Monday, October 5, 2026

PAMA: What Happened to Exome, Genome, and NIPT?

 Under PAMA, what happens to Exome, Genome, and NIPT beginning in 2027?

Here's the data, click to enlarge:

click to enlarge

Exome 2018 

In 2018 PAMA data, Exome came in at $4780, sibling exome at $12,000, and exome re-evaluation aka "dry lab" at $320.   This data was based on very few claims and some thought the $12,000 number may have mistakenly been meant as the family price (parent, parent, child = $4000x3 = $12,000).

Exome 2025

Regardless, the new prices (2025 prices reported in 2026) are exome $3680, sibling $6480, and dry lab $230.  About 30 labs reported an exome price.  [Note, CMS CLFS prices won't drop more than 15% per code per year.]

Genome 2018 to 2025

Moving on to genome, the prices were $5031, $2709, and $2337.   Genome drops to $3018, which is a 40% drop.   Sibling genome is $1650 (much less than sibling exome), and dry lab has no PAMA price report (CMS will  gapfill or crosswalk it).  Note CMS also has proposed taking "dry lab" genomics (81417, 81527) off the CLFS.

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NIPT 81420

For NIPT, there is the general code 81420 $759 and microdeletion 81422 $759.   These drop to $584 and $621, about -20%.  No surprise that 81420 was reported to CMS by 121 labs, much more than the other codes I review in this blog.  (Not shown here; PLA codes for NIPT for a few labs).

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It's possible to get raw data and map the distribution of prices reported to CMS, of which only the median result is seen in the Excel.  For more background, an example of distribution, and links, see here.

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###   Click to enlarge.  Distribution of NIPT per CMS PAMA data.



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SIDEBAR: 121 labs and LDT NIPT?

No FDA-cleared or approved U.S. kit was identified for the cell-free DNA prenatal screening represented by 81420. FDA has described these tests as lacking its authorization; its published list of cleared or approved molecular tests contains no corresponding NIPT assay.

However, 121 laboratories reporting prices does not establish there must be 121 independently developed NIPT assays.  PAMA collects payment information, while inter-laboratory billing arrangements can include specimens sent elsewhere for testing. Send-out testing—with the referring laboratory billing the payer—is therefore a plausible explanation for part of the count. But the exact number of operating NIPT labs - whether 5, 10, or 121 - can't be established from the PAMA data. 

##  Try.

A reasonable working estimate is about 10 distinct U.S. NIPT assay families, with a plausible range of 10–20 when separately validated local versions are included. That is an informed estimate, not a verified census.

Five prominent offerings are readily identifiable: Natera’s Panorama, Labcorp’s MaterniT21, Quest’s QNatal, Myriad’s Prequel, and BillionToOne’s Unity. Additional assays and laboratory implementations, including ARUP’s, push the count beyond five. Natera

The counting problem is that a distinct laboratory implementation need not represent a wholly independent invention: laboratories may use shared commercial technology but validate their own clinical assay. Conversely, a laboratory listing NIPT may simply send it out—Mayo’s MaterniT21 listing explicitly names Sequenom/Labcorp as the performing laboratory. aruplab.com

Thus, “roughly a dozen underlying assays, distributed through a much larger network of billing and referring laboratories” is a defensible characterization. The 121 PAMA reporters cannot establish the exact number, and the broader NIPT market also includes tests billed under codes other than 814

PLA Use of "100 genes or more." It's baaaack!

 For several years, there seemed to be a PLA moratorium on the use of "or more" so you'd see the construction "Panel of 85 genes" instead of "Panel of 85 or more genes."   Use of "Panel of 85 genes" suggested you'd start over with the PLA panel if you added a gene ("Panel of   85  ^86   genes.")

I discussed this in some detail today in a blog about new PLA codes.

https://www.discoveriesinhealthpolicy.com/2026/10/cms-issues-new-october-pla-codes-new.html



CMS Issues New October PLA Codes, New October Clin Lab Fee Schedule CLFS. Essay on "Or More."

CMS has released the October 1, or fourth-quarter, Clin Lab Fee Schedule, CLFS.   The next one, on January 1, will be based on PAMA prices.   CLFS here.  PLA codes (new and  unpriced) run up to 0698U.

AMA has issued PLA codes for the October 1 cycle.  This includes "new" codes released now and effective January 1, 2027  PDF here.  0699U to 0715U are released now for January.

The Mysterious Use of "OR MORE"

Labs that run gene panels don't want a PLA code with an exact number ("panel of 73 genes") since it has to be updated with product cycles (83 genes, 93 genes, etc).   From what I can tell, PLA gave out the format   "N genes or more"    regularly up to 0334U in October 2022.   

Then, we don't see the format again until now, where Guardant RNA test 0696U has "350 or more genes."   

This might seem like the ultimate micro-nerd observation, but if you got a code between 2022 and 2026 ,and had to editorially update it from "75 genes" to " 75 85 genes" this will all register as important.

Here's a Chat GPT extraction, which I'm trusting to find "or more."  Note the gap from 0334U to 0696U.

click to enlarge

(The analysis is based on current CLFS, and won't include deleted examples.)

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For use of "or more" to represent substances rather than genes, we have more examples, although there is still a gap from 0328U to 0511U.   


See also this uses:



At Least 110...

There is also some usage, not much, of "at least." 

Note that the meaning of "at least 85" and "85 or more" is identical.

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We should thank the PLA committee for  ag ain employing the term "or more" as in "85 or more genes."  This saves editorial revisions for the PLA itself, for the Path Coding Caucus, the Editorial Panel, and CMS, which must decide which editorial revisions trigger a new pricing year.

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And here should be an open public link to the above AI analysis. of the latest PLA list from AMA and the 10-2026 CLFS.  

https://chatgpt.com/share/6ac3ffc0-eaf4-83e8-8d54-c02212e5cf79







FDA Awards $1M for Approval Methods for AI Radiology Reports. Will AI Pathology Reports be Far Behind?

I've argued that the billion-dollar app in digital pathology will be an FDA-cleared app for draft report preparation.  (The pathologist then supervises the slide results and confirms/edits the report).  (My blog & PDF white paper.)  

  • Critical news 1: FDA has already starting approving some specific apps for draft reports in radiology.
  • Critical news 2:  FDA gave a $1M contract research award for how it should assess and approve report-generating apps in radiology.   Worth tracking because:  pathology won't be too far behind.

Report below by Chat GPT 6.



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FDA Funds Research on AI Generated Radiology Reports and Pathology Should Watch

I’ve argued that the billion-dollar app in digital pathology will be an FDA-cleared application for draft report preparation. The software analyzes the slides and prepares a draft; the pathologist reviews the findings, confirms or edits the report, and signs it out. That is the thesis of my September blog and accompanying white paper. [1]

Radiology is already moving in this direction, including FDA clearance of specific applications with automated reporting capabilities. Now comes another development worth tracking: a $1.29 million FDA research contract to study how AI-generated radiology reports should be evaluated. My thesis is that radiology offers a reliable preview of changes that will reach pathology after a lag. This award addresses a problem both specialties will need to solve.

Sunday, October 4, 2026

Understanding Sparano via Understanding the "C Statistic"

The adjacent blog about the Oncotype test and Sparano's new IICM+ test, pivots almost every quantitative comparison on "C-statistic.'   If you're like me, you may be asking, "Whazzat!?!"

