clarifications from December 2025 CMS meeting provide further enhancements.
- Major updates include consolidated provider group data, new plan sponsor/issuer fields, and clearer service setting codes.
- Improvements reduce file size, enhance accuracy, and enable more apples-to-apples comparisons across carriers.
- Bottom line: schema 2.0 is a meaningful step forward but leaves a lot of room for stronger improvements.
## December 2025 CMS Update
On December 11, 2025 CMS hosted a session for solution providers and the broader ecosystem to directly address questions as well as provide a few updates since the initial announcement. Here are highlights from this update:
### Network Name
- Network name will live within objects in the MRF and will reduce the dependency on file-naming conventions.
- The name should contain the common provider network name recognizable by the public.
- Format changed from a string to an array allowing a provider group to be associated with multiple network names without duplicating the provider group structure. This will reduce unnecessary data size increases.
- CMS also emphasized a common real-world pattern: a "network" usually isn't a single provider group. Instead, multiple provider groups can share the same network name, and Schema 2.0 supports documenting that relationship by repeating the same network name across multiple provider groups.
### Clarification on use of "Additional Information" field
Additional information is an open text field meant to serve as a "backstop" when:
- Standardized fields don't fully express the negotiated rate logic, OR
- Contractual provisions materially affect a rate but do not have dedicated schema attributes.
CMS grouped common uses into categories such as:
- Conditional or tiered reimbursement rules
- Alternative payment methodologies
- Contractual nuances
CMS also noted they monitor how additional_information is used and may standardize common patterns in future schema iterations if consistent usage emerges.
### Miscellaneous
**Clarity on new Severity of Illness field vs. Billing Code Modifiers**
- Severity of illness is typically associated with DRG-based coding/payment contexts, and may affect negotiated rates for DRGs.
- Modifiers are typically associated with CPT-based billing contexts, where modifiers may affect the negotiated rate.
**Opportunity to move more metadata in the TOC vs. repeating within MRFs**
- The TOC structure supports defining multiple reporting plans and pointing them to the same in-network file locations.
- This is framed as a core mechanism to avoid producing multiple large in-network files that are effectively duplicates.
- In Q&A, CMS addressed whether it's acceptable to have multiple in-network files with the same plan information without using a TOC. The response emphasized that if multiple files would share the same plan context/rates, the TOC structure is the intended mechanism to represent that relationship and avoid duplication.
Overall, there is room for improvement in standardization here but it is encouraging there is an ongoing dialogue on intention and best practices.
## What You Need to Know
- Schema 2.0 is the first major update to TiC since September 2023.
- The update consolidates provider groups, introduces plan context fields, and improves service setting reporting.
- By reducing duplication and adding clarity, data is now more consistent, human-readable, and actionable.
- However, out-of-network allowed amounts remain hampered by the 20-claim threshold.
- Payerset will continue to track CMS updates and provide customers with schema-aligned insights.
## Transparency in Coverage 2.0 and Why It Matters
When CMS announced Schema 2.0, expectations were high. Industry leaders had requested details on outliers and carveouts, better out-of-network rules, and overall improved detail to more accurately reflect payer-provider contracts. While not all of these changes were included, this release still represents progress.
At a high level, TiC 2.0 brings higher-quality data, reduced file sizes, and clearer plan-level context. For CFOs and managed care leaders, this means a more reliable foundation for benchmarking rates and comparing contract terms across insurance carriers.
## Key Schema Improvements
### Provider Group Data Consolidation
- **Internal references only:** Provider groups must now be defined once and referenced across the file. This should greatly reduce duplication and file size.
- **Removed separate reference file:** All provider group information now resides within the in-network file, preventing dead links and missing provider data.
- **Impact:** Fewer errors from dead links (e.g., Cigna, Geisinger) and easier parsing.
### New Plan Identification Fields
- **Plan sponsor name:** Identifies the employer or group sponsoring the plan.
- **Issuer name:** Separates carrier from plan name for clarity (e.g., "Issuer: BCBS Tennessee" vs. "Plan: Premium Plus PPO").
- **Clarified plan name field:** Now represents only the plan itself.
- **Product type:** Standardized classification (HMO, PPO, EPO, etc.). While this field is not in the schema, it is proposed and documented in the notes. We hope it is included in a fast follow-up update.
- **Impact:** Enables apples-to-apples comparison of plans across carriers. Previously manual workarounds (like Payerset's own categorizations) can now be automated.
### Service Setting and Place-of-Service Enhancements
- **"Setting"** is a new field that now distinguishes between inpatient and outpatient. Previously, inpatient and outpatient information had to be derived from multiple fields.
- **No empty allowed-amounts lists:** Enforces meaningful data if an out-of-network file exists.
- **Impact:** Simplifies alignment between TiC data and claims data (e.g., Type of Bill, POS codes).
### DRG Severity (SOI) Attribute
- A new attribute for Severity of Illness (SOI) within DRGs.
- **Impact:** Allows more precise inpatient benchmarking by distinguishing between base and high-severity DRG payments.
## Where Schema 2.0 Fell Short
- **Out-of-network threshold:** The 20-claim minimum rule remains. This means many payers can legally avoid posting OON data. While CMS removed "aggregation to a single provider," the broader problem may persist.
- **Missed opportunities:** No inclusion of outlier or carveout objects, which would have captured the real nuance of contracts.
- **Impact:** Transparency improves, but contract reality still lags behind.
## How to Act on This
The good news is that for Payerset customers, no action is required. We will incorporate the new fields and guide you on how to apply them to your analysis. The new data will simply "flow through," and the previous schema will be snapshotted so you can access historical data and see how these schema updates have materially changed the data and the insights derived from it.
## FAQ
**When does schema 2.0 enforcement start?**
Feb. 2, 2026. Files published after that date must conform to the new schema.
**Will schema 2.0 reduce file sizes?**
Yes. Provider group references alone dramatically reduce redundancy.
**Why is the out-of-network threshold still an issue?**
The 20-claim rule often results in payers posting nothing. This reduces transparency rather than protecting privacy.
**How does this help CFOs and contracting teams?**
With standardized plan identifiers and service settings, comparisons are more reliable. Negotiation leverage improves when contracts are benchmarked accurately and consistently.
**What's next?**
CMS has hinted at future schema changes, including drug pricing elements in 2026. From our perspective, this TiC 2.0 update was a half measure, with much left to be desired. We hope this update is not a foreshadowing of what the Drug Price Transparency data will hold.
---
## Managed Care Contract Negotiations & Price Transparency in 2025: Lessons from the Front Line
- URL: https://www.payerset.com/post/managed-care-contract-negotiations-price-transparency-in-2025-lessons-from-the-front-line
- Published: September 28, 2025
- Author: Joseph Tollison
- Section: Industry Insights
**TL;DR**
Margins are tightening, denial behavior is evolving, and employer dynamics are shifting. Organizations that win are building a proactive defense with stronger advocacy, clear accountability, smart automation, and day-to-day adaptability. The sections below open with longer-form context followed by actionable bullets that match those lessons from the front line.
I recently attended the [Florida HFMA Annual meeting](https://www.hfma.org/chapters/region-5/florida/) and in addition to coming back with some sand in my suitcase, I also came back with some key insights, insights directly from the voices of hospital managed care leaders. While challenges were shared, there was an overall feeling in the room of support and of tackling these challenges together. Here's what I learned.
## Snapshot: Current State for Managed Care Leaders
Hospitals and care delivery organizations are managing a payer mix that leans more heavily on Medicare and Medicaid while commercial enrollment fragments across narrow networks, ICHRAs, and self-funded plans with custom rules. Retail entrants and PE-backed ambulatory platforms continue to peel away profitable volumes, especially in surgery and advanced imaging. At the same time, insurance carriers are reshaping payment behavior through edits that short pay first and force providers to appeal, even when prior authorization exists.
Teams are turning to price transparency machine-readable files (MRFs) and market analytics to anchor rates and strategy. The data is increasingly usable and, when validated against internal audits, can be accurate enough to influence managed care contract negotiations. However, variability in plan definitions and file construction still creates noise.
- **Relentless payer mix pressure.** Expansion of government programs and narrower commercial options keep average reimbursement under strain.
- **Disruptors siphon revenue.** Retail and PE-backed sites of care move profitable work out of hospital settings.
- **Denials evolve into partial pays.** Edits reduce payment up front and push the burden of proof to care delivery orgs.
- **Data becomes leverage.** Transparency data is useful but requires careful validation and standardization.
## The Very Real (and Specific) Big Problems
The front line is experiencing two types of friction. First, payment tactics that are not outright denials but function like them. An example is Medicare Advantage inpatient claims initially paid at observation levels, which forces the hospital to fight back to a DRG payment. Another example is professional and facility E/M downcoding that reduces level 5 visits to lower levels and requires appeals to restore payment.
Second, the constant flow of policy and provider manual changes increases administrative lift. In one state, a Provider Stability Act created stronger notification and timing standards for commercial plans, and leaders suggested similar guardrails would help elsewhere. Prior authorization remains a persistent barrier, particularly when moving patients from acute to post-acute settings, where delays for rehab and SNF transfers tie up beds and frustrate families.
- **Non-denial tactics.** MA inpatient paid as observation until the DRG is earned back. E/M downcoding that must be overturned one claim at a time.
- **Policy change overload.** Website-only notices and short windows drive operational whiplash.
- **Prior authorization bottlenecks.** Rehab and SNF transfers stall while teams chase medical necessity reviews.
- **Employer and ERISA complexity.** Self-funded plans support tight edits. State guardrails often exclude ERISA unless contracts force direct notice and mutual acceptance.
## What Is Working Right Now
Organizations that are getting traction have formalized ownership, codified their positions, and tightened their operating cadence with carriers and internal partners. A centralized managed care compliance function tracks policy changes, monitors denial spikes by reason and line of business, and brings evidence into monthly Joint Operating Committee (JOC) meetings.
Internal policy libraries give revenue integrity, utilization management (UM), and coding teams a consistent stance on contested topics like level of care and room and board. Contract language is being upgraded to include material change notice, quantitative limits on prepayment reviews, and clear escalation timelines. Post-acute partners are pulled into regular roundtables to improve first-pass authorization quality and move patients faster.
- **Central command center.** One accountable owner for policies, denials, and payer trend analytics.
- **Policy playbooks.** Board-approved internal policies for hot spots like LOC, E/M levels, and room and board.
- **Stronger contracts.** Material change notice, mutual acceptance for impactful policy shifts, and limits on prepayment reviews.
- **Peer collaboration.** Quarterly councils with SNF and rehab partners to improve first-pass auths and transfers.
## Practical and Impactful Plays Managed Care Leaders Can Implement This Quarter
Short-term wins come from clarifying ownership, instrumenting analytics, and standardizing responses. Assign a leader who has authority across revenue cycle, UM, and contracting. Build a policy library for your top dispute areas and set three-year reviews with named owners. Triage every carrier policy into benign, conflicting, or material, and decide whether to educate, escalate, or negotiate. Track appeal overturn rates for specific edits. If a carrier's automated downcoding is overturned 70 to 80 percent of the time, present that evidence to disable the edit for your tax ID. Pilot tools that extract medical necessity from clinical notes to improve first-pass approvals for post-acute transfers.
- **Appoint a compliance lead.** Give them cross-functional authority and a clear mandate.
- **Stand up a policy library.** Top 10 issues first, with owners and review dates.
- **Classify policy changes.** Educate, escalate, or negotiate based on materiality and contract fit.
- **Instrument appeals.** Use overturn rates to challenge automated edits at the carrier.
- **Improve first-pass auths.** Use tooling to surface necessity elements from notes for rehab and SNF.
## Price Transparency Data: Accurate and Useful
Teams are using payer MRF-derived rate intelligence for competitive scans across neighboring counties and to set realistic ask targets for specific services. When internal managed care teams can validate the contracted rates against claims and remits, the alignment can be strong enough to guide strategy and is often accurate to the contracted penny, depending on the carrier and geography.
The main caution is variability. Plan constructs and file formats can blur narrow versus broad networks and mix rates that do not actually apply to a given membership segment. For this reason, ensure that your price transparency provider is offering plan-level detail by parsing all of the MRFs (not just the files associated with the most common plan names), and ensure that your price transparency partner is validating price transparency data with real-world all-payer claims data so you can build a strategy with confidence.
The data is powerful for positioning, but leaders should align on plan definitions, validate with claims, and supplement with internal cost and case-mix context.
- **What works.** Detailed peer service-line and billing code benchmarks.
- **What needs care.** Plan-level comparisons and validation with claims.
- **How to use it.** Anchor negotiations with validated ranges and pair them with internal cost and case mix to build the right strategy and negotiation approach. In addition, don't make your negotiation strategy solely about the services you deliver today, but also about where you are planning to grow through build-outs or acquisitions.
## Commercial and Employer Dynamics
Self-funded employers are paying the bill and frequently support tighter edits and stronger utilization controls. The best results come when providers bring employers into the conversation with real patient access data, delay metrics, and examples of unnecessary friction. Some large administrators operate custom plan rules for marquee employers that differ from standard policies. Contracts can reduce surprises by requiring direct notice to Managed Care, a defined review period, and mutual signature for material changes that impact payment. This is especially important when ERISA limits state-level protections.
- **Expect tighter controls.** Employers and TPAs often support aggressive cost containment.
- **Bring employers real-world evidence.** Access metrics and member experience data move the discussion beyond MLR.
- **Contract for notice and consent.** Direct notifications, review windows, and mutual signatures for material changes.
## The Direction of Travel: Five-Year Outlook
The operating model is shifting from reactive, per-click payment toward risk-bearing arrangements with real downside. Consolidation will continue on both the care delivery and carrier side. Teams that remain fee-for-service oriented without building risk capabilities will struggle.
Organizations that invest in advocacy, accountability, automation, and adaptability can maintain access while protecting financial stability. There is ongoing debate about how far consolidation and integration will go, and whether public policy will introduce more universal structures. Regardless of the policy path, the common traits of resilient systems are already visible: they organize their data, operationalize their policies, and close the loop from contract language to front-line workflows.
- **Consolidation accelerates.** Fewer, larger platforms on both sides of the table.
- **Risk becomes standard.** Quasi-risk to full risk replaces pure per-click thinking.
- **Four A's become table stakes.** Advocacy, accountability, automation, and adaptability define winners.
## A Simple Checklist to Start Tomorrow
Often getting started on the right foot is the part of the challenge. And what I heard from the room of professionals living and breathing this daily was the following checklist to tackle one by one.
1. Appoint a Managed Care Compliance lead with authority across revenue cycle, UM, and contracting.
2. Build a policy library for your top 10 dispute areas and schedule three-year reviews.
3. Instrument denial analytics by carrier, reason, and service line with alerting for spikes.
4. Prepare template clauses for material change notice, prepayment review limits, and JOC escalation.
5. Pilot documentation AI and denial prediction on targeted service lines.
6. Convene a quarterly post-acute council to improve first-pass auths and transfer throughput.
7. Use transparency data accurate and detailed benchmarks tied to referral patterns and case mix to set realistic asks.
## Call to Action
Reach out to Payerset for the most comprehensive price transparency data sourced directly from health plans and enriched with all-payer claims data. Build a trusted, accurate rate foundation for negotiations and strategy with plan-level detail and validation using claims that give contracting and revenue integrity teams confidence.
