The MAC DOS Article: Two AIs Compete to Do Better (Than MACs)

The MAC DOS Article: Two AIs Compete to Do Better (Than MACs)

After hearing complaints that the Lab DOS rule should have uniform explanations and enforcement at all MACs, CMS asked the several MACs to produce a new education article on DOS.  (Novitas-FCSO wrote its own.)

Below, I show the MAC example (from Wellpoint).   

Then Chat GPT, and CLAUDE, produce their own articles, which took a minute or two.

Then Chat GPT, and CLAUDE, "critiqued' their own work and compared to Wellpoint.

Finally, Claude produced a second version of the article, hopefully taking the best of each prior attempt.

01 Wellpoint Concensus Artice on Lab DOS - June 24, 2026  explaining 42 CFR 414.510.

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02 Chat GPT DOS Article Version 1 astra.docx

03 CLAUDE The Lab Date of Service Rule.docx

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04 CHAT GPT Offers a 3 way comparison.docx

05 CLAUDE Offers a 3 way comparison.docx

06 CLAUDE Writes Version 2 Composite Article 

(Also clipped below)

The question is, how does one view File #1 (MACs) versus File #6 (second AI effort)

 MAC word count (2505), Chat Ver 1 (1298), Claude Ver 1 (1558), Claude Ver 2 (1887.)

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COMPOSITE REWRITE BY CLAUDE,

MERGING CLAUDE Ver 01 and CHAT Ver 01

 

 

The Lab Date of Service Rule, Explained Plainly

A guide for hospital and laboratory billing staff, working from 42 CFR 414.510

CLAUDE (version 2)

For most laboratory tests, Medicare's date of service is the date the specimen was collected. Testing the sample later, sending it to another laboratory, or issuing the report later does not change that date. A different date applies only when the test meets one of the specific exceptions in 42 CFR 414.510.

The rule covers clinical laboratory tests and the technical component of pathology services. It does not set the date of service for the pathologist's professional interpretation. [§414.510(a)]

Why the date matters

Lab tests rarely happen on a single day. A test may be ordered on Monday, collected on Tuesday, stored for three weeks, run on a Thursday, and reported the following Monday. Medicare picks one of those dates, and that choice decides who bills.

If the date of service falls within a hospital stay or a hospital outpatient encounter, the test generally belongs to the hospital's claim. The hospital bills Medicare, and the performing laboratory looks to the hospital for payment. If the date falls after the encounter, the performing laboratory generally bills Medicare directly under the Clinical Laboratory Fee Schedule. The date also decides which year's fee schedule applies and whether the patient was in fee-for-service Medicare or Medicare Advantage on that day. A wrong date means the wrong entity bills, or the claim is priced or routed incorrectly.

The rule has one default and a short list of exceptions. They are covered below in the order the regulation gives them.

The default: the collection date

The date of service is the date the specimen was collected. [§414.510(a)] This covers most tests: a blood draw in a physician's office, a urine sample at a patient service center, a biopsy processed and read the same week. For example, if blood is collected on June 3 and an independent laboratory tests it on June 5, the date of service is June 3. Hospital specimens start from the same rule, and where the testing lab is located does not affect the date.

Exception 1: Collection spanning two days

If collection spans two calendar days, the date of service is the date collection ended. A 24-hour urine collection that begins June 3 and ends June 4 has a June 4 date of service. [§414.510(b)(1)]

Exception 2: Stored specimens

This exception is what people usually mean by "the 14-day rule." The outcome depends on how long the specimen was stored before testing.

Stored 30 calendar days or fewer. The date of service is the date the test was performed, but only if all five of these conditions are met: [§414.510(b)(2)(i)]

1. The test was ordered by the patient's physician at least 14 days following the date of the patient's discharge from the hospital.
2. The specimen was collected while the patient was undergoing a hospital surgical procedure.
3. It would be medically inappropriate to have collected the sample other than during the hospital procedure for which the patient was admitted.
4. The results of the test do not guide treatment provided during the hospital stay.
5. The test was reasonable and medically necessary for the treatment of an illness.

If any one of these conditions fails, the exception does not apply. The date of service reverts to the collection date, which falls within the hospital stay, and the hospital bills. Meeting the 14-day order requirement is only one of the five conditions. In practice, though, it is usually the one that decides the case. The 14 days run from discharge to the physician's order. Waiting longer before performing or billing the test does not make an earlier order qualify. CMS has long applied this exception to hospital outpatient surgical specimens as well as inpatient ones, with outpatient discharge serving as the starting point.

For example, tissue is collected during inpatient surgery on June 1 and the patient is discharged June 5. The physician orders a test on June 19, exactly 14 days after discharge, and the laboratory performs it June 22. The specimen was stored less than 30 days. If the other four conditions are met, the date of service is June 22 and the laboratory bills. If the physician had ordered the test on June 12, the date of service would be June 1 and the hospital would bill.