Here's a Chat GPT explanation of C statistic.

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The C-Statistic for the Perplexed: What Does “Better Prediction” Actually Mean?

A new cancer prognostic test is reported to outperform an established test. The evidence includes a C-statistic of 0.74 versus 0.58. Those numbers sound consequential—but what, exactly, did the better test do better? Did it correctly predict recurrence in 74% of patients? Did it identify more patients who needed chemotherapy? Did it estimate each patient’s risk more accurately?

The C-statistic answers a narrower question: How reliably does the test distinguish patients who experience an event sooner from those who experience it later? Understanding that question makes the numbers useful while preventing us from asking them to prove too much.

The explanation begins with the familiar ROC curve. We will connect its sensitivity and specificity axes to comparisons between individual patients, extend that idea to cancer recurrence over time, and then consider what a higher C-statistic establishes about a new test.

Most readers remember that a diagnostic test involves a tradeoff between sensitivity and specificity. A blood test might produce a numerical score. Set the threshold for “positive” low, and the test detects more affected patients but also flags more unaffected patients. Set it high, and false positives decline, but more affected patients are missed.

An ROC curve displays that tradeoff as the threshold moves. Its vertical axis is sensitivity; its horizontal axis is 1 − specificity, or the false-positive rate. The AUC, the area under that curve, summarizes how well the scores separate affected from unaffected patients across possible thresholds.

But AUC has another interpretation that is particularly helpful here:

Choose one patient with disease and one without disease. How often does the patient with disease receive the higher test score?

That probability is the AUC, with half-credit for tied scores. The graph and the patient-pair comparison are two ways of expressing the same quantity.

Consider four invented patients:

PatientActual statusTest score
ADisease90
BDisease60
CNo disease70
DNo disease20

Compare each affected patient with each unaffected patient. There are four comparisons:

  • A versus C: correctly ordered.

  • A versus D: correctly ordered.

  • B versus C: incorrectly ordered.

  • B versus D: correctly ordered.

The test wins three of four comparisons: AUC = 0.75.

Notice what we have counted: comparisons between patients. We have not chosen a positive-test threshold or counted correctly diagnosed individuals. Those require an additional decision about where to put the cutoff.

Now consider cancer prognosis. Everyone in the study may already have cancer. The task is to anticipate a future event, such as distant recurrence.

We could ask a yes-or-no question: “Did recurrence occur within five years?” That can support a five-year ROC analysis. But a study following patients over many years has additional information: when recurrence occurred. Recurrence at year 2 and recurrence at year 9 are both events, but they represent different clinical courses.

The survival C-statistic, also called the concordance index, evaluates whether the model’s ordering agrees with that observed sequence. Higher predicted risk should generally correspond to earlier recurrence.

Suppose Alice recurs at year 2 and Beth at year 9. If the model assigned Alice the higher risk score at diagnosis, their comparison is concordant: prediction and outcome agree. If Beth received the higher score, it is discordant.

A C-statistic of 0.75 therefore means, approximately, that the model correctly orders three out of four eligible patient pairs. A value of 0.50 represents chance-level ordering; 1.00 represents perfect ordering. Unlike ordinary binary AUC, survival concordance can compare two patients who both experience the event, asking which experiences it sooner.

There is one complication that matters enormously in real studies: we do not observe everyone indefinitely.

If Alice recurs at year 2 and Carol remains recurrence-free through year 8, their ordering is clear. Carol did not recur before Alice.

But suppose Diane leaves the study after year 1 without a recurrence. We cannot determine whether Diane subsequently recurred before or after Alice. Her follow-up is censored: we know she was recurrence-free through year 1, but not what happened afterward.

Different survival C-statistics handle incomplete follow-up differently. Sparano and colleagues used Uno’s C-statistic, which weights observed comparisons to account for censoring under specified assumptions. Consequently, its result is an estimated probability of concordance, rather than necessarily the simple percentage obtained by counting observed pairs. A time-specific ROC AUC and an overall survival C-statistic are related, but they need not have the same value.

This gives the Sparano results a more concrete meaning:

Recurrence periodOncotype Recurrence ScoreFull multimodal model, IICM+
Overall0.5780.735
Early: ≤5 years0.7220.791
Late: >5 years0.5140.710

These are the reported results in the held-out validation cohort. The improvement was larger for late recurrence than for early recurrence.

For the overall comparison, a reasonable plain-language interpretation is:

Under the study’s follow-up definitions and statistical adjustment for incomplete observations, the full model had an estimated 74% probability of correctly ordering an eligible patient pair, compared with about 58% for the Oncotype Recurrence Score.

The difference is about 16 percentage points in concordance. It does not establish that 16 additional patients per hundred would receive the right treatment.

Two further distinctions explain why.

A model can order patients correctly while giving them inaccurate numerical risks. Imagine that one model assigns three patients ten-year recurrence risks of 2%, 5%, and 10%. Another assigns the same patients risks of 20%, 50%, and 90%. The ordering is identical, so their C-statistics are identical. Yet the counseling and treatment implications could be dramatically different.

Whether patients assigned a 10% risk actually experience approximately 10 recurrences per 100 comparable patients is a question of calibration. C and AUC measure discrimination—the ability to distinguish outcomes—not calibration.

Better ordering also need not translate directly into better treatment decisions. Correcting the order of two patients who would receive the same treatment may accomplish little clinically. Improving risk assessment around a treatment threshold could matter considerably. The C-statistic alone does not distinguish those situations. Nor does predicting recurrence establish which patients benefit from chemotherapy. Researchers have specifically cautioned against treating survival concordance as a complete measure of clinical usefulness.

For readers assessing the next “better than Oncotype” headline, the practical questions are therefore:

QuestionWhat answers it?
Does the model reliably place earlier-recurrence patients above later-recurrence patients?Survival C-statistic
Are its numerical recurrence probabilities believable?Calibration
How many recurrences are detected or missed at a chosen cutoff?Sensitivity and specificity at that cutoff and time horizon
Does using the result improve treatment choices or patient outcomes?Clinical utility evidence

A higher C-statistic is meaningful evidence of better prognostic discrimination in the population studied. Establishing a better clinical test requires the remaining questions to be answered as well.


FOR THE STUDENT:  TEST YOUR UNDERSTANDING

  1. A prognostic model has a C-statistic of 0.80. A colleague says, “It correctly predicts recurrence in 80% of patients.” What is wrong with that statement?

    The denominator is patient-pair comparisons, not individual patients. The model correctly orders approximately 80% of eligible pairs, assigning higher risk to the patient who recurs sooner, with appropriate handling of incomplete follow-up.

  2. How can an area under a sensitivity–specificity curve also describe patient ranking?

    These are mathematically equivalent interpretations of ROC AUC. Moving the cutoff traces the curve; comparing every affected patient with every unaffected patient measures how often the affected patient scores higher. With half-credit for ties, that proportion equals the AUC.

  3. Two models have identical C-statistics, but one reports much higher recurrence probabilities. Can both be equally useful?

    Their ability to rank patients may be identical, while their calibration differs substantially. If one systematically exaggerates absolute risk, it could encourage unnecessary treatment despite its respectable C-statistic. Calibration must be evaluated separately.