Contact Payerset to see how combining price transparency and claims data delivers trusted price intelligence that can improve your next negotiation.
---
## A CFO Blueprint: What to Look for in Healthcare Price Transparency Solutions
- URL: https://www.payerset.com/post/cfo-blueprint-healthcare-price-transparency-solutions
- Published: September 27, 2025
- Author: Joseph Tollison
- Section: Industry Insights
Price transparency solutions for healthcare aren't simply just another compliance checkbox. For hospital CFOs, managed care leaders, and finance directors, it's the difference between negotiating blind and negotiating with clarity. The right solution helps you cut through the noise, spot hidden opportunities and move with confidence in a market where every dollar counts.
But here's the challenge: not all healthcare price transparency solutions are created equal. Many make big promises but fall short when you dig into the details. So how do you know which solution will actually deliver meaningful insights you can trust? And while yes, Payerset is indeed a price transparency solution option, we did our best to take a step back and take what we have learned and heard over the years from finance leaders such as yourself to put this guide together. Our why and reason to exist continues to be driven democratizing healthcare pricing through real transparency and our hope is that this guide helps contribute and support that mission that serves as our north star.
Almost 75% of CFOs in the [HFMA survey](https://www.hfma.org/finance-and-business-strategy/the-healthcare-cfo-of-the-future-turning-risk-into-opportunity/) identified strengthening payer-provider relationships as a leading focus for the next three years, either taking it on directly or entrusting their teams to drive meaningful progress. Price transparency fits into that focus and strategic need.
So here's our take on the top five characteristics and the supporting questions to ask and get answers to when you're considering what's next for price transparency in your organization.
## 1. Look Beyond Price Transparency Compliance: Does It Create Real Business Value?
A lot of vendors stop at "checking the box" for compliance. They parse machine-readable files (MRFs) and hand you a massive dataset, but they don't make it usable. That's not enough.
You need a partner who transforms raw, chaotic files into clean, structured intelligence. Ask yourself and more importantly the price transparency solution provider you're speaking with:
- Can this solution show me actual contracted rates in seconds, not hours or days?
- Is this data complete? Many solutions do not acquire all of the payers' posted files because it is cost-prohibitive, resulting in missing or inaccurate reimbursement rates.
- Does it reduce complexity by cleansing and formatting the data so that it is ready for analysis in Excel or a BI tool, allowing me to integrate it with my internal data?
- Is it verified with real-world claims data so I can build meaningful cohort comparisons and trust the rates posted by the payers?
In reading HFMA's *[The Healthcare CFO of the Future: Turning risk into opportunity](https://www.hfma.org/finance-and-business-strategy/the-healthcare-cfo-of-the-future-turning-risk-into-opportunity/)*, one of the quotes shared in the report really echoes the need to go beyond a compliance checkbox. "With our system being located between two metro areas, we want to make sure we're not only in compliance with price transparency requirements, but also that we're maintaining a competitive advantage for those services," [Kolin Huth](https://www.linkedin.com/in/kolin-huth-1285441a2/), CFO at Jackson County Regional Health Center in Maquoketa, Iowa.
## 2. Trust the Data: Is It Accurate and Auditable?
Price transparency data is messy by nature. Phantom rates, zombie rates, and mismatched provider groups can easily distort the truth. If your solution isn't auditing and normalizing data, you're left with unreliable numbers. And unreliable numbers can cost millions.
Look for a healthcare price transparency solution that:
- Continuously audits payer files for compliance and accuracy
- Flags and filters zombie rates so they do not skew results
- Provides clear sourcing so you always know where the numbers come from
- Retains all data over time for historical benchmarking
- Does not cherry-pick payer plans or products and includes the full range of plans, both large and small
This type of [complete and comprehensive data](/post/why-complete-healthcare-price-transparency-is-essential-and-what-it-takes-to-get-it-right) builds trust. And transparency without trust isn't transparency at all. You can scope out [our payer scorecard data](https://docs.payerset.com/payers/aetna-price-transparency) which provides another layer of visibility to build trust. And feel free to [browse our data dictionary](https://docs.payerset.com/using-the-payerset-platform/data-dictionary) to see all the fields that are continuously captured in our SaaS solution.
## 3. Prioritize Speed and Usability of Price Transparency Data
In the real world, you don't have hours or oftentimes even the on-staff expertise to hunt through a 10-terabyte file or wait weeks for a consultant to deliver a spreadsheet. You need answers fast, ideally in minutes, with just a few clicks.
Your healthcare price transparency solution should:
- Deliver instant rate lookups across payers, providers, and codes
- Offer intuitive dashboards anyone on your team can use (no need for advanced data science or engineering backgrounds)
- Fit seamlessly into existing workflows instead of adding new headaches
Here's a good test when you're on the next demo: Think about, could your managed care team pull insights from the tool during a live negotiation? If the answer is no, it could be a signal to keep looking.
## 4. Flexibility and Built for Your Future Needs as Hospital CFO
Healthcare contracts aren't static. Neither are your needs. A price transparency partner should help you tackle today's challenges and set you up for tomorrow's opportunities whether it's preparing for a contract negotiation, identifying leaked revenue or evaluating where to expand the business.
When evaluating a price transparency solution look for:
- Access to historical benchmarking to see how rates shift over time
- Custom queries that let you slice data the way you need
- Scalable infrastructure that grows with your organization's questions
- A vision for the future and upcoming product roadmap enhancements that will benefit you
You want a solution that adapts to your strategy, not one that boxes you in.
## 5. A True Partner, Not Just a Vendor
At the end of the day, technology is only half the story. You need a partner who shares your commitment to fairness, clarity and better outcomes. Someone who doesn't hide behind jargon or black boxes, but instead gives you the confidence to take bold, informed action.
So next time you hop on a price transparency solution demo take note of:
- Do they explain their methods in plain language?
- Are they open about their limitations as well as their strengths?
- Do they treat transparency as both a product feature and a core value?
- Are they able to share referencable customers?
- Can they share a sample data set to provide more confidence in their offering?
## The Bottom Line: Selecting a Price Transparency Solution That's Right for Your Organization
Choosing a price transparency solution is more than a procurement exercise. It's a strategic decision that can reshape how your hospital system negotiates, benchmarks, and plans for the future. The best solutions don't just simply give you more data, they give you clarity, confidence, and control. They turn a regulatory burden into a competitive advantage.
At Payerset, we believe price transparency should empower, not overwhelm. That's why we transform trillions of rows of payer-provider rate data into insights your team can not just only trust but use effectively. Because when you can see clearly, you can act boldly.
---
## Why Complete Healthcare Price Transparency is Essential - And What It Takes to Get It Right
- URL: https://www.payerset.com/post/why-complete-healthcare-price-transparency-is-essential-and-what-it-takes-to-get-it-right
- Published: May 14, 2025
- Author: Jerry DiMaso
- Section: Parsing Payer MRFs
Comprehensive healthcare price transparency data has become an essential input for providers, consultants, and healthcare systems alike, helping to support fair negotiations, data driven strategic planning, and stronger financial forecasts.
We outline why having the most complete healthcare price transparency data is critically important and provide a framework for avoiding the common pitfalls in using transparency data.
## Every Plan, Every Service, Every Provider
### Quick Recap
We've talked before about how transparency data isn't always as transparent as it sounds but here's a refresher of why it can be challenging to create the complete view of the data:
- **File Inflation** - Some payers publish massive files filled with inflated provider lists that include non-contracted entities, adding volume but not value—and making the files harder to process. Aetna and Anthem Elevance are textbook examples of this.
- **Temporary access windows** - Files are sometimes hosted behind APIs with download limits or expiration windows, making it difficult to access everything in time. This is an approach UnitedHealthcare takes among others.
- **Non-descriptive plan names** - Plans labeled with vague or unrecognizable names can be overlooked unless a system is in place to retrieve and examine every file, regardless of how it's labeled.
- **Inconsistent file mapping and buried rates** - Different health plans point to different file sets. Some overlap, others contain unique data. Without capturing and parsing everything, critical pieces can be missed.
### Why Complete Healthcare Price Transparency Data Is Non-Negotiable
So yes, while accessing healthcare price transparency data can be challenging, what's often overlooked is the absolute importance of getting the most detailed and comprehensive view possible. Whether you're advising a provider on a contract renewal, benchmarking rates across markets, or trying to understand how a payer's network structure has changed, having all the granular data is foundational.
Missing even one set of files, or failing to associate it properly with the right health plan, can mean overlooking entire reimbursement structures or excluding payer-provider arrangements that are critical to the bigger picture.
Here's why completeness matters:
- **Contractual visibility and alignment** - Knowing every rate that is associated with each health plan and provider group allows your team to clearly understand contract terms, uncover hidden rate structures, and anticipate negotiation leverage points. Knowing what's in the data—and what's missing—affects your leverage at the negotiation table.
- **Risk reduction** - Incomplete data can lead to assumptions that don't hold up under scrutiny, creating risk for your organization or clients.
- **Operational efficiency** - Cleaner, more complete data reduces the need for manual follow-ups, rework, or reliance on anecdotal evidence.
- **Historical context** - Being able to trace how rates and relationships have changed over time can reveal patterns, disruptions, or opportunities for improvement.
Put simply, the more complete your data, the better your decisions. And in today's climate, precision is a competitive advantage.
## What a Complete Approach Looks Like
The good news is that this is possible by following a framework with a few key principles:
- **All files, every time** - Every transparency file from every payer must be captured—regardless of whether the health plan name looks meaningful at first glance. Every file represents a potential set of unique contract terms.
- **Full parsing and matching** - Files need to be deeply parsed, flattened, and matched back to health plans in a structured, consistent way. This is especially true for JSON files with nested structures that mask key data. Every plan type, including employers, should be accessed and parsed and mapped appropriately for use in analysis.
- **Data validation and cleanup** - Irregularities like broken links, mislabeled plan IDs, or non-compliant formats need to be flagged and resolved wherever possible to avoid misinterpretation.
- **Historical access** - The ability to see past versions of data, not just current snapshots, provides essential context for historical trends and network changes. This enables retrospective analysis that supports smarter forecasting.
- **Self-service usability** - All of this must be made accessible to analysts, strategists, and managed care professionals—without requiring heavy engineering support. That means intuitive interfaces, searchability, and the ability to filter by attributes such as provider, plan, or rate attribute.
All of these capabilities add up to more than technical best practices. They represent the core infrastructure required to make price transparency truly actionable.
## Payerset is the Platform Built for These Needs
We've built our platform from the ground up to meet these needs. Every piece of our technology stack is designed to support full data access, ease of use, and real-time decision-making.
- We collect all files from every payer and TPA that's compliant with the federal Transparency in Coverage rule.
- We associate rates back to their correct health plans, even when plan names are vague or inconsistent.
- We ensure representation across more than 300 carriers and third-party administrators, including both national brands and regionals.
- Our longitudinal archive gives users the ability to trace changes over time—especially important when providers are added or dropped, or when contract terms change with little notice.
With **Rate Explorer**, business users can query and analyze pricing structures via an intuitive self-service portal—no coding or data prep required.
For data and analytics teams, our **Data Lake** product provides structured, historical files ready for integration into BI platforms or internal tools.
We also offer robust data quality management, ensuring your team isn't spending hours cleaning or reconciling files manually.
## Who This Is For
Our platform is built for professionals who need precision, speed, and flexibility: managed care experts, provider strategy leaders, healthcare finance analysts and consultants, revenue cycle consultants, and advisory firms all use our platform to do work that depends on nuance and completeness.
These users don't just need a general idea of rate levels or averages. They need specifics—at the CPT code level, across multiple plans and geographies, historically and in real time.
When your work influences contract terms, network participation, strategic growth, or reimbursement modeling, you can't afford to miss a data point. And the tools you use must be as thorough and reliable as the work you deliver.
---
## The Positive Future of Price Transparency in the U.S. in 2025
- URL: https://www.payerset.com/post/the-positive-future-of-price-transparency-in-the-u-s-in-2025
- Published: February 26, 2025
- Author: Jacob Little
- Section: Industry Insights
*This post was originally published on February 26, 2025, and updated on August 27, 2025.*
On February 25, 2025, President Trump signed his [second executive order regarding price transparency](https://www.whitehouse.gov/presidential-actions/2025/02/making-america-healthy-again-by-empowering-patients-with-clear-accurate-and-actionable-healthcare-pricing-information/). The executive order is titled "Making America Healthy Again by Empowering Patients with Clear, Accurate, and Actionable Healthcare Pricing Information." Below we cover the background his new executive order builds on, challenges we've overcome as an industry and ones that still remain, as well as outline the path forward to ensure the positive impact these new regulations can help create for the healthcare landscape.
We'll update this post throughout the year as healthcare price transparency continues to evolve.
## Foundation laid for price transparency
This new order builds upon Executive Order 13877, issued on June 24, 2019, titled ["Improving Price and Quality Transparency in American Healthcare to Put Patients First."](https://trumpwhitehouse.archives.gov/presidential-actions/executive-order-improving-price-quality-transparency-american-healthcare-put-patients-first/) This regulation was a paradigm shift, requiring hospitals and health plans to provide meaningful price information to the public. Specifically, it mandated:
- **Hospitals** to maintain a consumer-friendly display of pricing information for shoppable services and [provide a machine-readable file (MRF) listing negotiated rates for all services](/post/making-sense-of-hospital-price-transparency-data).
- **Health plans** to publish their negotiated rates with providers, their out-of-network payments, and the actual prices paid for prescription drugs.
- **Health plans** to maintain a consumer-facing tool that allows individuals to access price information.
This was a bold and necessary step toward healthcare and hospital price transparency.
## Barriers to implementation and enforcement
Despite the intent of these regulations, implementation faced several challenges. The data published by payers was often:
- **Infrequently updated**, resulting in missing or incomplete information.
- **Low quality**, with issues such as duplicate records, inconsistent naming, and non-standard billing classifications.
- **Difficult to access**, perhaps the biggest obstacle. Machine-readable files (MRFs) were often intentionally complex, making extraction, normalization, and usability a significant challenge.
As an industry, we have made significant progress in bringing the promised vision to reality yet still there's so much to be done. As a price transparency company, we are frequently asked: "What will the Trump administration do this time around to make this better?"
While we were confident that this administration would continue supporting price transparency, we were uncertain about specific priorities. However, on February 25, 2025, we were excited and relieved to see that the administration is fully committed to advancing transparency.
## The 2025 Executive Order: A renewed focus on price transparency
As stated in [Section 3 of the executive order](https://www.whitehouse.gov/presidential-actions/2025/02/making-america-healthy-again-by-empowering-patients-with-clear-accurate-and-actionable-healthcare-pricing-information/), titled "Fulfilling the Promise of Radical Transparency," the Secretary of the Treasury, Secretary of Labor, and Secretary of Health and Human Services are now required to take all necessary actions to:
- Enforce existing healthcare price transparency regulations.
