You've heard about how PAMA affects different groups - big labs, small labs, physician labs, and the labs of different medical specialties.
Here, I used the assumption that PAMA pricing was pushed all the way - that tomorrow's prices were set at the actual medians found in PAMA data.
My assumption was, we'd see a normal or Gaussian distribution around the average price cut. But no. For the whole population of providers (with >$50,000 Medicare lab income), the distribution of losses is very irregular.
I divided these into four groups:
- Group 1, Winners (N=82+80)
- Group 2, Average (N=1758)
- Group 3, Losers (N=984)
- Group 4, Big Losers (N=15+123)
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The uneven distribution of PAMA impact reflects different testing portfolios and commercial pricing dynamics. In each group we tried to ask both "who" is there, and "why?" Winners, particularly proprietary molecular laboratories, may benefit from differentiated assays, stronger pricing power, and Medicare rates already aligned with commercial prices. Average providers, including general laboratories and medical practices, experience approximately 16% reductions. Losers, concentrated in toxicology, pain management, and urology, may face intense competition, preferred-network discounts, and weaker bargaining power. Big losers may depend heavily on codes with exceptionally large price reductions. Code-level analysis is needed to distinguish these explanations.
Why PAMA Produces Winners and Losers:
Four Distinct Provider Populations
1. The descriptive findings
Our analysis of 4,855 provider NPIs, each with at least $50,000 in baseline 2026 Medicare laboratory payments, reveals a distinctly bimodal distribution of projected fully phased-in PAMA payment reductions. The two major peaks occur at approximately −16% and −28%, rather than clustering around the overall average reduction.
The distribution also contains two smaller but interesting populations: providers suffering exceptionally large reductions and providers experiencing little or no reduction.
For discussion, we have divided the distribution into four characteristic groups. These are selected clusters, rather than four exhaustive partitions of all 4,855 providers.
Group | Providers | Modeled change | Characteristic population |
|---|---|---|---|
Winners | 162 | −6% to less than +2% | Specialty molecular laboratories, ophthalmology, optometry |
Average | 1,758 | −18% to less than −14% | General clinical laboratories, primary care, medical subspecialties |
Losers | 994 | −30% to less than −26% | Pain management, anesthesia, urology, nurse practitioners, physician assistants, laboratories |
Big losers | 138 | −46% to less than −38% | Extreme negative tail; specialty composition requires further characterization |
The remaining 1,803 providers fall outside these four selected clusters.
The Winners: Little or no payment reduction
The 162 relatively protected providers comprise 82 with reductions between 2% and 6%, and 80 with changes close to zero.
The near-zero group is heavily dominated by clinical laboratories (60 of 80). It includes Exact Sciences, Tempus, Guardant Health, Genomic Health, Veracyte, and Biotheranostics. These are generally specialty diagnostic businesses, frequently associated with proprietary molecular and genomic testing.
The adjacent 82-provider group has a different composition: 34 clinical laboratories and 29 ophthalmology or optometry practices. All 29 eligible eye-care providers appear in this narrow interval, many with almost identical modeled reductions of 2.00178%. This concentration strongly suggests a common code or a similar mix of testing codes.
We're Average: The broad medical-practice population
The largest peak contains 1,758 providers experiencing reductions between 14% and 18%.
Unlike the worse-performing group, this population is dominated by mainstream medical specialties. Internal medicine and family practice account for 721 providers (41%), hematology and oncology for 281 (16%), and endocrinology, rheumatology, nephrology, and cardiology collectively for 272 (15.5%).
Clinical laboratories contribute 354 providers (20%). These include major general testing organizations such as Labcorp and Quest.
This population represents a broad cross-section of conventional medical laboratory utilization. Its aggregate reduction is close to the approximately 16% average decline in test prices reported by CMS.
Losers: A striking concentration of particular specialties
The second major peak comprises 994 providers with reductions between 26% and 30%.
It has a remarkably different specialty composition. Nurse practitioners and physician assistants account for 287 providers (28.9%), clinical laboratories for 241 (24.2%), pain management and anesthesia for 192 (19.3%), and urology for 131 (13.2%).
The specialty separation is striking: there are 192 pain/anesthesia providers in the losers group but none in the average group. Conversely, the 272 providers representing four medical subspecialty categories in the average group are entirely absent from the losers peak.
Podiatry provides an additional clue: 38 of the 41 eligible podiatry providers fall in the losers peak.
These concentrations strongly suggest that certain specialties use fairly standardized test portfolios, rather than that their providers independently negotiated similar commercial prices.
The Big Losers: The extreme negative tail
The 138 big losers experience reductions of approximately 38% to 46%. This is a small group, accounting for about 2.8% of the total provider population.