  4. A new test raises the C-statistic from 0.72 to 0.75. What would you want to know before paying for it?

    Is the improvement reproducible in independent patients, and does it change decisions where treatment benefits outweigh harms? A small increase could be valuable if it improves consequential decisions; a larger increase could accomplish little if management remains unchanged.

References

  1. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143(1):29–36. https://doi.org/10.1148/radiology.143.1.7063747.

  2. Uno H, Cai T, Pencina MJ, D’Agostino RB, Wei LJ. On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Statistics in Medicine. 2011;30(10):1105–1117. https://doi.org/10.1002/sim.4154. Free full text.

  3. Hartman N, Kim S, He K, Kalbfleisch JD. Pitfalls of the concordance index for survival outcomes. Statistics in Medicine. 2023;42(13):2179–2190. https://doi.org/10.1002/sim.9717.

  4. Sparano JA, Lama N, Gray RJ, et al. An Artificial Intelligence (AI) model integrating multiscale foundation model histopathology representations with molecular and clinical features predicts early and late distant recurrence in TAILORx. npj Breast Cancer. 2026. https://doi.org/10.1038/s41523-026-01022-y.

Is Sparano et al. Better than Oncotype DX? New Publication.

From time to time a test is published as "Better than Oncotype Dx," which has been around since about 2004.

Some have even been based on H&E slides, WSI, and AI.

Here's a complex multi-modal tour-de-force approach to a new test.  Here's some pre-publicity from 12/2025 on the project, from Caris.  

(See supplements at bottom of blog, for a tech critique of Sparano AI (via Dawood), and views about the test and Caris financials.)

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“Better Than Oncotype”?
A New Breast Cancer Study Combines AI, Genomics,
and Clinical Data

Recent coverage in ASCO AI in Oncology highlights an intriguing result: a new algorithm predicted breast cancer recurrence more accurately than Oncotype DX. The underlying study, published by Sparano and colleagues in npj Breast Cancer, combines digital pathology, gene expression, and clinical information. It offers substantial evidence of improved prognosis prediction—and several questions about what would make that improvement clinically useful. [1,2]



What the investigators built

Case Study: The Border Between Wellness Devices, Medical @Home Devices, and Coding

 What happens when a $299 wristband challenges a medical service built around much more expensive equipment? 

In this fictional business school case, TrackBP has raised $75 million to bring cuffless ambulatory blood pressure monitoring to market. (One part of the large remote physiological monitoring market, RPM.)  Its CEO sees a cleared FDA predicate and an existing CPT code. What could go wrong? Plenty: matching individual readings does not prove accuracy over 48 hours; clearance does not guarantee coverage; and cheaper technology can unsettle reimbursement assumptions. 

The case explores where wellness becomes medicine and asks whether FDA, AMA, and CMS can keep pace as familiar services move into radically smaller devices.

Case generated by Chat GPT Astra 1 (10/2026).  It should not be used as ground truth, but it can be an example of current abilities of AI to research and write a report.

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Find the report in the cloud HERE.


Friday, October 2, 2026

What Cancer Payment Rewards, and What Gets Left Behind: Shah 2026 in JAMA Oncol.

 In a new article at JAMA Oncology Shah describes two treatments - one superior - and then reveals we pay $100,000 for the less useful thing and $10,000 for the most useful thing.  He also reveals that most useful thing is radiotherapy.  And it is "facing drastic reimbursement cuts, leading some centers to shut down."  (Shah is a radiation oncologist.)  


 

He doesn't really give a clear description of how and why the two prices are so different.  (And why? Power? Malevalence?  Accident? Don't assume evil when inattention will do.)  Let's take a look at his article and write our own.  

As ten-page white paper HERE.

(Essay by Chat GPT 6).

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When AI Beats Google: Agentic AI Searchs for Media about "14 Day Rule"

The "14 day rule" for CMS lab specimens is pretty obscure.  I asked Chat GPT to search the internet for media, op eds, etc, on 14 day rule.   Here's what it came back with...in...two minutes.

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Media about the 14 Day Rule (AI agentic internet research)

Please use agentic internet research to find articles or media about the 14 Day Rule.

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Yes—there is meaningful public coverage, including op-eds specifically attacking the inpatient 14-day rule. But your impression is right: the discussion remains concentrated in oncology and physician media. I did not locate a verified Politico, Washington Post, or The Hill article specifically examining the inpatient rule’s harm to genomic testing. That leaves a plausible opening for a broader policy story.

The strongest findings follow, prioritized for your communications effort.

1. KevinMD: an entire physician op-ed on precisely your issue

“Medicare’s 14-day rule is hurting cancer patients” — Sean Jordan, MD, July 5, 2024.

This is the closest match to the opinion piece you envision. Jordan, a thoracic surgeon, describes the difficulty of ordering molecular testing after inpatient lung cancer surgery. He explicitly distinguishes 14 days after discharge from 14 days after surgery, explains the outpatient exception, and argues that the remaining inpatient policy delays results and subsequent systemic treatment.

He also describes the substantial work his institution undertook to establish compliant pathways. An excellent precedent—and Jordan is a potential clinician source for a reporter. kevinmd.com

2. OncoDaily: another piece devoted entirely to the inpatient rule

“Daniel Flora: The 14-Day Rule – When Bureaucracy Delays Cancer Care” — October 25, 2025.

This republishes a Substack post by Daniel Flora, medical oncologist and medical director of oncology research at St. Elizabeth Healthcare. He describes the postdischarge waiting period, followed by laboratory turnaround, potentially stretching the delay to a month or more. He calls for CMS and professional societies to reform the policy.

This is public physician advocacy rather than independently reported journalism, but it is exceptionally direct and accessible. OncoDaily

3. The ASCO Post: recent commentary from a prominent oncologist

“Access Denied: Insurance Barriers to Biomarker Testing in Lung Cancer” — Charu Aggarwal, MD, MPH, FASCO, July 25, 2026.

Aggarwal explicitly identifies the inpatient 14-day rule as an administrative barrier that delays treatment. The article places it within the larger mismatch between precision oncology and insurance policy, including the growing importance of testing in earlier-stage disease.

Especially useful given ASCO’s participation in your coalition, although an individual author’s commentary should not be presented as an official ASCO position. The ASCO Post

4. AJMC: reporting published just this week

“Overcoming Barriers to Precision Care and Patient Access” — Brooke McCormick, September 28, 2026.

Reporting on a Seattle oncology meeting, McCormick identifies the rule as a persistent barrier. Siddhartha Devarakonda of Providence Swedish Cancer Institute calls it “a huge disservice to our patients.” The article discusses clinicians’ use of rapid assays and outpatient ctDNA testing to address urgent diagnostic needs.

This provides a recent reporter byline, named clinical sources, and descriptions of practical consequences—all useful for developing a national story. AJMC

5. MUSC Hollings: a public-facing research story explaining the clinical stakes

“From biopsy to biomarker results: Hollings researchers uncover surprising results in turnaround time” — June 19, 2026.

The story features Adam Fox and Gerard Silvestri, discusses lengthy biopsy-to-result intervals, and expressly identifies the inpatient 14-day rule as a potential contributor. It explains why patients diagnosed during hospitalization still need biomarkers to guide subsequent outpatient care.