- Within 90 days, mandate the disclosure of actual prices of services rather than estimates.
- Issue updated guidance to ensure that pricing data is standardized and easily comparable across hospitals and health plans.
- Update enforcement policies to ensure [complete, accurate, and meaningful data reporting.](/post/why-complete-healthcare-price-transparency-is-essential-and-what-it-takes-to-get-it-right)
Sections B and C of the order are particularly critical, as they address major data challenges that have plagued price transparency since 2022.
## What are the price transparency challenges that must be addressed?
We live and breathe this data every day and while price transparency laws have led to progress, several persistent issues must still be resolved.
### 1. Conflicting rates in machine-readable files (MRFs)
Many MRFs list multiple rates for the same provider and service within the same plan but lack the necessary details to clarify which rate applies. This becomes especially problematic in rental networks, such as those used by Blue Cross Blue Shield, where the same provider appears under different contract structures.
One example of this is a provider is listed multiple times under the same plan with different rates, leading to confusion and reduced trust in the data. There are some particularly poor payers when it comes to publishing conflicting rates but on a positive note, United Healthcare made major improvements in 2023 by reducing conflicting rates, but the issue is not fully resolved.
### 2. "Zombie Rates" (Ghost Rates)
Some MRFs contain clinically implausible rates, meaning a provider is listed for services they have never billed for and likely never will. While transparency laws must ensure all contractually covered rates are disclosed, better data refinement strategies are needed to avoid unnecessary data bloat.
An example of this could be pediatricians listed with rates for hip replacement surgeries or hospitals listing rates for treatments they have never provided.
### 3. Barriers to accessing data
Many organizations lack the technical infrastructure to process the massive datasets contained in MRFs. Some insurance carriers submit multiple files for the same network with nearly identical data, creating redundancy and inflating file sizes.
**File Size Issues Across Payers:**
- **Elevance (Anthem)** have some files that exceed 1 terabyte when uncompressed and contain over 10 billion records per file when fully processed.
- **Aetna** files often range from several hundred gigabytes across many of their employer health plans.
- **United Healthcare** files, when uncompressed, are typically around 100 gigabytes.
- Some payers submit multiple MRFs for the same network, adding to unnecessary data duplication.
The sheer size and complexity of these files make it impossible for most organizations to access and analyze the data effectively without specialized technology.
### 4. Poor data retention policies
Many payers do not retain historical MRFs, making it impossible to verify past rates for billing disputes. Even if historical data is retained, changes in schema & structure are rarely documented.
These limited time windows can have very real consequences - reduced auditability, lack of accountability of payers & less negotiation leverage for care organizations.
At Payerset, we store all historical MRFs indefinitely, but this should be standard practice performed by the carriers posting these files. Arguably, this could even be centralized by CMS for single source access and better auditability.
### 5. The problem of "Percent of Charges" contracts
Many contracts express prices as a percentage of charges, rather than providing a fixed dollar amount. This makes it impossible to determine the real cost until after a procedure is billed, reducing price transparency.
A common scenario is a contract that states Procedure X is reimbursed at 75% of charges. However, without knowing the provider's billed charge, there is no way to estimate the cost. While hospital price transparency data helps bridge this gap, it does not cover all healthcare settings, making true price comparison more challenging.
### 6. Expiring file links and API rate limits
Many payers provide MRFs through public APIs, but these APIs have rate limitations that prevent full data downloads before links expire. Some payers even intentionally limit the amount of time current files are accessible when they are published with short download expiration windows.
We overcome this by deploying a distributed computing approach that downloads files in parallel before they expire. However, this should not be necessary.
## A step in the right direction: CMS Schema 2.0 for Transparency in Coverage (TiC)
A lot of exciting updates have happened over the past several months with one of those changes being a new FAQ related to "Schema 2.0" released by CMS.
Schema 2.0 sets out to solve some of the challenges with the initial implementation of the TiC Act. A few of the highlights we're excited about are much clearer direction on provider groupings to help reduce file sizes in many cases and increase usability, increased standardization with place of service and service codes, required Table of Contents, and more. [We dive deep in to these upcoming changes in a Q&A with our co-founder and CTO here.](/post/navigating-the-upcoming-changes-in-cms-schema-2-0-for-healthcare-price-transparency)
## Moving forward: A renewed commitment to healthcare price transparency
We have worked diligently for years to clean, transform, and simplify this data to help customers realize the intended value of these regulations. However, these challenges should not exist and they are not in the spirit of the law. The executive order earlier this year indicates a turning point. We now have the opportunity to accelerate progress, raise the standard for data quality, and ensure that price transparency delivers on its promise.
We are on a mission to:
- Democratize healthcare pricing.
- Make price transparency real, accessible & truly transparent as it should be.
- Work side by side with partners, customers & industry stakeholders to create a transparent, fair and equitable healthcare market.
Here's to 2025 and beyond!
---
## Making Sense of Hospital Price Transparency Data: 2025 Updates & Solutions
- URL: https://www.payerset.com/post/making-sense-of-hospital-price-transparency-data
- Published: February 23, 2025
- Author: Joseph Tollison
- Section: Industry Insights
*This blog was originally published on February 23, 2025 and updated on September 10, 2025*
Hospital price transparency data has come a long way since its messy early days. But even with 2025 regulations, inconsistencies still make analysis challenging. In this post, we break down the current state of hospital machine-readable files (MRFs), the latest updates, and how Payerset simplifies the process.
[Click here for direct links to each Hospital MRF by State](https://docs.payerset.com/hospital-transparency/mrf-links-by-state)
## The State of Hospital MRF Data
Unlike the Transparency in Coverage (TiC) rule, which applies to insurance carriers, hospital price transparency regulations are a separate initiative with different requirements, schemas, and implementation timelines.
When hospital price transparency requirements were first introduced, the lack of standardization made meaningful analysis nearly impossible. Hospitals published data in different structures, with no consistency in file formats, terminology, or coding methodologies.
Some of the most notable challenges included:
- **Inconsistent File Formats** – No unified schema for reporting, making data difficult to compare
- **Limited Field Utility** – Missing or vague pricing fields made apples-to-apples comparisons difficult
- **Lack of Cross-Referencing** – Theoretically, hospital-posted prices should match payer-posted prices, but discrepancies were common
- **Disorganized File Hosting** – No centralized or predictable locations for file access
Was this lack of consistency intentional obfuscation or just poor implementation? The reality is likely a mix of both, compounded by resource-constrained hospital IT teams.
## 2024-2025 Hospital Price Transparency Data Rule Updates
### July 2024: Standard Charges Format Implementation
To improve data consistency, the July 1, 2024, regulations introduced a standardized format, requiring hospitals to publish:
- **JSON Schema** – A structured, machine-readable format for improved parsing.
- **Wide-Form CSV Format** – A more accessible but still structured alternative.
### January 2025: Enhanced Accessibility and Data Accuracy
Starting in January 2025, additional measures were implemented to improve usability and compliance:
- **Increased Accessibility** – Hospitals must prominently display links to their MRFs in website footers.
- **Affirmation of Data Accuracy** – Hospitals must attest that their posted data is complete and correct.
- **New Data Elements:**
- Estimated Allowed Amounts – Projected pricing for services.
- Drug Unit Measurement Standardization – Making National Drug Code (NDC) comparisons easier.
The Centers for Medicare & Medicaid Services (CMS) [provides a detailed breakdown](https://www.cms.gov/newsroom/fact-sheets/hospital-price-transparency-fact-sheet) of the Hospital Price Transparency Rule, including the latest regulatory updates effective July 1, 2024. Similarly, the American Hospital Association (AHA) [discusses how hospitals are implementing these new requirements](https://www.aha.org/news/headline/2024-07-01-new-hospital-price-transparency-requirements-take-effect).
These updates were significant steps forward, but they didn't entirely solve the challenges of normalizing hospital price data for analysis.
## May 2025 Changes to Hospital Price Transparency Guidance
[On May 22, 2025, CMS issued](https://www.cms.gov/files/document/updated-hpt-guidance-encoding-allowed-amounts.pdf) updates regarding Hospital Price Transparency Guidance. The key change is that new guidance for encoding allowed amounts. In short, CMS is closing the "loophole" of placeholders and requiring real dollar values so that hospital transparency files are more consistent and useful.
Here's a summary of the changes that occurred in May.
**Dollar Amounts Required** — Hospitals must encode payer-specific negotiated charges as actual dollar amounts in machine-readable files (MRFs), whenever they can be calculated (e.g., negotiated base rate, case rate, per diem, or percentage of a known fee schedule).
**No More "999999999"** — Hospitals should stop encoding nine 9s as a placeholder for "estimated allowed amounts." CMS found this was overused and made the data less useful.
**Definition of "Estimated Allowed Amount"** — Must represent the average dollar amount historically received from a third-party payer for an item/service.
**How to Calculate** (must use prior 12 months of electronic remittance advice (ERA/835) data):
- If the negotiated algorithm/percentage applied for only part of the year, report the average dollar amount for that period only.
- If the item/service was provided at least one time in the prior 12 months, report the average of those paid amounts, and note "one or more instances in the prior 12 months."
- If the item/service was not provided in prior 12 months, encode an expected dollar value, and note "zero instances in prior 12 months."
**Documentation in Notes Field** — Hospitals must add clarifying notes in the MRF to indicate whether there were zero, one, or more instances in the prior 12 months when reporting estimated allowed amounts.
## Ongoing Challenges in Hospital Pricing Data
Despite improved formatting and regulatory oversight, several challenges persist that got in the way of hospital pricing data standardization. These are the three hurdles to overcome.
### 1. Payer Name and Plan Name Inconsistencies
Different hospitals refer to the same payer in multiple ways like:
- UnitedHealthcare might appear as "UHC," "United," or "UnitedHealthcare."
- Cigna could also be listed as "LifeSource" for transplant networks.
- Blue Cross Blue Shield (BCBS) variations make it difficult to determine the exact plan.
### 2. Billing Code Variability
- Some hospitals use a single billing code for a procedure (e.g., one CPT code for a colonoscopy).
- Others list multiple billing codes, each with slightly different pricing structures.
- There's no uniform approach, making it hard to determine whether differences stem from contract negotiation, billing methodology, or simple misclassification.
### 3. Slow Adoption of New Compliance Measures
- Hospitals are only required to update MRFs once per year, meaning outdated formats linger.
- Many hospitals are still catching up on compliance, delaying the effectiveness of new regulations.
Despite regulatory improvements, compliance remains a major issue. [According to a report from the Office of Inspector General (OIG)](https://oig.hhs.gov/reports/all/2024/not-all-selected-hospitals-complied-with-the-hospital-price-transparency-rule/), a significant percentage of hospitals have yet to fully comply with the transparency rule. Additionally, [a Patient Rights Advocate report found that compliance among hospitals has actually declined,](https://www.healthcaredive.com/news/hospital-price-transparency-continues-drop-patient-rights-advocate/733703/) highlighting ongoing inconsistencies in hospital-reported pricing.
## How Payerset Solves These Challenges
We take a human-first approach to organizing, simplifying, and making hospital price transparency data more actionable and accessible.
**1. Payer Name & Plan Name Normalization** — We standardize payer names and plan names so that they are consistent across all hospital files. This allows for easy comparison across hospitals and alignment with payer-posted data.
**2. Billing Code Categorization** — We map hospital billing codes to categorized, easy-to-understand procedure groups. This helps remove noise from the data and ensures proper comparability across providers.
**3. Compliance Tracking & Continuous Monitoring** — We track hospitals' compliance with regulations and update our datasets accordingly. As hospitals gradually align with the new standards, we incorporate their latest data for accuracy.
**4. Seamless Integration with Payer Data** — By linking hospital-posted data with Transparency in Coverage payer data, we provide a holistic view of healthcare pricing. This allows better benchmarking and removal of outliers to enhance pricing insights.
Hospital price transparency is improving, but significant challenges remain. Payerset makes it easy to work with hospital and payer data, eliminating inconsistencies and enabling smarter analysis.
---
## How Payerset Simplifies Healthcare Plan Complexity in Reporting
- URL: https://www.payerset.com/post/how-payerset-simplifies-healthcare-plan-complexity-in-reporting
- Published: February 21, 2025
- Author: Jacob Little
- Section: Industry Insights
## The Complexity of Reporting Plans & Why It Matters
When it comes to healthcare price transparency, one of the biggest challenges users face is navigating reporting plans—the structured data sets that insurance carriers use to define pricing. These plans represent different insurance products, such as PPOs, HMOs, and EPOs, but they go far beyond the broad categories that most people recognize.
Each insurance carrier structures their pricing data differently, and reporting plans are fragmented into multiple categories, including examples like:
- **Standard commercial plans** – The widely recognized plans available through insurers.
- **Exchange plans** – Those available on the Affordable Care Act marketplace.
- **Employer-sponsored plans** – Customized plans that large companies negotiate, often with unique pricing structures.
## Challenges Accessing Insights Today
Many price transparency platforms require users to select these reporting plan(s) before they can access a subset of pricing data. While this might seem like a logical way to organize information, it actually creates unnecessary complexity and friction for users. Why?
- Reporting plan names are often obscure, highly technical, and inconsistent.
- Selecting the wrong plan can exclude critical data and lead to inaccurate comparisons.
- Depth of the analysis is inherently limited with this reduced scope.
Forcing users to choose upfront process is time-consuming, fragmented, and often leads to incomplete or misleading comparisons. Not to mention, maybe you want to see the variability across common categories of plans for each payer but have no way of doing that without immense manual intervention.
## Payerset's Approach: Complete Flexibility & Data Accuracy
Payerset takes a different approach. Instead of forcing users into predefined selections, we maximize flexibility by automatically collecting, categorizing, and mapping all available reporting plan data.
Our process ensures that:
- We collect and process every file from every carrier—nothing gets left out.
- We automatically categorize and map plans to recognizable names and categories to eliminate confusion.
- Users can compare rates across multiple payers without needing to pre-select a plan.
This means that when users search for a procedure like knee surgery, they instantly see all relevant pricing data across all applicable reporting plans, organized coherently for easy analysis — without having to make complex selections first.
## How does this look in the real-world?
Imagine you're a hospital administrator evaluating reimbursement rates for a procedure. With traditional platforms, you might have to dig through multiple files, selecting different plans manually, just to piece together a full comparison.
With Payerset, the process is seamless:
1. Search for the procedure(s), provider(s), and Payer(s)
2. Instantly view rates across all relevant plans, automatically categorized and mapped for trusted analysis—no need to limit scope upfront.
3. Add additional ad-hoc descriptive details such as billing code modifiers, place of service, TIN value, and much more within the same view to enrich your analysis.
## Holistic View of Reporting Plan
This approach ensures you never miss critical pricing insights while making analysis faster, easier, and more accurate.