It is important to distinguish this group from the main losers peak. An exceptionally large modeled reduction could reflect exposure to one or two dramatically repriced tests rather than simply a more severe version of the contracting dynamics affecting pain management and toxicology providers.
The extreme-tail providers deserve a separate code-level examination before assigning them a dominant clinical or commercial explanation.
2. Why might these differences arise?
The central finding is that PAMA does not produce a uniform reduction in laboratory reimbursement. It transmits very different commercial pricing environments into Medicare, depending on the tests a provider performs.
CMS calculates PAMA rates for individual codes from volume-weighted median private-payer payments, not from the average prices received by particular medical specialties. The September 2026 preliminary CMS release reported an approximately 16% overall decline in prices, but the variation across test categories was substantial.
Therefore, the specialty segregation we observe is probably a second-order effect: different provider specialties order or perform different mixes of tests, and the tests themselves are exposed to different commercial markets.
The interesting question is why those commercial markets produce such different prices.
3. Economic explanations for the four groups
Winners: Proprietary testing, differentiated products, and less direct price competition
The most interesting feature of the winners is the presence of major genomic and molecular diagnostic companies experiencing almost no modeled price erosion.
One explanation is product differentiation and limited substitutability. A health plan negotiating for a routine chemistry panel has multiple alternative suppliers. A company offering a clinically established, proprietary cancer diagnostic may face much less direct competition for that particular assay.
An insurer cannot necessarily substitute a cheaper laboratory when the tests differ in their validated algorithms, clinical evidence, and medical indications. The laboratory may consequently possess considerably more negotiating leverage.
Coverage policy may also constrain bargaining. When a particular assay is identified by name in an insurer's medical policy, it becomes more difficult to substitute another laboratory's product merely because it is cheaper. Clinical differentiation can thus translate into pricing power.
However, a second explanation may be equally important: the price against which commercial reimbursement is being compared.
Many proprietary tests have relatively recently established Medicare rates, often developed through crosswalking or gapfilling rather than inherited from older fee schedules. A near-zero PAMA adjustment might simply mean that their existing Medicare prices are already close to current private-payer prices.
The administrative structure of the coding system is also relevant. Many proprietary tests receive individual PLA codes, and some qualify for ADLT status, with its special reporting and payment rules. These arrangements do not guarantee high reimbursement, but they can preserve the identity of individual assays rather than subjecting them to the pooled pricing of generic tests.
There is intriguing corroboration in the CMS September 2026 data. Across 151 PLA codes, the reported average price reduction was only 2.4%, compared with 22% for molecular pathology and 23% for genomic sequencing codes.
That difference is consistent with the near-zero specialty laboratory cluster, although the category-wide averages do not prove why particular PLA codes are protected.
Interpretation: The winners may possess genuinely greater commercial pricing power, but some may simply have Medicare baseline prices that were already aligned with the commercial market. Both mechanisms would produce the observed result, and they cannot be distinguished with the current provider-level data alone.
Average: Routine medical testing and broad commercial price convergence
The approximately 16% reduction characterizing the average population may represent the prevailing relationship between commercial prices and established Medicare laboratory fees.
Primary care providers, medical subspecialists, and comprehensive clinical laboratories generally use a diverse menu of routine tests. These include chemistry, hematology, immunology, metabolic monitoring, and other commonly available services.
Several features of this market may explain the moderate reductions.
First, routine laboratory testing is relatively standardized. Multiple laboratories can perform the same test, often using similar automated technology. This creates price competition.
Second, large national laboratories enjoy substantial economies of scale and can accept relatively low unit prices. In network negotiations, a commercial health plan can potentially offer substantial testing volume in exchange for discounted pricing.
Third, the breadth of the test menu matters. A laboratory negotiating a large contract may accept particularly low prices for some routine tests in return for attractive economics across the total contract. The price of any individual test need not reflect its standalone cost or value.
Finally, the diversity of testing can reduce extreme outcomes. A provider exposed to dozens of different CPT codes will usually experience a weighted average of their price changes, while a specialist relying on only a few codes can exhibit a much more concentrated response.
This may help explain why the principal average peak includes such diverse specialties yet remains narrowly distributed.
One striking implication is that the general laboratory market may have a fairly consistent commercial-to-Medicare price relationship, even across otherwise quite different medical specialties.
The stability of the approximately 16% peak may say as much about the structure of laboratory pricing as it does about any individual specialty.
Losers: Commodity testing, aggressive contracting, and concentrated portfolios
The 994-provider losers group is the richest source of possible economic explanations.