It also explains why starting treatment before results can have consequences beyond simply switching drugs later. However, the reported turnaround-time findings do not establish how much delay the rule itself caused. MUSC Hollings Cancer Center

Additional public coverage worth keeping in the packet

Outlet and itemRelevance
Targeted Oncology, February 9, 2024: “Worth The Wait? Genomic Testing Delays Initiation of Advanced NSCLC Therapy”, Jonah FeldmanReports oncologists discussing the 14-day postdischarge barrier alongside laboratory turnaround and pressure to start treatment. Immunotherapy, Biomarkers, and Cancer Pathways
OncLive, 2022: “Genomic Testing Challenges Persist”Tracey Evans discusses the rule’s particular difficulty for patients hospitalized with urgent NSCLC symptoms. An earlier example of clinician-focused reporting. OncLive
AJMC, July 6, 2026: Video discussion on biomarker-testing barriers in extrapulmonary neuroendocrine carcinomaIdentifies the inpatient rule as especially problematic for fast-growing tumors. Useful evidence that the concern extends beyond lung cancer. AJMC
LUNGevity, October 9, 2025: “Removing Obstacles to Drive Timely Precision Medicine for Patients”Publicly reports that, among surveyed respondents who applied the rule, 78% reported treatment delays and 40% reported treatment decisions sometimes preceding results. These are respondent reports, not percentages of patients harmed. LUNGevity Foundation

The historical coverage is also useful—and includes you.

Turna Ray’s November 3, 2017 GenomeWeb article, “CMS 14-Day Rule Revisions Ease Precision Medicine Access for Patients, Stakeholders Say”, is available as a public PDF through LUNGevity. It discusses delayed access, quotes LUNGevity, and cites your analysis repeatedly. It concerns the outpatient reform, making it useful background for a story about the inpatient problem that remained. lungevity.org

There is also a November 14, 2024 CAP public advocacy update describing support for a resolution urging changes so that complex testing on inpatient specimens can be paid separately, consistent with outpatient rules. That helps demonstrate that the concern reaches beyond laboratories and individual oncologists. CAP

Two considerations for a media pitch

First, some existing pieces say Medicare prohibits testing or requires physicians to wait. Your coalition can explain this more accurately: the rule creates a payment and billing barrier that can cause testing to be delayed; it does not prohibit clinically indicated testing during the window. CMS’s own explanation anchors the rule in specimen collection and order timing, with a qualifying outpatient exception. The ASCO Post article also appears to miscite the regulation; CMS identifies 42 CFR 414.510, not 410.508. CMS

Second, another public narrative already exists: enforcement against laboratories accused of manipulating the rule. DOJ’s 2022 Caris and 2023 Genomic Health announcements expressly discuss delayed cancer tests, but frame the problem as improper billing and circumvention. A reporter may encounter these quickly, so reform advocacy should distinguish the policy’s adverse incentives from defending violations of existing requirements. United States Department of Justice

My assessment is that you have ample published precedent to substantiate a pitch, while the issue still appears underexplored in general-interest policy journalism. The most useful starting packet would be Jordan’s op-ed, Aggarwal’s commentary, the September 28 AJMC reporting, and the MUSC story—paired with a documented patient timeline and the coalition’s concrete reform request.

One search limitation: The Hill and Politico restricted direct access. LUNGevity lists a December 2018 Hill opinion piece about lung-cancer treatment access, but I could not verify its contents and therefore have not counted it as coverage of the 14-day rule. LUNGevity Foundation

Tuesday, September 29, 2026

CMS Releases Pricing Proposals for 110 New 2027 Lab Codes

 CMS has released its 110 proposals for new lab codes for 2027.  On this web page, scroll down for Agendas and Important Materials to find the proposals.  CMS takes comment until October 21.

Chat GPT provides an analysis (giving me what I asked for, without my having to count row by row.)



The CMS Pricing Proposals, September 2026

110 codes are in play. 

CMS proposes crosswalking 61 and gapfilling 49. 

The striking finding is that CMS follows the panel’s exact majority recommendation for fewer than half the codes, although many departures preserve crosswalking while changing the comparator or multiplier.

The basic counts

Code groupCrosswalkGapfillTotal
PLA codes5349102
Other codes808

Total
61 (55%)49 (44%)110

PLA codes constitute 93% of the list. Answering the two directional questions explicitly:

  • Of proposed gapfills, 100% are PLA codes: 49/49.

  • Of PLA codes, 48% are proposed for gapfill: 49/102.

Not all the items have been assigned a "final code" on which we set PLA and non-PLA decisions.  Six non-PLA entries still show “TBD” in column C; I counted their distinct temporary codes in column B. Also, the 110 entries include four reconsiderations, so “110 codes under review” is more precise than “110 new codes.”

Most crosswalks are straightforward...57 out of 61!

Of the 61 proposed crosswalks, 57 (93%) use one code at one unit, without multiplication, addition, or subtraction. Only four are more complicated:

CodeCMS proposed crosswalk
0575U0005U × 0.5
0668U81229 + 86301
0600U87633 + 87632
0666U87633 + 87632

Thus, there are three additive crosswalks and one fractional crosswalk. 

None uses an upward multiplier.

How often does CMS follow the  expert panel?

Agreement needs to mean the same crosswalk code and arithmetic, or the same gapfill recommendation. Simply counting column I’s statements that CMS “agrees” would overstate agreement.

Comparison with the panel’s majority recommendationCodes
Same recommendation53
Different crosswalk code, multiplier, or combination35
Panel crosswalk → CMS gapfill19
Panel gapfill → CMS crosswalk2
No majority for a specific recommendation1
Total110

Among the 109 codes with a specific majority recommendation, CMS agrees exactly on 49% and departs on 51%.  Basicaly, CMS agrees vs disagrees 50/50.

The exception is 0657U: votes split 4/2/3 among three crosswalks, with three votes for gapfill. CMS chooses gapfill. That rejects the panel’s collective preference for crosswalking, but there was no majority for any particular comparator.

The disagreements between CMS and its Experts reveal several recurring priorities.

1. Missing private-payor data disrupts otherwise acceptable crosswalks.

For 13 codes, CMS explicitly cites the absence of private-payor data (in the currently active PAMA surveys) a relevant comparator. Examples include 0698U, where CMS substitutes 0523U for the panel’s 0570U, and MRD codes 0642U and 0647U, where CMS substitutes 81420 for 0307U. For 0635U, an atopic-dermatitis gene-expression test, CMS instead proposes gapfill because it could not identify a suitable replacement.

This is a substantial theme: a comparator can appear technically appropriate yet still fail CMS’s pricing rationale. These 13 cases overlap other disagreement categories; they are not an additional tally.

2. CMS repeatedly rejects multiplying prices to reflect additional test content.

Six infectious-disease antibody codes—0580U, 0615U, and 0636U–0639U—received unanimous panel recommendations using multipliers of two to five. CMS retains the base comparator but eliminates every multiplier, citing immunoassay efficiencies. Holding the comparator’s price constant, that produces amounts 50%–80% below the panel’s recommendation.

The same concern appears in sequencing. For 0672U–0676U, CMS rejects adding proband and comparator-code prices and proposes gapfill, citing shared informatics and reporting efficiencies. For 0687U, it removes the additional half-unit of 81266 because it believes 81265 already includes comparator-specimen resources.