## The Bottom Line: Holistic Analysis Leads to Better Outcomes
Payerset's mission is to make healthcare price transparency simple, accurate, and actionable. By eliminating the burden of pre-selecting reporting plans, we provide users with a more intuitive, comprehensive, and insightful experience.
With more accuracy, less frustration, and better decision-making, Payerset is the ultimate tool for anyone looking to analyze healthcare costs effectively.
Experience the easiest way to compare healthcare pricing—Try Payerset today.
---
## Payerset and PurpleLab Partner to Deliver Trusted Price & Coverage Intelligence, Transforming Healthcare Decision-Making
- URL: https://www.payerset.com/post/payerset-and-purplelab-partner-to-deliver-trusted-price-coverage-intelligence-transforming-health
- Published: February 13, 2025
- Author: Joseph Tollison
- Section: News
Payerset, the industry leader in enterprise price transparency data, and PurpleLab, a leading health tech company specializing in real-world data and medical claims insights, announced today a strategic partnership to launch a trusted price intelligence solution suite, setting a new standard in healthcare intelligence. This collaboration empowers healthcare organizations with unparalleled accuracy and actionable insights, enabling smarter, data-driven decisions that drive better outcomes.
PurpleLab, dedicated to advancing healthcare pricing transparency, also integrated Lime Tree Health's comprehensive hospital price transparency data into its platform earlier this year. By merging these powerful datasets from Lime Tree Health and Payerset with one of the US's largest medical and pharmaceutical claims databases, PurpleLab now delivers an unmatched, 360-degree view of the healthcare payer and provider landscape.
The trusted price intelligence solution will provide a holistic view of healthcare prices across providers, payers, and geographies. Health organizations will be able to determine the true cost of any service, from any provider, in any location, backed by validated data. This partnership will deliver critical insights into payer coverage policies, helping stakeholders understand their impact on patient access and affordability. Additionally, it offers actionable intelligence by uncovering historical pricing trends, allowing to predict future shifts, and enabling proactive cost management.
By providing easily accessible price transparency, the solution suite will be invaluable for:
- **Health Systems:** Improve revenue cycle management by informing contract and negotiation strategies, refining financial forecasting, budgeting, and resource allocation.
- **Payers:** Refine payment models, develop competitive benefits packages, and negotiate fair reimbursement rates.
- **Pharmaceutical Companies:** Inform market access strategies, support value-based pricing models, and demonstrate the economic value of their products.
- **Clinical Care Organizations:** Make informed treatment decisions, guide patients toward cost-effective care options, and improve care coordination.
"This partnership marks a significant milestone in our mission to deliver actionable healthcare intelligence," said Mark Brosso, CEO of PurpleLab. "By integrating Lime Tree Health's pioneering hospital pricing data into our platform alongside Payerset's industry-leading price transparency capabilities, we're setting the stage to make pricing intelligence accessible and actionable for everyone in healthcare."
Since 2018, PurpleLab has rapidly become the trusted partner for comprehensive healthcare data and analytics. Their [HealthNexus](https://purplelab.com/platform/) platform aggregates over 50 billion medical and pharmaceutical claims from a diverse network of over 20 sources. This massive dataset, encompassing 330+ million patient lives and 2.3 million healthcare providers, delivers unparalleled insights into patient journeys, treatment patterns, and clinical outcomes, enabling data-driven decisions that improve healthcare delivery.
"We see this partnership as an important milestone in reshaping healthcare pricing," said Jacob Little, CCO at Payerset. "Our goal is to deliver the transparency that creates fairness across the entire healthcare ecosystem, and teaming up with PurpleLab will help us achieve that vision."
Payerset offers insights that transform how health systems, software vendors, consulting firms, and other stakeholders understand and manage healthcare pricing. Their proprietary technology continually processes and stores every machine-readable file (MRF) posted by insurance companies, amassing over 10 trillion records of contracted rates per quarter. This comprehensive and constantly updated dataset offers a historical and current view of pricing trends, enabling in-depth analysis of cost variations and contract performance.
---
## Unlocking the Power of Historical Price Transparency Data
- URL: https://www.payerset.com/post/unlocking-the-power-of-historical-price-transparency-data
- Published: January 23, 2025
- Author: Jerry DiMaso
- Section: Industry Insights
## Overview of Use Cases for Historical Price Transparency
Accessing and understanding historical price transparency data has always been a challenge. The sheer volume and complexity make it difficult to track trends, identify patterns, and detect anomalies over time. This often leads to decision-making without the full picture. Customers wrestle with this by manually piecing together old statements, email threads, and scattered public data—an inefficient, time-consuming process. Worse, these methods often were forced to rely on incomplete or inaccurate information.
To solve this, we're introducing the Date Snapshot feature. This new tool simplifies historical rate analysis, giving users access to past pricing data to navigate healthcare costs with greater confidence. Users can select specific timeframes when creating fee schedules or rate comparisons.
## How Can Historical Price Transparency Data Improve Outcomes?
**Leverage Historical Rate Data** — Access to past negotiated rates helps users bring concrete data to the table, strengthening their position in pricing discussions with payers and providers. This eliminates reliance on estimates or assumptions.
**Identify Areas for Rate Reductions** — Analyzing rate changes over time can reveal unnecessary increases or significant price fluctuations, providing leverage to negotiate more favorable terms.
**Benchmark Against Industry Standards** — Comparing current negotiated rates with historical market trends helps organizations determine competitiveness and adjust contracts accordingly.
**Spot Unfavorable Contract Terms** — Users can identify past instances where negotiated rates were unusually high or misaligned with industry norms, ensuring future contracts are more balanced and fair.
## Get Started
Payerset customers already have access to this feature within the Fee Schedule and Rate Comparison tools. And we're just getting started—more enhancements are on the way!
---
## Reflecting on 2024 and What's Next for Payerset
- URL: https://www.payerset.com/post/2024-reflection-look-ahead
- Published: January 14, 2025
- Author: Jerry DiMaso
- Section: Industry Insights
When we started Payerset, our goal was ambitious: making the immensely complex and often inaccessible data surrounding price transparency available to everyone. We're talking trillions of rows of data from every hospital system, payer, and NPI across the country. It's a monumental challenge, but it's also one of the most important ones to solve for the future of healthcare.
Over the past year, we've made incredible strides. We spent many long nights improving the underlying engine to process the massive (and often intentionally over-complicated MRF data) published by payers, launched a new version of the Payerset platform into the world, took part in the HFMA (Healthcare Financial Management Association), National Price Transparency, and the HLTH conferences, and even launched our [Payerset podcast](/insights/categories/payercast)!
Beyond these accomplishments, however, we have learned so much and met so many fantastic people like [Cynthia Fisher](https://www.patientrightsadvocate.org/about-staff), [Dr. Marion Couch](https://www.linkedin.com/in/marion-couch-872ab3a6/), [Mark Brosso](https://www.linkedin.com/in/mark-brosso-73861b5/), [Alex Goolsby](https://www.linkedin.com/in/wgoolsby/), [Ron Urwongse](https://www.linkedin.com/in/rurwongse/), [Aneesh Chopra](https://www.linkedin.com/in/apchopra/), and countless others. Each step deeper into the world of healthcare price transparency has reinforced why we're doing this and how much potential there is to make a difference. The deeper we dig, the more energized we become.
## Bringing Healthcare Pricing Intelligence to Everyone
As we move into 2025, we're continuing to build on making price transparency data accessible to everyone. We're incorporating more data than ever before to deepen our insights and expand our capabilities. However, we don't want to stop at simply serving up the data. We've spent a couple of decades in data & analytics and know in our core that moving from simply being able to access data to intelligently making decisions and driving outcomes is where impact is maximized.
Payerset has been at the forefront of making transparency data accessible. Our vision, however, goes far beyond simply accessibility. To truly maximize our impact on the healthcare landscape, our aim is to empower everyone throughout the value chain with complete Pricing Intelligence. We want data to not only be readily accessible, but easily understandable and actionable.
Next-generation pricing intelligence involves incorporating new data domains (Claims, anyone?), exploring ways to prescribe actions to take in order to improve outcomes, specialized tools for particular use cases, historical data to create a holistic picture, and much more.
## What's Right Around the Corner?
Looking ahead, there's so much we're excited about. One of the first, and most visible ways, we're evolving is with a complete refresh of our platform. We've redesigned the user interface to make it even more intuitive and powerful for our users. But this update isn't just about aesthetics; it's about laying the groundwork for new types of analysis and use cases that will empower our customers to uncover insights they couldn't access before.
You'll also see some more detailed announcements over the following weeks. We've released to our community the ability to view how rates have changed over time to enable a slew of new use cases, incorporating Provider data with an interactive new explorer, enhanced our Rate Comparison explorer, along with much more. We'll also be teasing some upcoming innovations as well.
## Thank You
To our customers, partners, team, and community: thank you for being part of this journey so far. We've accomplished so much together in a short amount of time, and we're only just getting started. Here's to another year of learning, building, and making healthcare pricing a little more transparent for everyone.
---
## Exploring Price Transparency, Accountability, and Innovation in Healthcare with Andrew Gordon
- URL: https://www.payerset.com/post/exploring-price-transparency-accountability-and-innovation-in-healthcare-with-andrew-gordon
- Published: December 5, 2024
- Author: Joseph Tollison
- Section: Payercast
Healthcare price transparency has become a pivotal topic in the industry, and our recent Payercast episode featured Andrew Gordon, a social worker turned health economics researcher, who is helping shape the conversation around this critical issue. Here's a recap of the highlights, key insights, and lessons learned from our dynamic discussion.
> "Transparency unlocks accountability. With better information, we can hold stakeholders accountable for fair pricing and quality care." — Andrew Gordon
## A Journey Fueled by Curiosity
Andrew's path into healthcare research began with personal curiosity. Frustrated by an unclear $700 bill for an MRI, Andrew dove deep into the world of healthcare finance, seeking to understand billing practices, negotiated rates, and the systemic issues that complicate transparency. His commitment led him to engage with schedulers, coders, and billers to unravel the complexities of healthcare pricing. This hands-on approach highlighted both the human side of the system and its technological gaps.
## What is Price Transparency?
Andrew defines price transparency as the patient's ability to know their exact out-of-pocket costs for healthcare services before they receive them. This patient-centric perspective acknowledges the critical need for clarity in a system where bills are often unintelligible and unexpected charges can be financially devastating. While elective procedures lend themselves more easily to transparency, emergency scenarios present unique challenges.
## Barriers to Transparency
During our discussion, Andrew shared insights on why transparency remains a significant challenge:
- **Technological Lag:** Many healthcare systems are burdened by outdated technology and siloed data, making it difficult to deliver accurate estimates.
- **Complex Contracts:** Negotiated rates between payers and providers vary widely due to factors like service volume, site of care, and contractual carve-outs.
- **Operational Overload:** Hospitals face competing priorities, from staffing shortages to EMR implementations, which often push transparency efforts down the list.
- **Public Misconceptions:** Transparency can lead to patient confusion or mistrust, as seen in cases where itemized bills create more questions than answers.
## A Hopeful Shift: Accountability and Alignment
Transparency, as Andrew notes, unlocks accountability. With more information available, stakeholders including patients and employers can advocate for fairer rates and better care. Employers, in particular, are emerging as powerful players in this space, leveraging data to push for direct-to-provider arrangements that cut costs and improve outcomes for employees.
## The Future of Healthcare Pricing
Our conversation touched on the potential for transformative change in the industry:
- **Disruption through Technology:** Innovations like AI and advanced analytics could streamline pricing processes and enhance transparency.
- **Direct-to-Employer Models:** Employers are increasingly bypassing traditional insurance arrangements and partnering directly with providers to reduce costs.
- **Independent Care Models:** Physicians and small organizations are exploring subscription-based care and other models to sidestep insurance complexities.
Andrew's optimism is grounded in the progress being made but tempered by the recognition that systemic change takes time. The key is collaboration across stakeholders and a commitment to aligning incentives in ways that prioritize the patient.
## Personal Development Insights
In addition to his professional expertise, Andrew shared a valuable personal development tip: create environments of psychological safety. Whether in healthcare or any other field, fostering open dialogue and encouraging diverse perspectives can lead to better solutions and stronger relationships.
## Final Thoughts
Andrew Gordon's dedication to understanding and improving the healthcare system is both inspiring and instructive. His ability to connect the dots between research, policy, and practice highlights the importance of curiosity, humility, and collaboration in driving meaningful change.
We are excited about the momentum in healthcare price transparency and grateful to have leaders like Andrew pushing the conversation forward. Stay tuned for more episodes of Payercast as we continue to explore the future of transparency, accountability, and innovation in healthcare.
---
## Reflections from the Georgia HIMSS Conference: Driving Digital Transformation in Healthcare
- URL: https://www.payerset.com/post/reflections-from-the-georgia-himss-conference-driving-digital-transformation-in-healthcare
- Published: October 3, 2024
- Author: Joseph Tollison
- Section: Industry Insights
The annual Georgia HIMSS conference brought together leaders and innovators from across the healthcare landscape, and this year's event did not disappoint. With strategic learning tracks covering cybersecurity, artificial intelligence (AI), and digital transformation, the conference offered practical insights into how healthcare organizations (HCOs) in Georgia can adapt and thrive in an increasingly digital world.
## Insights from the Digital Transformation Track
I had the opportunity to attend the digital transformation (IT) track, which was packed with engaging sessions and interactive panels. We heard from experts representing major Georgia-based healthcare organizations like Grady Health System, Shepherd Center, and the Georgia Hospital Association. One theme stood out: success in digital transformation is not just about implementing technology — it's about empowering people.
### Key Takeaways
**Change Management is Critical:** One of the most impactful discussions centered on the importance of change management, a concept often overshadowed by project management. Valerie emphasized the importance of focusing on the end state and outcomes for people, not just checking boxes on training or processes. It's about ensuring that teams are equipped to succeed, a point echoed by many speakers.
**Business Cases and Leadership Buy-In:** In one of the panels, speakers like Steven McWilliams and Murry Ford discussed how to make strong business cases for technology investments within healthcare organizations. They highlighted that the key to success is inspiring teams and addressing barriers to adoption, whether it's bridging generational gaps in the workforce or making tough decisions during crises like Hurricane Helene.
## Learning from Real-World Challenges
The impact of Hurricane Helene loomed large over many conversations. Several hospitals across the Southeast, including those in Georgia, had to revert to using paper records during the storm. This situation served as a stark reminder of what truly matters in healthcare technology: the people and processes behind it. While technology can streamline operations and improve outcomes, the crisis revealed that when it comes down to it, what really counts is how quickly and effectively teams can adapt, even without the "bells and whistles" of high-tech systems.
**The People Factor:** Murry Ford, now overseeing revenue at Grady, brought a unique perspective as someone who transitioned from an IT role to a business leadership position. His insight into the prioritization of initiatives showed that focusing on outcomes and cross-functional alignment is critical to navigating the complex healthcare environment.