Pain management, anesthesia, urology, and podiatry are clearly overrepresented. Nurse practitioners and physician assistants also show strong concentration, although their provider labels do not identify their clinical practice settings.
A particularly plausible explanation is the economics of high-volume, relatively standardized specialty testing.
Consider toxicology. Some pain management and substance-use practices generate substantial recurring demand for drug testing. A commercial insurer may see many laboratories offering broadly comparable services, while also having strong incentives to control spending and utilization.
In that environment, competing laboratories may be willing to accept substantial price concessions to obtain preferred network status and access to patient volume.
Your hypothesis about contracting is therefore economically plausible. But I would distinguish several mechanisms:
Competitive procurement. Health plans can negotiate with multiple alternative laboratory suppliers. A laboratory offering a largely interchangeable service may have limited ability to resist price reductions.
Preferred-network access. A laboratory may accept a deeply discounted fee schedule in return for inclusion in a preferred network, more dependable patient access, and reduced barriers to utilization. The precise mechanism depends on the insurer and benefit design; direct Medicare Part B laboratory testing generally does not involve beneficiary cost sharing.
Asymmetric bargaining power. A large insurer may control access to a substantial patient population, whereas an independent regional laboratory may depend heavily on a handful of payer contracts. Even if the laboratory is profitable at prevailing prices, its negotiating position could be weak.
Competition on cost rather than clinical differentiation. Routine toxicology, urine testing, and other common specialty tests may provide fewer opportunities to defend a premium price through unique clinical evidence or proprietary intellectual property.
There is another consideration: some historical Medicare prices may have been disproportionately high relative to commercial reimbursement. If so, the projected loss reflects both today's competitive conditions and the correction of an older Medicare price structure.
This last explanation is important because it does not require an unusually aggressive new insurer negotiation. The large reduction could arise even if commercial prices have been relatively stable for years.
The 38 of 41 podiatry providers concentrated in one narrow bin are particularly instructive. Such mathematical precision is difficult to attribute to dozens of independent contract negotiations. It is more consistent with a highly standardized testing portfolio dominated by one code or a few codes with similar modeled reductions.
Interpretation: Competitive contracting and low bargaining power offer plausible explanations for why the underlying test codes have low commercial prices. But the extraordinary clustering of individual providers is probably explained more directly by their shared code portfolios. These are complementary, not competing, explanations.
Big losers: Severe historical price discrepancies and narrow code dependence
The 138 providers in the extreme negative tail may represent a different phenomenon.
One possibility is an unusually large discrepancy between historical Medicare reimbursement and current commercial market prices. Such a discrepancy may arise when an older Medicare rate was established under different cost structures, payment conventions, or utilization assumptions.
A second possibility is extreme concentration in a small number of codes. An organization receiving most of its Medicare laboratory revenue from one dramatically repriced test could lose more than 40% even if the remainder of its testing portfolio is relatively stable.
A third possibility concerns how services are packaged or valued by commercial payers. Where coding conventions permit, a commercial payer might reimburse components of a testing service at relatively low amounts compared with historical Medicare rates. Such arrangements could produce large code-level differences without necessarily indicating that the laboratory's total commercial business is unprofitable.
I would be cautious about attributing this group primarily to weak market power. A 40% or greater loss is not itself evidence of a poor negotiating position. It is evidence of an unusually large difference between the modeled new and existing Medicare reimbursement for that provider's test mix.
For this group, the underlying CPT/HCPCS codes are essential.
4. Comparing the possible drivers
Economic explanation | Winners | Average | Losers | Big losers |
|---|---|---|---|---|
Proprietary or differentiated assays | Strong candidate | Less important | Less important | Unknown |
Highly competitive commercial market | Less likely for unique assays | Important | Potentially very important | Possible |
Preferred-network discounts | Variable | Common possibility | Strong hypothesis | Possible |
Laboratory bargaining power | Potentially greater | Mixed | Potentially weaker | Unknown |
Concentration in a few test codes | Variable | Often less concentrated | Strongly suggested | Strong candidate |
Historical Medicare prices above commercial prices | Less apparent | Moderate | Potentially large | Potentially extreme |
Coding and Medicare pricing history | Important | Important | Important | Potentially decisive |
5. A particularly important insight:PAMA transmits prices, not costs
There is a conceptual distinction worth emphasizing.
PAMA's weighted-median methodology does not determine the actual cost of performing a test. Nor does it determine the test's clinical value. It transfers prices established through commercial insurance arrangements into the Medicare fee schedule.
Commercial prices can embody a complicated mixture of production costs, supplier competition, insurer bargaining power, network access, market concentration, clinical differentiation, and historical pricing conventions.