3. Technical platform can outweigh similarity in clinical purpose.

The largest repeated disagreement concerns 12 methylation-based risk tests, 0616U–0627U, spanning conditions including dementia, cardiovascular disease, and psychiatric disorders. The panel favored 0565U; CMS selects 0318U, explaining that microarray technology is a better match than NGS. In 11 of these rows, 0318U explicitly received zero panel votes; it is not listed as a voting option in the remaining row.

Platform differences also drive gapfill decisions for 0690U—digital PCR versus an NGS comparator—and 0613U, where CMS distinguishes the new test’s NGS component from the proposed comparator’s PCR methods.

4. CMS favors gapfill when it considers the resource comparison insufficiently specific.

Examples include MRD codes 0688U and 0689U, rapid/ultrarapid sequencing codes 0657U–0659U, and kidney-disease code 0653U. For 0653U, CMS distinguishes analysis of a portion of the exome from the panel’s proposed comparator, which CMS describes as whole-exome sequencing. For liver methylation codes 0611U and 0612U, CMS says the descriptors do not clearly establish the method needed to support the proposed NGS crosswalk.

The recurring message is that broad clinical resemblance does not establish comparable resources.

5. CMS also overrides gapfill recommendations when it sees a usable existing method.

There are only two such reversals. For prostate-risk code 0609U, CMS selects 81539 because the descriptors are nearly identical, despite a 10–1 panel preference for gapfill. For 0604U, CMS rejects unanimous gapfill and selects the generic LC-MS/MS comparator 83789, reasoning that the method is common on the CLFS.

A further notable override is 0697U, an obesity-related genetic test: CMS adopts the laboratory’s proposed comparator 0349U, which received zero votes, over the panel’s unanimous choice of 0466U.

What It All Means

Taken together, these decisions show CMS emphasizing method, descriptor specificity. 
This year CMS often lowered pricing by referring "shared processing efficiencies" for multiple analytes.  

One special consideration this year, if the panel recommended  a code on the fee schedule which has no parallel private payer PAMA data - CMS generally "nixed" that as a viable crosswalk, and kept looking.

Strong panel support—even unanimity—often does not overcome those CMS selection rules..

(BQ - And CMS prefers to work only from the actual text of the code, not other arguments the lab may have, like GB of sequencing, etc.)


MolDx: Final Gapfill Pricing for CY2027

The gapfill year has run its course for 17 codes. We saw proposed prices in May, final pries in September.  Five codes changed price (although one by only 5%).  Get the spreadsheet on this page, scrolling down to "agenda & important materials."

I've put a summary spreadsheet in the cloud.  I've included a fact-based "Tips and Tricks" section at bottom.



Chat GPT writes as follows:

CMS Final Gapfill Prices:
Five Increases Among 17 Tests

CMS’s 2026 gapfill pricing cycle, establishing rates for 2027, includes 17 test codes: two conventional CPT codes and 15 PLA codes. Five prices increased from proposed to final, while 12 remained unchanged. None decreased. The national median matches the MolDX price for each test.

The five increases range from 5% to 375%, with markedly different effects in dollars:

CodeTest, abbreviatedProposedFinalDollar increasePercent increase
81524CNS tumor methylation classification$1,995.69$2,500.00$504.3125.3%
87182Carbapenemase detection$9.65$31.75$22.10229.0%
0534UProstate cancer risk assessment$464.88$489.68$24.805.3%
0542URenal transplant allograft injury assessment$218.32$1,037.91$819.59375.4%
0597UBreast cancer recurrence risk, RNA and proteins$2,510.21$3,873.00$1,362.7954.3%

The largest dollar increase went to 0597U, up $1,363 per test. The largest percentage increase went to 0542U, whose final price is approximately 4.75 times the proposed amount. By comparison, 87182 more than tripled but gained only $22.10. The change for 0534U was modest: $24.80, or 5.3%.

MolDX supplied a rationale for each revision:

  • 81524: The increase better reflects similar existing services, specifically 0020M.

  • 87182: Updated information supported a higher price reflecting the resources required to perform the test.

  • 0534U: MolDX considered updated gapfill information, stated that the code descriptor does not accurately reflect the test performed, and used 0401U as a crosswalk based on comparable methodology.

  • 0542U: Newly provided gapfill documentation supported an increase reflecting the resources required to perform the test.

  • 0597U: MolDX moved away from 81520 as the pricing comparator, instead referencing 81518, 81519, and 81521 to better reflect typical rates for similar services.

The rationales fall into two practical groups: new information about testing resources supported two increases, while comparisons with existing services supported three. 

Most prices held steady, but revised documentation and a different choice of comparator produced substantial increases for several tests.

TIPS & TRICKS

Let's say you're sure your test is worth $3000 but MolDx initially priced at $2050.   Based on the rationales above, how should you approach an appeal?

Make the case for the missing $950 using documented resources and well-chosen comparators. The five rationales suggest two productive approaches, although they do not reveal which arguments were submitted unsuccessfully.

  1. Explain what the $2,050 valuation misses. Submit a clear accounting of the resources required to perform the test: labor, reagents, instrumentation, quality control, and analysis. Identify any omitted steps or incorrect assumptions in the original submission. For 87182 and 0542U, MolDX expressly attributed increases to updated information about required resources.

  2. Show why approximately $3,000 is the better comparison. Identify existing services with comparable methodology, complexity, and resource requirements. Explain why those services are better benchmarks than the apparent $2,050 comparator. The 0597U rationale is particularly useful: MolDX explicitly replaced one comparator with a different group and raised the price by 54%.

  3. Check whether the descriptor led to a misunderstanding. Explain precisely what the laboratory performs and how that maps to the code. For 0534U, MolDX flagged a mismatch between the descriptor and the actual test and selected a comparator based on methodology. That produced only a 5% increase, so clarification alone does not establish a $3,000 price.

The submission should make a reviewer’s decision straightforward: the current valuation, the specific assumption being challenged, the supporting evidence, and the calculation supporting the requested amount. A short comparison table and supporting documentation would do more work than a lengthy statement that the test is innovative or clinically valuable.

Moving from $2,050 to $3,000 requires a 46.3% increase—within the range of increases observed here. That establishes that a revision of this size is possible, not that it is likely. The strongest argument would show that both the resource evidence and the most appropriate existing comparators support approximately the same requested price.

Digital Pathology Update: Signals from Leica, from CAP TODAY, from Mayo

 [AI generated, Chat GPT 5.6]

[Links at bottom]

##

Future of Digital Pathology:
Signals from Leica, Mayo, and CAP TODAY

Digital pathology’s next major commercial advance may come when software can assemble a substantially complete pathology report for a physician to review, correct, and sign. That capability could give laboratories a powerful new reason to invest in scanners, image management, AI, and reporting infrastructure: a measurable reduction in the work required to complete a case.

Three recent developments illuminate the path toward that possibility. Leica Biosystems is expanding access to Tempus’s Paige applications within its digital pathology platform. Mayo Clinic Laboratories describes AI assistance across laboratory operations, including draft interpretive reporting. And a CAP TODAY discussion with Digital Pathology Association leaders describes a profession preparing for increasingly comprehensive AI support.

These developments occupy different stages of that trajectory. Together, they suggest that the important question is becoming how effectively digital tools contribute to a completed, clinically useful diagnosis.