## Balancing Regulatory Compliance and Innovation
Another topic discussed at length was the tension between regulatory compliance and the speed of digital transformation. Industry experts, including Sepi Browning and Jeff Morrison, shared their experiences with improving patient care while navigating the challenges of regulatory requirements. Although these regulations often slow the pace of change, they also serve as a forcing function, aligning teams around a shared goal. As Todd Schlesinger pointed out, once mandates are in place, the ability to execute is clear, it's just a matter of prioritization and leadership.
## Conclusion
The Georgia HIMSS conference was a great reminder of the incredible culture and people who are the connective tissue of healthcare in this state. From interactive discussions on digital transformation to insights on crisis management and compliance, the event highlighted that healthcare's future depends not just on technology but on the people who make it work. As we continue to innovate, we must remember that true transformation happens when we empower our teams, prioritize the patient experience, and execute with purpose.
I'm already looking forward to next year's Georgia HIMSS event. Until then, let's keep building a stronger, more connected healthcare community in Georgia.
---
## Payercast S01 E03 - Price Transparency Use Cases, HFMA 2024, Late Adopters or Sitting Ducks?
- URL: https://www.payerset.com/post/payercast-s01-e03-price-transparency-use-cases-hfma-2024-late-adopters-or-sitting-ducks
- Published: August 26, 2024
- Author: Jacob Little
- Section: Payercast
**Podcast Title:** Key Price Transparency Use Cases, HFMA 2024, Late Adopters or Sitting Ducks?
**Season 1, Episode 3**
## Overview
In this episode of Payercast, the Payerset team explores how health insurance carriers are proactively reducing reimbursements to hospitals by leveraging price transparency MRF files from other carriers. We dive into the core and most successful use cases for price transparency data and share PT success stories from the 2024 HFMA conference. Tune in to learn more about the evolving landscape of healthcare reimbursement and price transparency.
## Featured Topics
### Use Cases for Price Transparency Data in Healthcare
The conversation delved into various use cases for health plan price transparency data, particularly focusing on the contractual rates between providers and carriers. Price transparency is increasingly becoming a valuable tool for healthcare organizations and payers alike, as the industry adapts to using this data more strategically. Below are some key takeaways.
### Bi-Directional Rate Benchmarking and Negotiation
**Key Insight:** One of the most common uses of price transparency data is benchmarking reimbursement rates. Healthcare organizations use this data to understand how their rates compare to similar providers in their area. This allows for more informed negotiations with payers. For instance, a hospital can compare its reimbursement rates by billing code to neighboring facilities with similar demographics, clinicians, and market power, leading to more competitive negotiations.
However, the insurance carriers are also using the data—sometimes in reverse. Hospitals have received letters from large health insurance companies pointing out that the hospital is being reimbursed at a higher rate than competitors and suggesting reductions based on the transparency data. This puts healthcare organizations on the defensive, making it critical for them to be proactive in their use of this information.
**Actionable Insight:** Healthcare organizations must adopt a proactive approach to rate negotiation using this data, or risk being caught off-guard when payers leverage it against them. Understanding how payers are using this data can help prevent significant revenue loss.
### Market Expansion with Price Transparency Data
**Key Insight:** The second significant use case discussed was market expansion. Larger health systems are using transparency data to strategically expand their services beyond their current geographic regions. For example, a hospital in Florida might look to expand into Tennessee, or a multi-specialty practice in one state may seek to acquire a practice in another.
By analyzing publicly available rates, organizations can assess market dynamics and reimbursement rates in regions where they don't yet have a relationship with insurers. This data provides a foundation for understanding what rates are common and how they can position themselves for market entry.
**Actionable Insight:** Market expansion decisions can be more informed and strategic when leveraging this data. Organizations can avoid blindly entering a new market by first gaining visibility into the prevailing rates and reimbursement structures.
### Underpayment Analysis and Claim Filling Gaps
**Key Insight:** Another emerging use case involves using the data as a backstop for revenue cycle management. Underpayment analysis allows health systems to catch gaps in revenue more quickly by automating the detection of discrepancies between expected payments and actual payments received.
**Actionable Insight:** By incorporating price transparency data into their revenue cycle processes, healthcare organizations can improve the speed and accuracy of underpayment detection, enhancing financial performance and reducing the revenue loss associated with claim underpayments.
## Case Study: UofL Health — Leading the Way in Price Transparency Utilization
At the HFMA annual conference, UofL Health in Kentucky was highlighted as an example of a health system successfully leveraging price transparency data. They were able to extract and analyze data from a large payer, Anthem Elevance, and used it in their negotiations. As a result, they secured significant reimbursement increases across 20 DRGs (Diagnosis-Related Groups), which is forecasted to improve their EBITDA by 3-4% over the next year.
**Key Takeaway:** Proactively adopting price transparency data can yield significant financial benefits, as demonstrated by UofL Health. Other healthcare systems can follow suit by collaborating with cross-functional teams and technology departments to analyze data and use it in negotiations.
## Conclusion
The podcast emphasized the importance of price transparency in transforming healthcare organizations' negotiation strategies, market expansion efforts, and revenue cycle management. Proactive organizations are already using this data to increase revenues and strengthen their negotiation positions. However, those who ignore it risk becoming "sitting ducks" when payers come knocking. Now is the time for healthcare systems to embrace price transparency and use it to their advantage.
With the rising use of price transparency data across the industry, both proactive adoption and strategic application are essential. Those who prepare now will be better equipped to handle the new dynamics in healthcare pricing and reimbursement.
---
## Contract Negotiation Powered by Price Transparency Data: A Success Story from UofL Health
- URL: https://www.payerset.com/post/contract-negotiation-powered-by-price-transparency-uoflsuccess
- Published: June 29, 2024
- Author: Jacob Little
- Section: Industry Insights
Our hats off to the UofL team, Abraham Gage and Michael Venable, for being change agents and jumping into payer price transparency data early. They wrote the code, got the servers, and focused on getting this data and turning it into millions in annual savings. This is the vision of price transparency. Rebalancing the asymmetrical nature of healthcare pricing in this country by simply saying, "Hey, you are not giving me the fair rate in these specific areas based on your contract data that you published."
One very important note: while the UofL team impressively parsed the payer price transparency data on their own, parsing and cleaning the data is a continually evolving challenge with the data and payer restrictions changing monthly. If you don't have the time or focus to get this information, you don't have to do it on your own. We provide a super easy-to-use portal where you can simply search any care delivery organization, health insurance carrier, and category of services (or specific billing code) and get all of the rate data you need returned in seconds.
## Key Outcomes from UofL Health's Initiative
Using payer sourced price transparency data, UofL achieved a 33% increase in reimbursement for spine fusion procedures with MCC.
### Significant Increases in Reimbursement Rates
The use of price transparency data allowed UofL Health to secure strategic increases in reimbursement rates for various services. For example, they achieved a 33% increase in reimbursement for spine fusion procedures with MCC (Major Complications or Comorbidities). The overall percentage increase in rates from September 2023 to May 2024 was substantial for several services, indicating a successful negotiation process driven by the insights gained from the transparency data.
### Enhanced Negotiating Position
Access to competitors' pricing data across multiple geographies provided UofL Health with a robust foundation to negotiate better rates. This transparency empowered the health system to avoid accepting lower rates and instead push for rates that reflect the market value of their services.
"The system is doing what the system should be doing, ensuring that the codes that are going to be generated yield a material return," highlighted the strategic advantage gained through detailed market intelligence.
### Strategic Decision-Making
The transparency data informed UofL Health's market entry decisions and initial contract settings. This was particularly evident in their decision to establish a standalone imaging center to retain patients within their system and prevent them from being redirected to lower-cost providers by insurers. The data also helped UofL Health to avoid catastrophic disasters in M&A and joint ventures, ensuring better-informed decisions.
### Validation and Accuracy
Ensuring the accuracy and reliability of the data was a critical step in the process. The team cross-verified the extracted data with internal claims and revenue cycle data, as well as corroborated findings with trusted partners. This meticulous validation process helped avoid pitfalls and ensured that the negotiation strategies were based on accurate and comprehensive data.
"Extracting value from TiC data takes effort and patience," emphasized the need for a dedicated approach to harness the full potential of transparency data.
### Future Expectations
The team anticipates ongoing evolution in payer behavior and additional guidance from CMS. They expect that payers will continue to balance compliance with transparency requirements and protection of shareholder value. The presentation underscores the importance of incorporating transparency data into the broader analytics portfolio to continuously identify and respond to market opportunities and threats.
## Why Payer Transparency Matters
UofL Health's success story underscores the transformative potential of payer transparency data in leveling the playing field in healthcare pricing. By accessing and utilizing this data, healthcare providers can negotiate fairer rates, make informed strategic decisions, and ultimately improve their financial health.
At Payerset, we recognize that not every organization has the resources or expertise to navigate the complexities of payer transparency data. That's why we offer a user-friendly portal that simplifies this process. With our platform, you can quickly and easily access the rate data you need, empowering your contract negotiation.
Regardless of whether you opt for our solution, a competitor's, or decide to tackle it on your own, TAKE ACTION! Given the ease of accessing this data, it is quickly becoming negligent to ignore.
---
## Payercast S01 E02 - Recapping The 2024 National Healthcare Price Transparency Conference
- URL: https://www.payerset.com/post/payercast-s01-e02-recapping-the-2024-national-healthcare-price-transparency-conference
- Published: May 24, 2024
- Author: Joseph Tollison
- Section: Payercast
**Podcast Title:** Recapping The 2024 National Healthcare Price Transparency Conference
**Season 1, Episode 2**
## Overview
The Payerset team was a proud sponsor of the National Healthcare Price Transparency Conference (NHPTC) this year. We learned so much and met so many incredible people that are making a real difference in U.S. healthcare. The conference was praised for its impactful discussions on healthcare financials, engaging expert panels, and the overall event organization. The key themes revolved around the necessity for data transparency, accountability in healthcare pricing, and strategies for employers to manage healthcare costs effectively.
## Featured Topics
### Event Experience
- The conference had a welcoming atmosphere with around 700 total guests, including 200 in-person attendees.
- The event was well-organized with high-quality virtual content available for free.
- Attendees appreciated the opportunity to access all presentation decks and virtual content seamlessly.
### Panel Discussions
Two panels were highlighted: one focused on market strategies and the other on policy and employer responsibility in managing healthcare costs. Experts from various sectors, including employers, providers, and consultants, shared their insights on improving healthcare financials.
## Detailed Overview
### 1. Market Strategies for Data Utilization
**Key Takeaway:** Employers have a fiduciary responsibility to understand and manage healthcare costs, as these expenses significantly impact their bottom line and employees' wages.
**Actionable Insights:**
- Employers must obtain and analyze data to combat issues like double billing and fraud.
- Direct contracting with care delivery organizations is becoming more feasible and can lead to substantial savings.
- Examples: Purdue University saved $25 million over five years by effectively managing healthcare costs.
### Policies to Increase Transparency
- **Price Transparency:** Highlighted the importance of making pricing information accessible to promote competition and accountability.
- **Data Transparency:** Emphasized the need for comprehensive data analysis to understand and improve cost structures.
- **Quality Transparency:** Stressed the importance of correlating quality metrics with pricing to ensure value in healthcare services.
- **Ownership Transparency:** Discussed the complex corporate structures of healthcare organizations and the need for clarity in ownership and financial operations.
- **PE Influence:** Noted that private equity firms' involvement in healthcare often indicates a broken market that is fixable for profit.
### Employer Responsibility and Accountability
- **Fiduciary Duty:** Employers are responsible for ensuring that their healthcare spending is efficient and effective.
- **Negotiation and Cost Management:** Highlighted the importance of negotiating better rates and understanding high-volume procedures to manage costs.
- **Case Study:** A healthcare organization managed to significantly reduce costs by switching providers, showing the impact of informed decision-making.
## Key Speakers and Takeaways
### Chris Whaley (Brown University)
- **Insight:** Proved that healthcare organizations can break even or profit with efficient operations.
- **Analysis:** Showed how rising hospital prices drive overall spending growth, using data from the Bureau of Labor Statistics.
### Marilyn Bartlett (CPA)
- **Ownership Transparency:** Provided insights into the complex corporate structures of healthcare organizations.
- **Call to Action:** Encouraged understanding the true cost of care delivery and improving operational efficiencies.
### Sean Grimminger
- **PE Firms in Healthcare:** Explained that private equity firms enter broken markets for profit, indicating areas for potential improvement.
## Conference Highlights
### Sage Transparency 2.0
A new tool released at the conference, Sage Transparency 2.0, offers comprehensive data on healthcare pricing and quality metrics. The live demo, led by Gloria Sachdev, showcased the tool's capabilities in an interactive format.
### Legislative Support
Noted the bipartisan support for healthcare price transparency, with legislation spearheaded by figures like Senator Braun and Bernie Sanders. Emphasized the importance of continued legislative efforts to improve healthcare transparency and accountability.
## Conclusion
The National Price Transparency Conference provided valuable insights into healthcare financials, emphasizing the importance of data transparency, employer accountability, and strategic management of healthcare costs. With expert panels, practical examples, and new tools like Sage Transparency 2.0, the conference underscored the necessity for informed decision-making in the healthcare sector. The overarching message was clear: transparency drives competition and efficiency, ultimately benefiting both employers and employees in managing healthcare costs.
---
## National Healthcare Price Transparency Conference Recap
- URL: https://www.payerset.com/post/national-healthcare-price-transparency-conference-recap
- Published: May 15, 2024
- Author: Joseph Tollison
- Section: Industry Insights
The National Healthcare Price Transparency Conference, held on May 13, 2024, was a significant event bringing together industry leaders, healthcare professionals, and policymakers to discuss the critical issues of healthcare pricing and transparency. Hosted by prominent figures like Gloria Sachdev, Cynthia Fisher, and Mark Cuban, the conference delved into various facets of healthcare costs, price transparency, and the impact of these elements on employers, employees, and the broader healthcare system.
If you didn't attend, please make sure you do next year. It will be well worth it if you care about price transparency in healthcare. For those who were not able to attend, my aim is to capture the essential points from each session, highlighting key insights and actionable takeaways.
## Cynthia Fisher Keynote
**Topic: Empowering Patients and Employers through Price Transparency**
Cynthia Fisher, founder of PatientRightsAdvocate.org, emphasized the need for accessible, upfront healthcare pricing to create a functional and competitive market. She shared impactful stories, like that of Cindy Reddy from Colorado, who faced financial ruin due to a surprise medical bill, and how transparency helped remedy the situation.
Fisher highlighted the success of states like Colorado and Florida, where transparency laws have led to significant savings for employers and improved healthcare access for employees. She stressed the importance of compliance, noting that only 34.5% of hospitals are fully compliant with transparency regulations. Fisher advocated for stronger enforcement and state-level actions to ensure broader compliance and consumer protection.
**Key Takeaways:**
- Price transparency empowers patients to avoid overcharges and financial hardship.
- Employers can leverage transparent pricing to negotiate better deals and reduce healthcare costs.