Stored more than 30 calendar days. The specimen is considered archived, and the date of service is the date the specimen was obtained from storage. [§414.510(b)(2)(ii)] This provision has no 14-day requirement and no other conditions. Note that 30 days exactly is not "more than 30." Also note that retrieval and testing are separate events. If a tumor block from an inpatient surgery is stored for six months, retrieved on December 4, and tested on December 7, the date of service is December 4. Billing staff therefore need both dates.

Exception 3: Chemotherapy sensitivity tests on live tissue

This is a narrow category for tests, identified by CMS through program instructions, that use fresh tissue to measure how tumor cells respond to specific chemotherapy drugs. [§414.510(b)(3)–(4)] It does not cover every test used to choose cancer treatment. For these tests the date of service is the date performed if the same five conditions apply, with one change: the 14-day clock runs from discharge to the decision about which chemotherapy agents to test, not to the order. Most billing staff will rarely encounter this exception.

Exception 4: Advanced tests on hospital outpatients

This exception, added in 2018 and broadened in later rulemaking, is the one that matters most for genomic and precision-medicine testing. It covers four categories of tests: [§414.510(b)(5)]

• molecular pathology tests performed by a laboratory other than a blood bank or blood center;
• ADLTs designated under paragraph (1) of the ADLT definition in §414.502 (the multi-biomarker, algorithm-based kind, not the FDA-cleared kind under paragraph (2));
• cancer-related protein-based MAAAs; and
• the test described by CPT 81490.

For these tests the date of service is the date the test was performed if all five of these conditions are met:

1. The test was performed following the patient's discharge from the hospital outpatient department.
2. The specimen was collected from a hospital outpatient during a hospital outpatient encounter, as both terms are defined in 42 CFR 410.2.
3. It was medically appropriate to have collected the sample during that encounter.
4. The results of the test do not guide treatment provided during that encounter.
5. The test was reasonable and medically necessary for the treatment of an illness.

People often miss two points about this exception.

First, there is no 14-day waiting period. The regulation does not require the physician to wait until after discharge to order the test. It requires only that the test be performed after discharge. For example, a patient has an outpatient biopsy and is discharged on June 3. A listed molecular test is ordered the same day and performed June 6. The date of service is June 6, and the performing laboratory bills Medicare directly. CMS's own explanation states that the test is separated from the hospital outpatient encounter, and the laboratory does not bill the hospital. Ending the practice of making hospitals bill for expensive single-source tests they had no part in was the reason for this exception.

Second, it applies to outpatients only. Discharging an inpatient and sending the specimen to an outside laboratory does not make it an outpatient specimen. Inpatient specimens go back to Exception 2, including its 14-day requirement.

Whether a test qualifies is a question of category, not marketing. A test is not covered just because someone calls it "advanced." CMS publishes and periodically updates a list of qualifying HCPCS codes. Check the list instead of guessing whether a particular PLA code is on it.

Putting it together

For any claim, work through these questions in order:

1. Was the specimen stored more than 30 calendar days before testing? If so, the date of service is the date it was retrieved from storage.
2. Was the specimen collected during a hospital inpatient stay or a hospital outpatient encounter? If not, the date of service is the collection date (or, for a two-day collection, the end date), and you are done.
3. Is it a hospital outpatient specimen, and is the test on the CMS list? If so, and all five Exception 4 conditions are met, the date of service is the date performed. The order date does not matter.
4. Was the specimen collected during a hospital surgical procedure, and was the test ordered at least 14 days after discharge? If so, and the other four Exception 2 conditions are met, the date of service is the date performed. For live-tissue chemotherapy sensitivity tests, measure the 14 days to the drug-selection decision instead.
5. If none of these apply, the date of service is the collection date, and the test belongs on the hospital's claim.

Three common misreadings

"The rule is collection date unless the test is ordered 14 days after discharge." This shorthand is common and incomplete. The 14-day order requirement is one of five conditions in a single exception. Meeting it does nothing if any of the other four conditions fails, and it is irrelevant to Exception 4.

Mixing up the 14-day clock and the 30-day clock. These clocks measure different things. The 14-day clock runs from discharge to the physician's order. The 30-day clock runs from collection to testing and determines whether a specimen is archived. Some published flowcharts merge them into a single timeline, such as "ordered within 15–29 days of discharge." That timeline is not in the regulation, and it produces wrong answers.

Applying the 14-day wait to outpatient molecular tests. For listed tests on hospital outpatients, Exception 4 has no requirement about when the test is ordered. Checking the 14-day rule first sends claims to the hospital that belong to the laboratory.

A note on "date performed"

Section 414.510 uses the phrase "the date the test was performed" but does not define it. Some contractors, including Wellpoint Federal (J6/JK), have said they treat the report date as the date performed. That is a workable convention, but it is contractor guidance, not regulation, so confirm how your own MAC handles it. Record the testing date and the report date separately.

Documentation and limits

Hospital and laboratory staff need the same facts: collection date and setting, discharge date, order date (or drug-selection date), test category, storage and retrieval dates where relevant, and testing date. When you rely on an exception, the record should also support its clinical conditions. The pathology or laboratory report is usually where an auditor will look for these facts.

Two final cautions. When an exception applies, the date it sets is mandatory. You cannot pick whichever date gives the preferred billing result. And a correct date of service does not by itself establish coverage, separate payment, or a particular entity's right to submit the claim. Those questions come next, under their own rules.