Leica and Tempus: Bringing the Components Together

Leica Biosystems’ September 24 announcement adds two Tempus offerings to the Aperio AI Store: Paige PanCancer Detect and the Paige Prostate suite. PanCancer Detect is designed to identify suspicious areas across more than 40 cancer types. The prostate applications address detection, grading and quantification, and perineural invasion.

The announcement emphasizes access through a common workflow. Applications are available within Aperio HALO AP, reducing the need to navigate separate interfaces for different vendors and algorithms. The commercial proposition extends beyond the performance of any individual tool: laboratories need a practical way to bring multiple capabilities into daily work.

The regulatory scope is explicit. The newly announced Paige offerings are for research use only, not diagnostic or clinical use. Aperio HALO AP is also labeled for research use only in the United States. The announcement therefore describes an expansion of research capabilities, rather than a newly available clinical reporting system.

Nevertheless, the direction is significant. Detection, grading, tumor quantification, and assessment of perineural invasion are recognizable components of prostate case interpretation. Bringing them into one environment creates an opportunity to connect their outputs to a larger task.

That larger task is case assembly. A future system would need to associate findings with the correct specimens and cores, reconcile results across slides, identify missing information, and populate a draft report. Leica’s announcement does not claim that capability. It does show increasingly sophisticated analytical components becoming accessible within a shared workflow.

The next competitive question is how much work remains between those outputs and the report the pathologist signs.

Mayo: AI Moves Into Interpretive Drafting

Mayo Clinic Laboratories’ update, “From Intake to Interpretation: How AI Assists Lab Teams Today,” describes a broad operational approach. Applications include extracting information from incoming paperwork, supporting specimen routing, forecasting workload and blood-product demand, assisting with complex analytical signals, and quantifying immunohistochemical staining.

These uses matter because a laboratory’s performance depends on many connected tasks. Faster interpretation cannot fully compensate for delays in specimen intake, missing information, or poorly coordinated capacity.

For the future of reporting, however, the most consequential passage concerns draft interpretations. Mayo describes AI assembling multiple assay results and clinical details into a narrative that a pathologist reviews, edits, and signs. Christopher Garcia, MD, emphasizes bringing information together so that the physician does not begin with a blank page. Authorship and accountability remain with the signing expert.

This moves AI into the production of the laboratory’s professional work product. The benefit potentially includes less information gathering, less repetitive composition, and more consistent incorporation of relevant findings.

The distinction between this capability and comprehensive automated histopathology reporting remains important. Mayo’s account does not demonstrate a general system that reads an entire surgical pathology case and independently generates its complete draft report. Nor does it provide controlled estimates of productivity gains. It does establish that interpretive drafting is part of the institution’s described AI activity.

For the market thesis, that is a meaningful signal. Report assembly is becoming an explicit application category, with physician review built into the workflow.

CAP TODAY: Infrastructure Meets the AI Assistant

CAP TODAY’s September discussion with six Digital Pathology Association leaders places these developments in a broader professional context. Participants describe growing adoption driven by workforce pressures, remote work, access to subspecialists, and precision medicine.

Eric Walk, MD, of PathAI cites a late-2025 survey in which half of laboratories were digital in some capacity. That qualification matters: partial adoption is different from a fully digital diagnostic service. Even so, the discussion portrays a market increasingly concerned with making digital infrastructure productive.

Walk also anticipates AI agents that assist pathologists with primary diagnosis and case workup, drawing an analogy to the preliminary review residents provide in academic departments. Michael Rivers describes the opportunity to combine pathology, sequencing, and clinical information into broader decision support. These are visions of assistance extending across a case.

The panel also identifies substantial unfinished work. Memorial Sloan Kettering’s Orly Ardon emphasizes governance, metadata, quality systems, and accessibility. Performance can vary with patient populations, scanners, and specimen preparation. Bethany Williams highlights the value of data representing diverse practice settings, alongside the continuing challenge of institutional silos.

A second commercial opportunity runs through the discussion: linking diagnostic laboratories with pharmaceutical development, biomarker discovery, and clinical trial enrollment. Digital infrastructure may support both routine diagnostic work and new collaborations. Its value will depend on whether those connections produce useful information at the right point in patient care.

The Convergence: A Report Worth Reviewing

Our white paper at Discoveries in Health Policy from August 2026, proposes that substantially complete, machine-generated draft reports could become the application that sharply accelerates digital pathology adoption. The economic argument is straightforward: reducing the work required to complete cases could strengthen the laboratory’s business case for the entire digital infrastructure.

The three updates make that hypothesis more concrete. Leica illustrates the assembly and distribution of analytical capabilities. Mayo describes interpretive drafting. The CAP TODAY panel anticipates broader case assistance and identifies the infrastructure necessary to support it. None establishes that the commercial tipping point has already arrived.

Shuoshuo Wang’s 2026 article on the pathology report as a “boundary object” helps explain what successful reporting systems will require. A report serves several communities—pathologists, treating physicians, researchers, and others—whose members interpret it using shared knowledge and context. Its concise language can communicate effectively to an expert while leaving relationships insufficiently explicit for computational reuse.

Wang’s argument therefore adds a demanding qualification: extracting individual findings or filling structured fields is insufficient. Systems must preserve which finding belongs to which specimen, where the evidence originated, and what remains uncertain. An unmentioned finding cannot automatically be treated as negative.

Applied to report generation, the implication is clear. A useful draft needs an inspectable connection to the underlying case. Fluent prose alone provides little assurance that those relationships have been preserved.

What Is Needed Now

As analytical tools and drafting capabilities advance, integration becomes more urgent. Outputs need to reach the laboratory information system as usable information, with specimen identity and supporting evidence intact. Pathologists need an efficient way to verify proposed findings, correct them, and recognize unresolved questions.

Evaluation must also move toward the completed case. Relevant measures include total review and correction time, clinically consequential errors, completeness, turnaround time, and performance across different laboratories. A report that appears nearly finished may still require extensive checking; the meaningful endpoint is safe completion with a demonstrable net benefit.

Finally, expert corrections could become a valuable resource for improving future systems. That possibility requires deliberate capture of revisions, appropriate data permissions, and controlled validation of model updates. A signed report is valuable evidence, but it is not automatically an error-free training label.

The strongest market opportunity may belong to systems that make this entire process work reliably: assembling evidence, producing a useful draft, supporting efficient physician review, and delivering a report that others can confidently act upon. Leica, Mayo, and CAP TODAY provide complementary signals that the field is moving toward that objective. The next decisive evidence will concern how much better laboratories can complete the case.


##

.

  1. Leica Biosystems. “Leica Biosystems Brings Tempus Pathology Products to Aperio AI Store, Accelerating AI-Powered Capabilities for Oncology Research.” Press release. September 24, 2026. Official Leica release. Leica Biosystems

  2. Gilligan J. “From Intake to Interpretation: How AI Assists Lab Teams Today.” Mayo Clinic Laboratories, Insights. March 23, 2026. Full article. news.mayocliniclabs.com

  3. CAP TODAY. “DPA Leaders on Digitization, AI, and Deployment.” September 2026. Roundtable moderated by publisher Bob McGonnagle, with Orly Ardon, Nathan Buchbinder, David Lahm, Michael Rivers, Eric Walk, and Bethany Williams. Full interview. captodayonline.com

  4. Bruce Quinn Associates LLC. Digital Pathology Has Been Measuring the Wrong Endpoint? Beyond Srigley’s Six Levels: The Coming Billion-Dollar Hockey Stick. White paper. August 2026;   PDF link shared in your LinkedIn post.  Blog link. 