- Stronger state laws and enforcement are crucial for achieving comprehensive price transparency.
## Mark Cuban Keynote
**Topic: Revolutionizing Drug Pricing with Transparency**
Mark Cuban, through his initiative Cost Plus Drugs, aims to disrupt the opaque pharmaceutical pricing system. Cuban discussed the inefficiencies and lack of transparency in drug pricing, sharing his company's approach of displaying costs, markups, and final prices openly. He criticized the traditional pharmacy benefit managers (PBMs) and advocated for employers to demand transparency in contracts.
Cuban highlighted the benefits of direct contracting and the importance of employers owning their data to make informed decisions.
**Key Takeaways:**
- Transparency in drug pricing can lead to significant savings and fairer prices.
- Employers should demand transparent contracts from PBMs and insurance companies.
- Direct contracting and owning healthcare data are essential strategies for cost management.
## Chris Whaley - RAND 5.0 Study
**Topic: The Impact of Price Transparency on Healthcare Costs**
Chris Whaley presented the findings from the RAND 5.0 study, which examined hospital prices and the variability across states. The study revealed that employers pay significantly more than Medicare for the same services, with substantial variation not explained by quality or cost differences. Whaley emphasized the fiduciary responsibility of employers to ensure efficient use of healthcare dollars and highlighted lawsuits against companies failing to meet these obligations.
**Key Takeaways:**
- Employers pay, on average, 254% of what Medicare pays for hospital services.
- There is significant price variation across states and hospitals, not linked to quality.
- Employers have a fiduciary obligation to manage healthcare costs effectively.
## Session Highlights
- **Price Transparency Success Stories:** Examples from states like Colorado and Indiana where transparency laws have led to substantial savings and improved healthcare access. Real-life cases of patients avoiding overcharges through access to transparent pricing.
- **Technological Innovations:** Development of tools by tech developers in states with robust transparency laws to facilitate healthcare shopping for consumers. Potential for future apps allowing patients to compare prices and quality, similar to airline ticketing systems.
- **Employer Strategies:** Case studies of companies like Harris Rosen's hotels and school districts in Florida saving millions through direct contracting and transparent pricing. Recommendations for employers to audit healthcare spending and renegotiate contracts based on transparent pricing data.
- **Policy and Enforcement:** Discussion on the slow enforcement of federal transparency laws and the need for state-level actions to protect consumers. Bipartisan support for price transparency laws and the potential for future legislative changes to enhance compliance and enforcement.
## Conclusion
The National Healthcare Price Transparency Conference underscored the critical role of price transparency in transforming the healthcare market. By empowering patients, enabling employers to manage costs effectively, and fostering competition, transparent pricing can lead to significant improvements in healthcare affordability and access. As Payerset continues to democratize price transparency data, these insights and actionable steps from the conference will be invaluable in driving our mission forward.
---
## Payercast S01 E01 - Reviewing Price Transparency Rules and Exploring a Machine Readable File (MRF)
- URL: https://www.payerset.com/post/payercast-s01-e01-reviewing-price-transparency-rules-and-exploring-a-machine-readable-file-mrf
- Published: May 4, 2024
- Author: Joseph Tollison
- Section: Payercast
**Podcast Title:** Reviewing Price Transparency Rules and Exploring a Machine Readable File (MRF)
**Season 1, Episode 1**
## Overview
In Payerset's debut Payercast episode, we provide an in-depth exploration of healthcare price transparency, discussing the mandates for data disclosure, the structure of the data, and the practical applications for using this data effectively.
## Featured Topics
### The Price Transparency Mandate
The discussion begins by outlining the federal mandates that require both hospitals and payers to make their reimbursement rates public. This initiative, spearheaded by CMS along with the Department of Health and Human Services (HHS) and the Department of Treasury, aims to increase transparency in healthcare pricing to enable better decision-making by consumers and other stakeholders.
The mandate covers a wide array of healthcare services and requires that detailed data on negotiated rates be published in an accessible manner. The intent is to make healthcare costs more predictable and to empower consumers to make more informed choices.
### What Does a Machine-Readable File Look Like?
The podcast goes into detail about the structure and content of the machine-readable files (MRFs) that contain the data released under the price transparency rules.
#### Table of Contents
The Table of Contents (TOC) file is discussed as a crucial element of the MRFs. It acts as an index that guides users to various data files related to specific healthcare plans. The TOC lists all the files a payer has published, which contain the negotiated rates and allowed amounts. This file helps users navigate the complex structure of healthcare data, linking directly to the in-depth data files that contain the specific rate information.
#### In-Network File
The podcast then explores what an in-network file looks like, detailing how it contains information about reimbursement rates for services provided within a network. These files are extensive and include details such as provider identifiers, service codes, and the corresponding negotiated rates. The structure of these files is designed to facilitate the extraction of specific data points but requires specialized tools and understanding due to the complexity and volume of the data.
### How Can These Files Be Used?
The practical applications of the data contained within these files are extensively discussed. The hosts explain how analysts and healthcare organizations can use the data to:
- **Analyze Pricing Variations:** By comparing rates across different providers and regions, stakeholders can identify pricing disparities and opportunities for cost reduction.
- **Strategic Planning:** Organizations can use detailed rate information for better contract negotiations and to optimize their service offerings based on competitive pricing.
- **Compliance and Reporting:** Ensuring that pricing strategies comply with regulatory requirements and using the data for mandatory reporting purposes.
- **Market Analysis:** Companies can perform detailed market analyses to understand the competitive landscape, helping them make informed decisions about where to allocate resources.
The podcast emphasizes the transformative potential of price transparency in healthcare, predicting that increased access to pricing data will lead to more competitive pricing structures and potentially lower healthcare costs overall. The detailed walkthrough of data files and discussion on their practical uses provide a comprehensive guide for stakeholders looking to leverage this new wave of data transparency in the healthcare industry.
---
## Price Transparency Q1 2024 Update: Strides Forward and the Road Ahead
- URL: https://www.payerset.com/post/price-transparency-q1-2024-update-strides-forward-and-the-road-ahead
- Published: April 7, 2024
- Author: Thomas Miller
- Section: Industry Insights
As the first quarter of 2024 unfolds, we find ourselves navigating through an evolving landscape of price transparency. The commitment to enhancing the quality and availability of healthcare price information has yielded notable progress, alongside continued challenges. Here's a snapshot of where we stand:
## Enhanced Data Quality and Compliance
The effort to publish valid, accurate data has seen considerable advancements. More payers are now delivering on their obligation to provide clear and comprehensive price transparency data. This improvement, however, is not uniform—while many are making genuine strides, a handful remain behind. Our observations suggest these discrepancies are less about reluctance and more about the technical and operational challenges involved. It's a journey that requires time, investment, and skill development, emphasizing the need for continued support and resources in this arena.
## Innovative Use Cases Emerging
Our clients are at the forefront of translating this wealth of data into actionable insights. The ingenuity on display is remarkable, ranging from integrating payer data with Medicare information and claims volumes to better target negotiation efforts, to constructing detailed fee schedules for hospitals as a starting point for the new MRF requirements. These applications are opening new avenues for strategic decision-making and growth for institutional and professional healthcare organizations alike.
## Looking Forward
The journey towards full price transparency is ongoing, marked by significant achievements and persistent challenges. The developments in early 2024 underscore a collective commitment to this goal, alongside a recognition of the hurdles that still lie ahead. As we continue to navigate these waters, the promise of price transparency to transform healthcare remains clear and compelling.
We remain dedicated to pushing the boundaries of what's possible with price transparency data, supporting our clients in leveraging this information to drive strategic growth and operational excellence.
Stay tuned for more updates as we continue to advance this important work.
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## Navigating the Landscape of Oncology Diagnostic Pricing in the U.S. Northeast
- URL: https://www.payerset.com/post/navigating-the-landscape-of-oncology-diagnostic-pricing-in-the-u-s-northeast
- Published: April 4, 2024
- Author: Joseph Tollison
- Section: Industry Insights
The cost of cancer treatment in the United States is a pressing concern for patients, healthcare providers, and insurance companies alike. With the mandate from CMS (Centers for Medicare & Medicaid Services) on health plan price transparency, a new era of data availability promises a window into the previously opaque world of healthcare pricing. This post delves into an insightful study that uses the vast datasets provided by Payerset to shed light on the price variation for oncology diagnostics in the Northeastern U.S.
## The Critical Role of Cancer Diagnostics
Cancer diagnostics play a pivotal role in the early detection, accurate diagnosis, and the formulation of personalized treatment plans. Technologies like mRNA diagnostics have revolutionized the way treatments are selected, offering hope for more effective and less invasive treatments. However, the cost of these diagnostic procedures can vary significantly, creating a labyrinth of pricing that patients and providers must navigate.
## The Study: A Deep Dive into Oncology Diagnostic Pricing
Leveraging the "Cancer Diagnostics Negotiated Rates – Payerset Price Transparency" dataset, this study analyzes the contracted rates posted by major health insurers, focusing on providers specializing in oncology in the Northeast United States. The study zeroes in on the billing codes for key oncology diagnostics, including breast and prostate mRNA and gynecologic live tumor cell culture diagnostics.
## Conclusion: Empowering Providers with Price Transparency
This study exemplifies how health plan price transparency data can be a powerful tool for healthcare providers. By understanding the landscape of negotiated rates, providers can enter negotiations armed with data, aiming to secure fair reimbursement rates that reflect the value of their services.
As the healthcare industry continues to grapple with cost containment and value-based care, initiatives like this study not only illuminate the path towards more equitable pricing but also underscore the importance of transparency in fostering a sustainable healthcare ecosystem.
---
## Locations for Health Plan Transparency in Coverage Machine Readable Files Now Publicly Documented
- URL: https://www.payerset.com/post/locations-for-health-plan-transparency-in-coverage-machine-readable-files-now-publicly-documented
- Published: January 28, 2024
- Author: Joseph Tollison
- Section: Parsing Payer MRFs
Due to the Transparency in Coverage rules for health plans that began in July 2022, health insurance companies have been required to post their In-Network Rates for all covered items and services between the health plan and in-network providers. This includes both individual providers and organizations.
Payerset has addressed numerous challenges in parsing these files. We've removed duplications, filtered out irrelevant services (since payers list rates for a service and provider even if the provider would never perform that service), and selected relevant health plans, thereby reducing noise and size, and making the data more accessible and insightful. An example of such an irrelevant service could be orthopedic surgery rates listed for a drama therapist.
Another significant challenge, and the focus of this post, is the difficulty in locating the Machine Readable Files (MRFs) themselves. Often, they were not easily found or necessarily linked on the insurance companies' websites. To address this, we are now documenting the location of all MRFs in our public documentation at [docs.payerset.com](http://docs.payerset.com).
Our goal is to enhance transparency compliance, as some health insurance companies have been remiss in posting their MRFs. Additionally, we hope this will serve as a valuable public resource for anyone interested in health plan price transparency.
For regular updates and to access this vital information, check out our documentation with MRF locations.
---
## Uncovering the Future of Price Transparency: A 2023 Recap and 2024 Outlook
- URL: https://www.payerset.com/post/uncovering-the-future-of-price-transparency-a-2023-recap-and-2024-outlook
- Published: January 6, 2024
- Author: Joseph Tollison
- Section: Industry Insights
## Price Transparency 2023 Recap
As we step into 2024, it marks the third year of public health plan price transparency data being available. Reflecting on the past year, I would like to share some price transparency observations and learnings that we've gathered in 2023.
## Catalyzing Strategic Growth
The year 2023 stood out as a pivotal moment for major health systems, including Northwell Health and Prisma Health, in utilizing Payerset's solutions for price transparency. The engagement of these health systems in using this data marks a significant market trend, especially considering that the early adopters of price transparency were primarily health tech, healthcare consulting, and actuarial firms.
We haven't reached widespread democratization of price transparency yet, but there's evident progress in the right direction. Our customers are beginning to realize tangible benefits from the newfound financial clarity they have across markets and their peers.
The data we provide has been instrumental for these organizations, supporting effective negotiations and strategic planning. Our solutions have been critical, whether it's in the context of payer negotiations, managing health systems, or offering consulting services. They have played a key role in fostering revenue growth and informed strategic decision-making.
## Significant Progress in Compliance and Data Accuracy
The journey through 2023 was critical for achieving compliance in health plan price transparency. Our rigorous audit of every Machine-Readable File (MRF) from each payer revealed significant advancements:
- **UnitedHealthcare's Full Compliance:** Achieved in August 2023, UnitedHealthcare addressed earlier discrepancies in their rate structures, providing clarity on actual rates.
- **Improved Accessibility and Reliability:** The year saw a substantial improvement in the accessibility and reliability of large payers' MRF data. Issues like corrupted or inaccessible files, prevalent early in the year, were effectively resolved.
- **Expanding Data Sets:** A testament to data completeness and continued growth is Aetna's files, which increased from 5,038 in August to 5,395 by November. This expansion reflects a richer repository of providers, rates, and codes.
The highlight of the year was our ability to accurately link active contracted rates to the penny for major health systems in states like New York, Tennessee, Texas, Kentucky, Utah, and Indiana. This level of precision challenges the misconception that price transparency data is either unwieldy or inaccurate.
## 2024: A Year of Practical Application and Expansion
The focus for 2024 is set to shift towards the practical application of this data. We anticipate innovative use cases emerging as early adopters integrate this valuable data into their operational workflows. Moreover, we expect a broader spectrum of organizations to begin leveraging this data, driving their strategic plans.
The road ahead is filled with opportunities, and we at Payerset remain committed to our journey of democratizing price transparency data in healthcare.
Looking forward to an impactful and transformative year!
---
## Peeling Back the Layers of Health Plan Price Transparency: A Journey Towards Democratized Data
- URL: https://www.payerset.com/post/peeling-back-the-layers-of-health-plan-price-transparency-a-journey-towards-democratized-data
- Published: November 20, 2023
- Author: Joseph Tollison
- Section: Industry Insights
Today, I'm sharing my thoughts on price transparency in healthcare, a topic that holds promise for making healthcare more affordable. The idea of making things open and clear to spread power and increase wealth isn't new. It's seen in how personal computers came about, and in the foundation of capitalist and democratic societies.
I speak for our team at Payerset, who work with price transparency data daily, and from what we see, it's going to be a long ride. But, there's hope, as we're already seeing some positive changes on the horizon.
When you first hear about price transparency data, it might sound like a way to peek into the secret deals on rates between payers and healthcare providers. And yes, the rates for the same services often vary a lot. But as we dig into this data, we find there's much more to be uncovered, things we didn't think of at the start of this journey to open up this information.
**One big eye-opener is the messiness and complexity of the contracts themselves.** Our dive into the data shows that through messy deals or company mergers, there's no simple relationship between an organization (like Baylor Scott and White) and their rates for services. Usually, it's more like a puzzle. One organization, under one tax ID, with many provider IDs tied to that tax ID, and then multiple contracts tied to various provider IDs (contracts and rates might even be tied to small or unexpected facilities). And then, you need to piece it together because different billing codes are tied to different provider IDs. The mix of different negotiated rate types tied to facilities where you wouldn't expect them adds to the complexity.