Moreover, CMS uses the volume-weighted median price, so high-volume contracts can have more influence over the Medicare rate than numerous low-volume contracts. The prices include applicable discounts and other concessions reflected in final payments, subject to PAMA reporting rules.
This creates an interesting feedback mechanism: a laboratory's willingness to accept a lower commercial payment to obtain additional volume may ultimately help reduce Medicare reimbursement for that code nationally.
The laboratory that negotiates an aggressive commercial contract may therefore contribute to a Medicare price reduction affecting itself and its competitors. That is a distinctive feature of PAMA's architecture.
It also means that the market power of large laboratories can have an ambiguous effect. Large laboratories may negotiate favorable prices when they possess differentiated offerings, but their scale may allow them to accept lower prices for routine tests to gain substantial business. When those prices enter the national weighted median, smaller competitors may experience the resulting Medicare reductions even if they never negotiated comparable contracts.
6. What the bimodality tells us about the marketplace
Two dominant modeled reduction peaks
−28%
Losers
Specialized, concentrated portfolios
−16%
Average
Broad medical-practice portfolios
A normal distribution of losses might suggest numerous relatively independent factors producing modest deviations around a common mean.
Instead, the sharply separated peaks suggest at least two distinct underlying pricing regimes or code portfolios.
One regime appears associated with general clinical laboratory testing, where commercial payments stand moderately below current Medicare prices.
The other appears associated with certain specialty testing markets, where the difference is substantially greater.
The winners may represent a third regime: differentiated proprietary diagnostic tests whose commercial and Medicare payments are much closer together. The extreme losers may represent a fourth, although their characteristics remain insufficiently established.
This is stronger than simply observing that toxicology providers lose more than internists. It suggests that commercial laboratory pricing may be segmented into fundamentally different markets, with different competitive structures and historical price relationships.
There is a significant qualification: the four groups do not necessarily correspond to four coherent commercial marketplaces. Several different tests could produce similar numerical price changes by coincidence. The code-level distribution will determine whether the apparent market segmentation is real.
7. Where we could take the analysis next
The most valuable next step is not to collect more provider names. It is to identify the individual laboratory codes responsible for each peak, then examine their commercial pricing histories. If you have the provider names, or NPI numbers, CMS provides just that information (for CY2024, as of today).
In particular, I would choose representative providers from pain management, podiatry, general internal medicine, ophthalmology, and the near-zero molecular diagnostic group.
For each, I would ask which three to five codes account for most of the baseline Medicare dollars, how much each code's price changes, and whether those prices are supported by actual reported commercial medians or another rate-setting methodology.
That would allow us to test several competing hypotheses: whether the losers are dominated by relatively inexpensive commodity testing, whether the winners are protected by proprietary assays, and whether large reductions are primarily due to historically inflated Medicare prices rather than current commercial pricing pressure.
We should also examine how much of each peak represents a single repeated code-level price change. The eye-care group provides a promising precedent: 20 providers displaying almost exactly the same 2.00178% reduction are unlikely to reflect 20 independently negotiated economic outcomes.
Finally, provider counts should not be confused with financial exposure. In our earlier analysis, clinical laboratories represented only 24% of the losers peak and 20% of the average peak, but accounted for 82% and 91% of their respective baseline dollars. In the near-zero group, laboratories represented 99.4% of baseline dollars. Thus, the business story of PAMA is overwhelmingly concentrated in laboratory reimbursement, even though physician and nonphysician specialties dominate much of the provider-count distribution.
Conclusion
Let's summarize the findings this way:
The bimodal distribution of PAMA reductions is probably not an accidental statistical feature. It reflects the interaction of clinical test selection, commercial laboratory market structure, and historical Medicare payment policies.
The smaller reduction peak is associated with broad-based medical laboratory testing. The larger reduction peak is concentrated in specialties whose testing portfolios may be more standardized and commercially competitive. Proprietary molecular diagnostics often appear relatively protected, potentially reflecting differentiated clinical products, pricing power, or Medicare rates already aligned with private-payer payments.
The central economic insight is that PAMA converts commercial contracting outcomes into nationwide Medicare prices. Differences in insurer bargaining power, laboratory competition, preferred-network contracting, and proprietary test differentiation can therefore become differences in Medicare reimbursement—even for providers who had no direct role in negotiating the contracts that ultimately determined those prices.
The next stage of analysis should establish which of these explanations are actually supported by the code-level data.
And one distinction is worth preserving throughout: the data compellingly demonstrate the separation of provider populations; they do not yet prove why their underlying tests command different commercial prices. That is the economic question we have now uncovered.