  5. Wang S. “The Pathology Report as a Boundary Object: From Clinical Communication to Computational Representation.” Precision Pathology. 2026;1:100002. Published online July 25, 2026. doi:10.1016/j.prpath.2026.100002. DOI link.  

Date of Service Rule for Inpatients: Does It Conflict with Other Regulations?

The Medicare Date of Service rule defines "date of specimen collection" as the "date of service" - for inpatients, unless the test is ordered over 14 days after the hospital discharge.

The way it works is this: The patient is admitted July 1, has surgery July 2, is discharged July 6.  A molecular test is ordered ten days later (July 16).  This July 16 is less than 14 days after July 6, the "DoS" is July 2 - the date of specimen collection.  Since "the date of service" (!) is during the inpatient stay, between the inpatient date of admission and date of discharge - the service is not payable; it is included in the DRG which covers all services from date of admission to date of discharge.

But: Does this make sense?  What if Medicare law defines inpatient stay or inpatient services as those which occur between the time of admission and the time of discharge?   Is it legitimate then for the little DOS regulation to throw an event (the molecular test) weeks into the past, before the test was even ordered?

CMS has touched on this in Medicare Advantage policy.  CMS found that Medicare Advantage plans were rejected good prior auth paper work because it was signed after the time of testing (e.g. that past date of service fiction).  CMS reprimanded such plans for bad behavior, and noted the DOS regulation does not actually move real events into the past.  (91 FR 20014, 4/14/2026, Some plans may improperly rely on the Part B laboratory date of service policy...the MA plan may deny the laboratory's prior authorization on the grunds that the DOS has already occurred, citing the DOS policy...it is wholly inconsistent with 42 CFR 422.138(b) to apply the DOS policy ...to deny a request...for services that have not yet been performed."  Similar, it is a fiction that the molecular lab test weeks in the future, took place, weeks in the past, during the span of time that legally defines the inpatient interval.

How shaky are the foundations on which DoS billing has been built?  I asked Chat GPT to write a detailed policy memo on the topic.  PDF link provided.

Report >> here.

See the recently convened "Alliance for Timely Biomarker Testing" here.


The main research run took AI 1m46sec.  I then reviewed for feasability, problems, surprises.
I then gave instructions for the actual article. 
AI writing the article by AI took 6m19sec.

##
Please draft a memorandum that briefly outlines the history of the DOS rule and its potentially adverse impact on inpatients due to delay of biopsy results to several weeks after discharge.   The original inpatient DOS rule is unchanged since 2007, twenty years and literally before the era of molecular oncology (FMI hadn't even been imagined in 2006-2007).   In M.A. rulemaking, CMS has noted that DoS is a fiction and should not be used to deny Prior Auth cases (e.g.).    In this memo, we discuss whether the use of DOS to treat future services, weeks in the future, as part of the DRG for an inpatient.  then discuss your various tables of findings on teh matter.  Take the stance of a Medicare policy attorney who both wants to write a memo that is meaningful (even impressive) to CMS attorneys, but also readable to a lay person (such as don Thompson, head of inpatient policy).   Be scrupulously polite but you know that when you combine things like the medicare advantage prior auth denial silliness with your various statutory and CFR citations, you've got a good argument that the DOS as applied to create DRG services where none exist, is sort of shaky.   Do not conclude the current use of DOS to put events weeks int the future into a DRG stay weeks in the past, is "illegal" but convey in conclusion that it raises a doubtful area of policy.   Your output is a single memo with various parts marked by headings or subheadings for readability.

Monday, September 28, 2026

The Distribution of PAMA Prices: Case Study 81455, 81456 (51 or more tumor genes)

CMS has released a new clin lab fee schedule for 2027-2028-2029.   Price cuts were common, based on reported private payer pricing data.  (Genomeweb here, HC Dive here.)

CMS released Excel files of roughly 7M claims (999,999 rows x 7 pages).  Find the data on this page under the header "CY 2027 Information" which includes preliminary data aka raw data and preliminary medians aka new fee schedule.

CMS does not release data for sole source tests or for tests with less than 10 uses.

Charting 81455, 81456

I took a deep dive on codes 81455 (51+ tumor genes, DNA only or DNA+RNA) and code 81456 (51+ tumor genes, RNA only).

For 81455, there were 19,088 price points submitted, across 1669 individual data rows.   For 81456, there were 14,731 price points submitted, but across only 526 individidual data rows. 

New pricing for 81455 fell only $58, from $2919 to $2861.   Price for 81456 is unchnaged in 2027 ($2919).

The distribution of private payer prices shows a tall peak acound the CMS fee schedule price, a prominent drift to the left (lower prices), and a very thin tail up to super-high prices (e.g. $15,000).

The AI Bar Charts

Here below are the bar charts, after binning the data in $500 increments.  For both tables below, click to enlarge.  For a zip file of the input and output data here.



Given what we saw in the data for 81455, I asked for a finer-grain view, in $100 bins from $2800 to $3100.  Here it is:


The data does NOT shown a Guassian curve around $2950.  Rather, there is a separate peak at 85% of the CLFS.   

Our of about 19,000 services, about 6000 were at $2950, and about 2500 were at around $2400 or 85%.

Together, about 45% (almost half) of 81455 payments fell either at the CLFS public reference price or, at 85% of it.

This bump at 85% was also seen in PAMA pricing for genetics as far back as 2016/2017.




How I did it.

I selected the data ranges for 81455 and for 81456 and put them in their own Excel's.  I then gave Chat GpT this prompt.   (What I am showing below is actually a cleaned up version of my hand written prompt.)

The attached spreadsheet contains CMS PAMA raw data for CPT code 81455, covering tumor genomic analysis of 51 or more genes. Each row lists the CPT code, a reported price, and the number of services reported at that price.

Please create a vertical bar chart using the following method:
  1. Group prices into uniform $500 bins: $0 to under $500, $500 to under $1,000, and so on through $14,500 to under $15,000. Add a final bin for $15,000 and above. 
    1. Each bin includes its lower boundary and excludes its upper boundary.
  2. For each bin, sum the reported number of services—not the number of rows or distinct prices. 
    1. For example, 5 services at $600 plus 2 services at $700 contribute 7 services to the $500–$999 bin.
  3. Display the price bins in ascending order on the X-axis and the total number of services in each bin on the Y-axis. Include bins with zero services.
  4. Verify that the sum of all bin totals equals the total number of services in the source data.
Provide the chart as a PNG image and an Excel (.xlsx) workbook containing the original data, calculated bin totals, and an editable chart.






Friday, September 25, 2026

LegislationWatch: ASAP (Alzheimer Tests), MAIA, PACA, PPA (all for Medicare Advantage Claims)

 ALZHEIMER ON THE HILL

On September 16, 2026, the House Ways and Means committee advanced a bipartisan Alzheimer bill, "Alzheimer's Screening and Prevention Act ASAP."

See a dedicated website: https://alzimpact.org/ASAP_Act   

It's numbered as, H.R. 6130 / S. 3267.  