On top of this, we still see issues with payers meeting the rules. Just last week, we found a big payer missing major provider IDs in their data, flagged what was wrong, and opened a ticket with CMS. The customers we work with, who have been part of contract negotiations for years, confirm that **this messy picture actually reflects the real situation out there.**
On the outpatient clinic side, it's interesting to see rates tied to individual providers. In one case, a single tax ID had 436 providers tied to it. What our customers might find is that this data is just duplicated over and over. We see many examples of "rate stuffing" where a payer will just copy the same rates under the provider IDs for the same tax ID. This is a way for the payer to follow the law, while making the data so big that it becomes almost impossible to work through and analyze (unless you are Payerset, of course).
Seeing the complexity of these contracts in the industry has been a big part of understanding price transparency. Some organizations have everything under one contract while others have many contracts tied to different provider IDs.
All this confusion is going to come into the light. What's important now is getting this information to people who understand contracts well, actuaries who understand rates well, and organizations like accounting firms and other healthcare consultants who want to use this information to help their clients.
Like I said, it's going to be a long journey, but we're already seeing early movers and healthcare experts holding payers accountable for these messy and inaccurate contracts, and the big variations in service rates. Change is on the way!
It's going to take a lot of hard work. But Payerset will open up this information and get it into as many hands as possible so that they can do good with it. Thank you all for your partnership and dedication to making healthcare better. Thank you for teaming up with us on this journey.
Payerset is price transparency, simplified.
---
## November 2023: A Landmark Month in Health Plan Price Transparency
- URL: https://www.payerset.com/post/november-2023-a-landmark-month-in-health-plan-price-transparency
- Published: November 18, 2023
- Author: Joseph Tollison
- Section: Parsing Payer MRFs
November brought fascinating developments in the world of health plan price transparency.
## Unprecedented Growth in Data
This month, we witnessed more data than we've ever seen before. This wasn't just a marginal increase; we're talking about huge growth. The surge was due to improved compliance, leading to an increase of codes, plans, and an accumulation of hundreds of billions more rows of data.
## Notable Highlights: Elevance and United
A significant highlight was Elevance, a payer that has historically struggled with hundreds of corrupted Machine-Readable Files (MRFs). Impressively, they have now achieved a record of zero corrupted files. Meanwhile, United reported an increase of 200 billion rows in their data.
## Challenges Beyond the Surface
However, this growth spurt in data brings to light a concerning aspect. The essence of price transparency was envisioned to simplify understanding healthcare costs. Yet, the reality is quite the opposite. Organizations that didn't invest heavily in R&D, teams, and infrastructure a year ago to parse this complex and massive data have now officially fallen behind.
## Elevance and United: A Closer Look
As we mentioned before, every single file from Elevance was parsable without any signs of corruption, encompassing 10,105 distinct MRFs. Some individual Elevance files, even when compressed parquet, reached sizes as large as 150 GBs for a 4-field file. To put this in perspective, one such file can contain up to 30 billion rows from just one provider MRF.
On the other hand, United had only six files that were unparsable. Two were listed but unavailable, and the other four suffered corruption due to JSON errors, which were rectifiable through our processes.
## Our Mission and Commitment
Our proprietary Payer Parse technology easily handles these evolving challenges, including changes in schema and the significant increases in data volume.
At Payerset, our mission is to democratize price transparency data. We are committed to continually enhancing our data parsing and storage processes. This commitment is not just about technological advancement; it's about making this data more accessible and affordable. We aim to lower costs and extend these savings to our customers.
Let's harness the potential of this data for good.
Until our next parsing adventure!
---
## A Turning Tide: How Major Payers are Stepping Up Their Data Game
- URL: https://www.payerset.com/post/a-turning-tide-how-major-payers-are-stepping-up-their-data-game
- Published: November 13, 2023
- Author: Thomas Miller
- Section: Parsing Payer MRFs
As we've previously explored on this blog, the landscape of healthcare price transparency is far from uniform. While some payers are more like fortresses with moats and firewalls, others are making strides in becoming veritable libraries of information. Today, we're shining a spotlight on a few industry leaders who are making life easier for everyone in the ecosystem.
## Aetna: The Old Guard Improves Yet Again
Aetna has long been at the forefront of providing clean, accessible data. While their files are undeniably massive, they are providing everything required of them by CMS right on their monthly cadence as expected - truly a model in the price transparency space!
## United: A Comeback Story
We've got to give credit where credit is due. United has made major progress in ensuring their data is both accessible and parseable. Gone are the days of blocked drives and firewall gymnastics. Today, their JSON files are (mostly) clean and accessible, meeting industry standards and making life that much easier for data engineers and analysts alike.
## Elevance: Growing Pains But Getting There
Elevance (formerly Anthem) has been improving their data accessibility and cleanliness, though of the major payers, they are still laggards in terms of ensuring file availability. Recently, they upped the ante by dropping about 3 dozen files that are over 1 terabyte in size—each with more than 20 billion records. To be honest, it threw us for a loop initially, requiring a revamp of our parsing process and some additional compute/memory, but we managed to make it work with just about 30 extra hours of parsing time.
## Size Matters, but So Does Quality
What we're seeing across the board is a trend toward not just more data, but better data. While the volumes are shooting up—requiring some extra computational muscle—so is the quality of the information. This isn't just a win for us at Payerset, but for businesses and consumers who can make more informed decisions faster.
## How Payerset Makes It Easier
If this talk of terabytes and JSON files sounds intimidating, fret not. Payerset is designed to help you operationalize this ever-growing wealth of data swiftly and efficiently. Our technology leverages cloud computing and cutting-edge parsing algorithms to ensure you're never left in the data dust.
The trend is clear: healthcare data is becoming more voluminous, but also cleaner and more useful than ever before. It's an exciting time for anyone invested in the democratization of price transparency data.
---
## Price Transparency Data: A Game-Changer for Health-Tech
- URL: https://www.payerset.com/post/price-transparency-data-a-game-changer-for-health-tech
- Published: November 6, 2023
- Author: Thomas Miller
- Section: Industry Insights
In an industry that's notoriously opaque about pricing, the growing trend towards price transparency data is nothing short of revolutionary. But what does this mean for health-tech companies aiming to empower consumers? Well, a lot actually.
## The Problem: Default Rates and Hidden Costs
Let's start with a common issue faced by consumers—getting a hospital bill that seems disproportionately high for a procedure that wasn't covered by insurance. Hospitals have a default rate for services, and when insurance doesn't kick in, they bill you at this often-inflated rate. It's like paying the sticker price for a new car without any of the dealer incentives or discounts.
## The Power of Data
Here's where price transparency data comes in. By having access to a detailed list of what insurance companies actually pay for procedures, health-tech companies can help consumers negotiate these bills down to a fair rate. This isn't just theoretical; we're seeing this happen in real-time. One company we've been in talks with (whose name must remain confidential for now) is actively using this data to help consumers reduce their hospital bills.
## How It Works
Imagine you receive a bill for a procedure that wasn't covered by your insurance. The hospital says you owe them $5,000. With price transparency data, you can see that the negotiated rate between the hospital and insurance companies for that same procedure is typically around $1,500. Armed with this information, debt-relief health-tech companies can help consumers negotiate the bill down to this fair rate.
## The Role of Payerset
Here at Payerset, we're making it easier for health-tech companies to tap into this valuable data source. Our platform provides an efficient way to operationalize these large data sets, enabling quicker insights and facilitating direct consumer benefits.
## The Bigger Picture
Reducing individual medical bills is just the tip of the iceberg. The applications of price transparency data extend to various facets of healthcare, including identifying the most cost-effective providers for different procedures, creating more personalized insurance plans, and much more.
As we see it, we're in the early stages of a significant shift towards more transparent and equitable healthcare. And for health-tech companies looking to make a meaningful impact, the opportunities are endless.
---
## From Data to Decisions: Payerset's Path in Demystifying Price Transparency
- URL: https://www.payerset.com/post/from-data-to-decisions-payerset-s-path-in-demystifying-price-transparency
- Published: October 24, 2023
- Author: Joseph Tollison
- Section: Industry Insights
There is a plethora of use cases for price transparency data, and we are merely at the beginning of discovering them all. The diverse perspectives people have when examining this data truly excites me, showcasing a landscape rich with possibilities. The process begins with Step 1: the creation of technology capable of processing and storing all this data swiftly and affordably. Step 2 follows: maintaining a historical record of the industries' prices. We at Payerset have proudly achieved both steps, and now the stage is set to make a substantial impact and demystify price transparency.
Here's a glimpse of the feedback and insights we've gathered from our customers and industry conversations regarding the potential benefits of this data.
## Hospital Systems
There's immense potential here to effect change rapidly, although the adoption rate might lag. When discussing rate variations, it's critical to also address the varying capabilities of financial leaders within health systems. We've encountered contracting teams well-versed in leveraging this data, adept at holding firm on their rates while making strategic adjustments during payer negotiations. This finesse of balancing potential savings of millions with adjustments in high volume or high-cost services, without becoming a target for payers, is akin to an expert ship captain steering a massive vessel with precision.
Conversely, some financial leaders find themselves overwhelmed, akin to a deer caught in headlights, unsure of how to utilize this data. The trailblazers and change agents in this sphere will undoubtedly lay down the path for others to follow, although I ardently wish the pace of this transformation could be accelerated. The prevailing attitudes of fear, risk-aversion, and adherence to the status quo are significant roadblocks to addressing healthcare costs.
## Benefits Navigation Companies
These orgs view the data through a distinct lens, developing workflows and automation to enhance provider recommendations and streamline the care journey for their members. With a solid grasp of their member's reporting plans, they aim to navigate rates based on the specific services needed, considering factors like location. This approach starkly contrasts with the rate benchmarking seen in hospital systems.
## Payers
They are keen on leveraging this data to proactively engage in discussions with provider organizations and to gain a competitive advantage in RFP scenarios. It's worth noting that major payers have had access to this data or proxy data, which has been their competitive edge for years.
## Specialty Clinics
Much like Health Systems, they employ this data to negotiate better rates. Additionally, they explore this data for market expansion, presenting an intriguing use case.
---
The insights from these use cases are continually refining our products and services. We are extremely grateful and excited to be on this journey with our customers. Your partnership is invaluable to us. Our mission is to democratize this data, making it accessible to those who can drive positive change with it.
Payerset is price transparency, simplified.
---
## The Parse Oscar Goes To...
- URL: https://www.payerset.com/post/the-oscar-goes-to
- Published: October 23, 2023
- Author: Thomas Miller
- Section: Parsing Payer MRFs
It was a hard-fought battle with many worthy companies working their hardest to publish their massive negotiated rates data out to the world so they could be in compliance with [Price Transparency laws](https://www.cms.gov/healthplan-price-transparency) by last July. Getting this data together, cleaning it up at scale, and publishing it to the world was no small task, especially when one considers the lengths these companies went to tread the line between compliance and obscurity so that the data was technically available, but virtually unusable.
And we get it - if we had to open source our Payerset algorithms, which contain the intellectual property we have built to parse this data, we would probably make it pretty tough to use too. After all, that IP represents thousands of hours of time and many, many dollars invested into our platform to make it what it is today.
Still, some of the techniques used by these companies like Elevance, Aetna, United Healthcare, are nothing short of impressive. And we thought that deserved some recognition. So without further ado...
## For Largest Individual Files, the Award Goes To...
**Elevance!** Wow, did you have some big files, especially those Provider References and their many permutations, *chef's kiss*. Congratulations on all of your hard work to get your huge data out there!
## For Longest Time to Parse, Award Goes To...
**Aetna!** It was neck-and-neck with United up until the last few hours, but you edged them out in the end with an impressive 204 hours of total parsing time. Way to go!
## For Overall Biggest PITA, the Award Goes To...
**Humana** - you know what you did, and you should be proud. Your files required the most creative solution to scale parsing your nearly 11 million individual files. Thank goodness for serverless compute! You even managed to pop a random giant Cigna file into your table of contents for some reason, so extra points for that!
## Honorable Mentions
Of course, some companies didn't win an award, but we still want to mention payers like Cigna, all the regional Blue Cross payers, and United Healthcare, who came so close on all of the categories. Remember, there's always next month!
Well done everyone, congratulations!
We look forward to the new challenges we will see now that we have published this blog post and informed these companies that they should change their strategy.
---
## And Then There Was Humana...
- URL: https://www.payerset.com/post/and-then-there-was-humana
- Published: May 27, 2023
- Author: Joseph Tollison
- Section: Parsing Payer MRFs
As the quest for price transparency in the healthcare industry continues, as we've discussed before on the blog, there are some insurance companies that fall short in providing open access to their price transparency data. A notable case is Humana, a giant in the health insurance sector. Humana presents its own unique set of challenges when it comes to accessing its price transparency files, hindering efforts for data extraction and analysis. In this blog post, we will delve into the intricate hurdles Humana sets up, thwarting web scraping technologies, and consequently, obstructing data accessibility for consumers and other stakeholders.
## 12... Million... Files
Yep, that's right. Humana has provided its price transparency data in the form of nearly 12 million files that can be downloaded from its website. At first glance, this may not seem like that big of a deal, after all, can't computers process millions of files really quickly?
Well, in some cases yes, but in most cases, especially when dealing with even relatively small JSON files, the challenges to parsing that data file up fast. Consider each file taking 2 minutes to process and store. That's 12,000,000 minutes = 400,000 hours -- that's over 44 years. Not super convenient or helpful, in my opinion.
Now of course, computers can process more than one file at a time, but there are conditions that have to be met for that to happen. Without getting too much into the weeds of RAM and compute requirements, each compressed file is ~5 megabytes, uncompressed they range between 30 and 50MB. Most modern PCs have at least 8GB of RAM, so that would equate to processing ~200 files at a time if you were doing nothing else with the computer. Great, we're down to 2,000 hours, or 83 days. **But the files are updated monthly, so by the time you finish, you're already 2 months behind!**
This is where we start getting into needing large amounts of RAM and compute to process these files in a reasonable amount of time. Cloud to the rescue! Amazon, Microsoft, and Google (among others like Vultr and Liquid Web) rent high-powered computers by the hour, so we can effectively use these computers to process our code. This gets expensive quickly - the servers that Payerset uses to process the Humana data cost over $7/hour and it still takes a few days running continuously, so it's not cheap.
But processing power isn't the only complication...
## Yet Another Web Scraping Challenge
### API Rate Limiting
Humana utilizes rate limiting on their developer API, a technique that has many valid reasons for existing, not the least of which is to prevent attacks on systems, but one that also slows down web scraping ventures. This means restricting the number of requests a user can send within a defined period, thereby slowing the pace of data extraction or even bringing it to a standstill. With nearly 12 million files to parse through, rate limiting significantly inflates the time needed to obtain this vast amount of data, presenting a pretty big challenge.