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MEDICARE ADVANTAGE CLAIMS - ON THE HILL

Tired of Medicare Advantage payment nightmares?  Congress would like to help.   See Medicare Advantange Improvement Act of 2026, and Medicare Advantage Prompt Pay Act, and the Protecting Approved Care Act.

Read about MAIA-2026 here.  It's numbered as, HR 8375/S 4384. 

For the Prompt Pay Act PPA-2026, see HR5454 here.

Read about Medicare Advamtage "Protecting Approved Care Act" PACA here and here.  See the langhage here.  (It was just announced and may not be numbered yet.)

##

ALL ABOUT ALZHEIMER (ASAP)

Alzheimer’s Blood Tests: Congress Moves Toward Screening Coverage as Diagnostics Expand Access

The Alzheimer’s Screening and Prevention (ASAP) Act has advanced unanimously through the House Ways and Means Committee, highlighting a growing policy question: how should Medicare accommodate blood tests that can identify Alzheimer’s pathology earlier? Alongside the legislation, increasingly accessible diagnostic platforms could help more patients reach evaluation and treatment. These developments reinforce one another, but an important distinction remains: diagnosing patients with cognitive symptoms and screening people without symptoms involve different clinical evidence and coverage questions.

On September 16, the Ways and Means Committee voted 40–0 to advance H.R. 6130, the ASAP Act. The bipartisan legislation has a Senate counterpart, S. 3267. This is a significant committee milestone, although the legislation has not become law.

The introduced version would establish Medicare coverage for Alzheimer’s disease and related dementias early-detection screening tests furnished beginning January 1, 2028. It expressly includes detection at the presymptomatic and early stages, requires FDA clearance, classification, or approval, and adds the tests to Medicare’s clinical laboratory payment provision. An important version caveat: the supplied Congress.gov page records approval of a committee substitute, but the displayed legislative text is still the November 2025 introduced bill. These details therefore describe that version.

For diagnostics readers, the drafting is striking. Although promoted as a blood-test bill, it begins with a “genomic sequencing blood or blood product test,” then allows other equivalent technologies—including single-analyte tests and protein expression—as the Secretary determines appropriate. The list even includes medical imaging. The proposed statutory category is considerably broader than a single Alzheimer’s blood biomarker, although its sequencing-first structure is an unusual starting point for a field where protein biomarkers such as pTau217 are attracting attention.

The Alzheimer’s Impact Movement describes this as a “mammogram moment”: a chance for Medicare policy to accelerate adoption of earlier detection. That is an effective advocacy frame. However, the analogy expresses an aspiration; it does not itself establish that screening asymptomatic people for Alzheimer’s will produce benefits comparable to mammography. The clinical question remains what happens after detection—and whether that sequence of care improves outcomes.

A complementary commercial perspective comes from Yiqi Seow’s commentary on Roche and Lilly’s pTau217 collaboration. Seow argues that diagnostics can expand access to a therapeutic market by overcoming the difficulty of identifying eligible patients. When evaluation depends on PET imaging or lumbar puncture, diagnostic capacity can constrain treatment access. A blood assay deployed on widely installed laboratory instruments could ease that constraint. The contrast with oncology is not absolute, but the central insight is useful: diagnostic infrastructure can be essential to realizing a drug’s clinical and commercial potential.

Roche and Lilly’s Elecsys pTau217 test received FDA clearance in August for people aged 55 and older with cognitive decline. It helps assess Alzheimer’s-related pathology and must be interpreted with other clinical information; it is not a standalone diagnosis. That symptomatic population should not be conflated with population screening before symptoms appear. (reuters.com)

Taken together, these developments show Alzheimer’s diagnostics advancing on two fronts: the practical capacity to test more patients and the proposed Medicare authority to cover screening earlier in disease. Their convergence could reshape access. The policy challenge is to connect each testing use to a clearly defined population, a meaningful next clinical step, and evidence that earlier knowledge improves care.

ASAP Act: Congress.gov

###

###

ALL ABOUT MEDICARE ADVANTAGE

Three Medicare Advantage Reform Proposals: Faster Decisions, Reliable Approvals, and Timely Payment

[For links see top of blog].

Three legislative proposals address different parts of the same Medicare Advantage problem: obtaining authorization, relying on that authorization once care is delivered, and getting paid afterward. The Medicare Advantage Prompt Pay Act focuses on payment deadlines. The Protecting Approved Care Act targets retrospective denials and payment reductions. The broader Medicare Advantage Improvement Act combines administrative reforms with coverage standards and financial accountability. Together, they would make plans more accountable for how coverage works in practice.

The discussion below reflects the supplied legislative versions: two introduced bills and an unnumbered September 2026 draft of the Protecting Approved Care Act. Their proposed requirements should not be mistaken for current law.

The Prompt Pay Act: Put a clock on payment. H.R. 5454, introduced by Representatives Jodey Arrington and Linda Sánchez, would require Medicare Advantage organizations to pay at least 95% of clean claims within specified deadlines, covering both contracted and noncontracted providers. Electronic claims from contracted providers would have a 14-calendar-day deadline; other claims would have a 30-calendar-day deadline.

The bill also defines a clean claim through standardized billing-data requirements, establishes presumptions for when a claim was received, requires interest on late payments, and authorizes civil monetary penalties of up to $25,000 per determination of noncompliance. Reporting would disclose payment timeliness and interest paid. Its proposed effective date is January 1, 2027. The central idea is straightforward: make timely payment a measurable federal obligation across provider relationships.

The Protecting Approved Care Act: Make coverage decisions dependable. The supplied Landsman draft would restrict retrospective medical-necessity denials, reopening of coverage or payment determinations, and downcoding that reduces payment. It preserves specified exceptions for reopening and downcoding, including regulatory “good cause” and reliable evidence of fraud or similar fault. Its proposed requirements begin with 2028 plan years.

One especially consequential provision extends protections to covered services for which the plan requires no authorization. That reaches beyond the familiar argument that a plan should honor its prior approval: it would also limit retrospective challenges when the plan did not require an approval process in the first place. A drafting detail merits attention—the definition includes prior and concurrent authorizations, while the operative approval clause refers to authorization made “during” receipt of care. That wording warrants clarification before assuming every preservice approval receives identical protection.

The Medicare Advantage Improvement Act: Connect access, payment, and enforcement. H.R. 8375, led by Representative John Joyce with bipartisan cosponsors, is the most comprehensive proposal. Its principal reforms, generally beginning in 2028, include:

  • Faster authorization: generally 72 hours for standard requests and 24 hours for expedited requests, with specified extensions and timing qualifications.

  • Less repetitive administration: real-time authorization capabilities for designated services and restrictions on requiring another authorization for clinically necessary changes to approved care.

  • Stronger payment protections: qualifying claims documenting authorization would be deemed clean, with a 100% prompt-payment standard, alongside restrictions on retrospective denials, downcoding, and third-party reviews.

  • Coverage accountability: medical-necessity criteria no more restrictive than traditional Medicare’s, public criteria where Medicare guidance is absent, and additional hospital and post-acute-care protections.

Its enforcement provisions are particularly notable. A new compliance score would trigger 1%, 1.5%, or 2% reductions in plan payments for progressively lower performance below a score of 90. A separate compliance and coverage-protection domain would enter Star Ratings, with its measures weighted more heavily than those in other domains. Administrative behavior would therefore affect the plan’s own revenue.