## The Impact on Data Accessibility
Humana's use of these mechanisms to foil web scraping and data processing efforts has severe implications on data accessibility for consumers and other stakeholders. By creating hurdles to download their price transparency files, **Humana effectively renders this information inaccessible for virtually all users.**
While it may be unintentional, this lack of data accessibility goes against the spirit of data transparency laws which are designed to empower consumers to make informed healthcare decisions and stimulate competition in the industry. Without the ease of access to price transparency data, consumers are left navigating in obscurity, unable to compare costs and make knowledgeable choices regarding their healthcare.
This is why we started Payerset - to make this data easier to access for everyone with the hope of reducing the cost of healthcare in the US.
---
## Payerset and Snowflake: Powering Data Transparency in Healthcare
- URL: https://www.payerset.com/post/payerset-and-snowflake-powering-data-transparency-in-healthcare
- Published: May 27, 2023
- Author: Joseph Tollison
- Section: News
## Introduction
We are thrilled to announce that Payerset, a pioneering solution for healthcare price transparency, is now officially listed on the Snowflake marketplace. This collaboration aims to make payer price datasets more accessible, fostering improved decision-making processes within the healthcare industry.
Access our sample datasets for free in the [Snowflake Marketplace](https://app.snowflake.com/marketplace/listings/Payerset%20LLC).
## Payerset's Solution to an Industry Challenge
Data transparency within the healthcare industry is more critical than ever. As we navigate an intricate landscape of costs and services, it is crucial to keep all stakeholders - consumers, health systems, and insurance companies alike - informed and empowered. Payerset simplifies the understanding of payer price datasets by provider, service, and payer, shining a light on a complex industry challenge.
The confidential nature of "in-network" pricing agreements between health insurers and providers has been a longstanding norm, upheld by confidentiality stipulations and antitrust rules. However, a groundbreaking shift is underway. The CMS's unprecedented initiative towards health plan price transparency mandates the public revelation of negotiated "in-network" prices shared between healthcare providers and health plans.
The introduction of new laws requiring insurance companies to publicly post all negotiated rates with providers for each billing code is a significant move towards data transparency. This shift opens up a myriad of opportunities and benefits for all players within the healthcare ecosystem.
## Benefits of Data Transparency
- **Fostering Competition:** Data transparency levels the playing field for healthcare providers. It allows smaller hospitals and providers to negotiate better rates, fostering healthy competition that leads to better services, more affordable prices, and improved patient outcomes.
- **Promoting Accountability:** Making pricing and billing information publicly available makes it easier to identify and address instances of significant price variation leading to a more efficient and ethical healthcare system.
- **Encouraging Innovation and Collaboration:** As more information becomes available, stakeholders can identify trends, areas for improvement, and new opportunities for growth, fostering the development of innovative products and services. Payerset is excited to be a part of this new wave of innovation.
## Collaboration with Snowflake
The partnership between Payerset and Snowflake elevates the importance and impact of data transparency within the healthcare industry. With Payerset's availability on Snowflake's platform, we're providing an accessible and efficient means for stakeholders to utilize and benefit from the price transparency data. This is a game-changer for insurance companies seeking to foster trust, health systems aiming to increase efficiency, and most importantly, consumers making critical healthcare decisions.
As we continue to enhance our solutions for an increasingly data-driven healthcare industry, we are excited to invite everyone to experience the benefits of our new collaboration with Snowflake.
## Conclusion
The future of healthcare lies in the effective use of transparent data. As we take a giant leap in this direction, we're thrilled to see the transformative impact Payerset will have on improving the healthcare experience for all.
We look forward to welcoming you on this journey toward a more open, equitable, and efficient healthcare system. Check out Payerset on the Snowflake marketplace today!
---
## Aetna's Price Transparency & Thwarting Web Scraping to Keep Data Under Wraps
- URL: https://www.payerset.com/post/aetna-s-price-transparency-thwarting-web-scraping-to-keep-data-under-wraps
- Published: March 2, 2023
- Author: Thomas Miller
- Section: Parsing Payer MRFs
As the demand for data transparency in the healthcare industry grows, some insurance companies have been less than forthcoming in making their price transparency data easily accessible. Aetna, one of the leading health insurance providers, is an example of a company that has made its price transparency files extremely difficult to download. In this blog post, we will explore the various techniques Aetna employs to block web scraping technologies, hindering data accessibility for consumers and other stakeholders.
## Techniques to Block Web Scraping Technologies
### Limiting Request Rates
Aetna has also employed rate limiting to hinder web scraping efforts. By limiting the number of requests a user can make within a specific time frame, the company can effectively slow down or halt the data extraction process. This makes it much more difficult for users to download large amounts of data and significantly increases the time required to obtain the necessary information.
### IP Blocking and User-Agent Analysis
To further complicate web scraping efforts, Aetna may use IP blocking and user-agent analysis to identify and block suspicious activity. By analyzing a user's IP address and the information their browser sends to the server, Aetna can identify and block users or bots that exhibit scraping behavior. This adds another layer of difficulty for those attempting to access the price transparency data.
### Obscuring Data Through JavaScript and AJAX
Aetna has also been known to obscure its data using JavaScript and AJAX techniques. By dynamically loading content on their web pages, they make it more difficult for web scrapers to locate and extract the desired information. This requires more advanced scraping techniques and additional resources, creating further barriers for users trying to access the data.
## The Impact on Data Accessibility
Aetna's efforts to block web scraping technologies have a significant impact on data accessibility for consumers and other stakeholders. By making it difficult to download their price transparency files, Aetna effectively keeps this vital information out of reach for many users. This lack of data accessibility undermines the goals of data transparency laws, which aim to empower consumers to make informed healthcare decisions and foster competition within the industry.
Without easy access to price transparency data, consumers are left in the dark, unable to compare costs and make informed choices about their healthcare.
Aetna's use of multiple techniques to block web scraping technologies highlights the challenges faced in the pursuit of data transparency in the healthcare industry. While the company may have legitimate reasons for employing these tactics, the overall impact on data accessibility is concerning. To ensure that the goals of data transparency laws are met, it is essential for stakeholders to continue advocating for increased access to information and hold companies like Aetna accountable for their actions.
---
## Anthem Blue Cross Blue Shield's Data Obfuscation: The 180GB Table of Contents
- URL: https://www.payerset.com/post/anthem-blue-cross-blue-shield-s-data-obfuscation-the-180gb-table-of-contents
- Published: February 10, 2023
- Author: Joseph Tollison
- Section: Parsing Payer MRFs
As the healthcare industry navigates the complexities of costs and services, data transparency has become an increasingly important aspect. However, not all insurance companies have embraced this change with open arms. In this blog post, we will discuss how Anthem Blue Cross Blue Shield (BCBS) made their table of contents, which is merely a list of files containing price transparency data, over 180GB uncompressed, and the techniques they employed to replicate the same data repeatedly, making it more difficult to crunch and process.
## The 180GB Table of Contents: A Barrier to Data Accessibility
Under the new law requiring insurance payers to publicly post their negotiated rates with providers for each billing code, Anthem BCBS has gone to great lengths to create a table of contents that presents a significant challenge for data accessibility. Instead of offering a concise, easy-to-navigate list of files, the company has produced a massive 180GB uncompressed table of contents.
This sheer volume of data is overwhelming for most users and requires powerful hardware to process and analyze. By employing this tactic, Anthem BCBS has effectively created a barrier to entry for consumers and other stakeholders who lack the resources to access and understand the information contained within.
## Replicating Data to Obfuscate Information
Anthem BCBS's approach to data obfuscation extends beyond the size of its table of contents. The company has also employed techniques to replicate the same data over and over, further complicating the process of extracting meaningful information. This tactic not only increases the overall volume of data but also creates confusion for users attempting to decipher the information.
The replication of data can make it challenging to identify the relevant files and extract the desired information. Users must sift through countless duplicates to find the correct data, a task that is both time-consuming and resource-intensive. This deliberate obfuscation serves as an additional barrier, discouraging users from accessing and utilizing the price transparency data.
## Understanding the Implications
By creating an unwieldy table of contents and replicating data, Anthem BCBS has effectively limited the accessibility of its price transparency data, despite technically adhering to the new law. This approach raises concerns about the company's commitment to transparency and the potential impact on consumers and other stakeholders.
As we've discussed in previous blog posts, data transparency is crucial for empowering consumers to make informed healthcare decisions, fostering competition, and promoting collaboration within the healthcare industry. When companies like Anthem BCBS employ tactics that hinder data accessibility, they undermine these goals and perpetuate existing disparities within the healthcare system.
Anthem Blue Cross Blue Shield's approach to data transparency, specifically their 180GB uncompressed table of contents and replication techniques, serves as a cautionary tale within the healthcare industry. As we continue to advocate for increased data transparency, it is vital to remain vigilant and hold companies accountable for their actions. By doing so, we can work towards a more transparent and equitable healthcare system that benefits consumers, health systems, and insurance companies alike.
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## Navigating the Maze: How Insurance Companies Found Loopholes in Data Transparency Laws
- URL: https://www.payerset.com/post/navigating-the-maze-how-insurance-companies-found-loopholes-in-data-transparency-laws
- Published: January 3, 2023
- Author: Thomas Miller
- Section: Industry Insights
In recent years, there has been a growing call for transparency in the healthcare industry, with the aim of increasing competition and empowering consumers to make more informed decisions. One such effort is the new law that requires insurance payers to publicly post all their negotiated rates with providers for each billing code. While the intent behind this law is commendable, the reality is that several insurance companies have found ways to technically adhere to the requirements without providing meaningful access to the data. In this blog post, we will explore how they managed to do this and discuss why they might have chosen this path.
## Circumventing Transparency Requirements
It is important to recognize that the law does set certain standards and requirements for insurance companies to follow. However, some insurance payers have reportedly invested significant resources into making the data they publish difficult to access and use. The techniques employed to achieve this include duplicating content millions of times and creating massive JSON files that can only be opened and processed by the most powerful hardware available. As a result, the information becomes virtually inaccessible to the average user.
## Why Insurance Companies Choose This Path
While the tactics employed by these insurance companies may seem questionable, it is essential to understand their motivations. The healthcare industry is a complex, high-stakes environment, and companies are driven to protect their interests. By making the data difficult to access, they can maintain a competitive edge and continue to negotiate favorable rates with providers.
At the same time, the current system allows these companies to technically comply with the law, thus avoiding penalties and maintaining their public image. The financial incentives and potential consequences of non-compliance are likely significant factors in their decision to skirt the rules.
## The Impact on Consumers and Stakeholders
The primary concern with these tactics is the impact they have on consumers and other stakeholders in the healthcare industry. The intended purpose of the law is to promote competition and empower patients to make informed decisions. However, when insurance companies publish unusable data, consumers are left in the dark, unable to compare rates or understand the costs associated with their care.
Additionally, this lack of transparency can perpetuate existing disparities within the healthcare system. Smaller providers and hospitals may struggle to negotiate favorable rates without access to the same information as their larger competitors.
## Moving Forward: Encouraging Transparency and Collaboration
Although the situation may seem bleak, it is not without hope. It is vital that we, as a society, continue to push for transparency and data accessibility in the healthcare industry. This can be achieved by refining existing legislation, increasing penalties for non-compliance, and promoting the development of user-friendly tools to help consumers navigate the information.
Furthermore, fostering collaboration between insurance companies, providers, and regulators is crucial to create a system that is fair and beneficial for all parties involved. Only by working together can we overcome the challenges posed by the current landscape and create a healthcare system that truly serves the needs of its users.
The new law requiring insurance companies to publish their negotiated rates is a step in the right direction toward increased transparency in the healthcare industry. However, the tactics employed by some insurance payers to circumvent these requirements highlight the need for ongoing vigilance, collaboration, and refinement. By understanding the motivations behind these actions, we can work towards creating a more transparent and equitable healthcare system for all.
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## The Power of Data Transparency: Unlocking Benefits for Consumers, Health Systems, and Insurance Companies
- URL: https://www.payerset.com/post/the-power-of-data-transparency-unlocking-benefits-for-consumers-health-systems-and-insurance-comp
- Published: December 7, 2022
- Author: Thomas Miller
- Section: Industry Insights
## The Power of Data Transparency for Payer Pricing
Data transparency has become a crucial aspect of the healthcare industry as we navigate the complexities of costs and services in a rapidly evolving landscape. A new law requiring insurance companies to publicly post all of their negotiated rates with providers for each billing code is a significant step towards achieving this goal. In this blog post, we will delve into the importance of data transparency laws and how they can benefit consumers, health systems, and insurance companies alike.
## The Importance of Data Transparency Laws
### Empowering Consumers: Informed Decision-Making
Data transparency laws enable consumers to access vital information about healthcare costs and services. With increased access to information, individuals can make informed decisions about their healthcare options, understanding the financial implications of their choices, and seeking out the best value for their specific needs. This level of transparency can ultimately lead to a more engaged and empowered consumer base.
### Fostering Competition: A Fair-Playing Field
By requiring insurance companies to disclose their negotiated rates, data transparency laws help level the playing field for healthcare providers. Smaller hospitals and providers can use this information to negotiate better rates and compete with larger institutions more effectively. This healthy competition can lead to better services, more affordable prices, and improved patient outcomes.
### Promoting Accountability and Reducing Fraud
Data transparency laws can also play a vital role in promoting accountability and reducing fraud within the healthcare system. By making pricing and billing information publicly available, these laws make it easier for regulators and watchdog organizations to identify and address potential instances of fraud, waste, and abuse. This increased level of oversight can lead to a more efficient and ethical healthcare system.
### Encouraging Innovation and Collaboration
Data transparency can also serve as a catalyst for innovation and collaboration within the healthcare industry. As more information becomes available, stakeholders can identify trends, areas for improvement, and new opportunities for growth. This can lead to the development of innovative products and services, as well as collaborative efforts between healthcare providers, insurance companies, and other industry players to improve the overall healthcare experience for consumers.
## How Data Transparency Benefits All Stakeholders
### Benefits for Consumers
For consumers, data transparency means having access to essential information about the costs and services associated with their healthcare. This enables them to make informed decisions about their care, leading to better health outcomes and more efficient use of resources.
### Benefits for Health Systems
Health systems stand to benefit from data transparency through increased competition and collaboration. With access to pricing and billing information, health systems can identify opportunities for improvement and work together to develop innovative solutions that improve patient care and reduce costs.
### Benefits for Insurance Companies
Insurance companies can also benefit from data transparency laws by fostering trust with their customers and maintaining a competitive edge. By providing consumers with clear information about their services and pricing, insurance companies can build trust and brand loyalty. Additionally, transparency can help insurance companies identify areas where they can improve their offerings and better serve their customers.
Data transparency laws play a crucial role in promoting fairness, accountability, and innovation within the healthcare industry. By making pricing and billing information publicly available, these laws empower consumers, encourage competition, and foster collaboration between various stakeholders. As we continue to push for increased transparency in the healthcare system, we can look forward to a brighter future where consumers, health systems, and insurance companies all reap the benefits of a more open and equitable system.