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Sources: 42 CFR §414.510 (eCFR) · CMS, Laboratory Date of Service Policy and qualifying test code list

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A few notes on this version. I avoided giving a date for the later rulemaking ("broadened in later rulemaking") because I couldn't confirm when the MAAA and 81490 additions took effect; if you know the rule years offhand, add them back. I replaced the "CMS and several MACs" attribution with "this shorthand is common." And I moved the archive question to the front of the sequence, because §414.510(b)(2)(ii) isn't limited to hospital specimens.

 

The question would be, what does a person think of File #1(MACs) versus File #6.(AI combined ver 2).

PAMA and Codes for Exome, Genome, and "Rapid Genome"

 

PAMA 2027: Genome Prices Fall Below Exome—and Rapid Genome Codes Have Missing Data

CMS’s preliminary PAMA results point toward a notable reversal in genomic testing reimbursement: standard genome sequencing has a lower private-payor median than exome sequencing.

The broader genome assay currently receives higher Medicare payment. Under the new medians, genome sequencing would ultimately receive substantially less. However, Medicare’s annual reduction limit delays the payment crossover: genome remains more expensive in 2027, with the genome-exome price reversal  projected to begin in 2028.

Rapid and ultra-rapid genome sequencing present a different situation. Several established proprietary codes have current Medicare prices, but no private-payor medians in the preliminary workbook. A newly announced rapid genome code, effective January 2027, adds another pricing question. (It will enter pricing meetings in June 2027).

Article by Chat GPT.

Standard exome and genome: different reductions, reversed targets

The central comparison involves 81415, exome sequence analysis, and 81425, genome sequence analysis.

Code	Service	2026 Medicare rate	Preliminary private-payor median	Median versus 2026	Preliminary 2027 payment considering  cap
81415	Exome sequencing	$4,780.00	$3,680.60	−23%	$4,063.00
81425	Genome sequencing	$5,031.20	$3,018.72	−40%	$4,276.52

These figures appear in CMS’s preliminary weighted-median and payment-calculation workbook, released September 21, 2026.

In 2026, genome sequencing receives $251.20 more than exome sequencing. The new medians reverse that relationship: genome’s median will reach $3018,  is $661.88 lower, approximately 18% below exome’s median.

That is a pricing inversion worth distinguishing from the cuts themselves.

Why genome still pays more - just in 2027

The private-payor median is the pricing target, but Medicare does not necessarily move to that target immediately.

Current law limits annual payment reductions to 15% for 2027 through 2029. Both codes qualify for the full 15% reduction in 2027. Because both start above their medians, their first-year payments retain the existing relationship: $4,276.52 for genome versus $4,063.00 for exome.

Applying the reduction limit to subsequent years illustrates when the inversion would reach actual payment.

Year or pricing target	Exome, 81415	Genome, 81425	Genome minus exome
2026 current rate	$4,780.00	$5,031.20	+$251.20
2027 preliminary capped payment	$4,063.00	$4,276.52	+$213.52
2028 projected payment	$3,680.60	$3,635.04	−$45.56
2029 projected payment	$3,680.60	$3,089.78	−$590.82
Current preliminary median target	$3,680.60	$3,018.72	−$661.88

The 2028–2029 figures are calculations assuming the preliminary medians and current reduction limits remain unchanged. They are not separately announced CMS rates.

Exome reaches its median in 2028, while genome continues declining. Consequently, genome becomes slightly less expensive in 2028 and markedly less expensive in 2029.

The important distinction is therefore between an inversion already visible in the medians and an inversion delayed in Medicare payment by the phase-in rules.

Different reporting populations—and no QC change

The two medians also arise from different reporting populations. Before additional quality-control exclusions, CMS reports:

Reported private-payor data	Exome, 81415	Genome, 81425
Reporting entities, measured by TIN	30	12
Reporting laboratory NPIs	30	13
Reported test volume	14,666	2,164

These are private-payor volumes submitted for PAMA pricing, not Medicare utilization or spending.

The populations may differ in laboratories, contracts, payer mix, and payment distributions. The summary data do not identify which factors explain genome’s lower median.

Additional QC changes neither median. These are minor CMS adjustments for duplicate reporting.  Exome remains $3,680.60, and genome remains $3,018.72. The price reversal survives the additional data cleaning.

Rapid genome codes: current prices, missing medians

The rapid-sequencing codes should be distinguished carefully. 0094U describes rapid genome analysis; 0425U describes each rapid comparator genome, such as a parent’s or sibling’s; 0426U describes ultra-rapid genome analysis. Newly announced 0699U describes another proprietary rapid genome service.

Code	Service, abbreviated	Code effective date	2026 Medicare rate	Preliminary PAMA median
0094U	Rapid genome analysis [Rady]	July 1, 2019	$7,582.20	No data obtained
0425U	Rapid analysis, each comparator genome	January 1, 2024	$4,094.39	No data obtained
0426U	Ultra-rapid genome analysis	January 1, 2024	$7,582.20	No data obtained
0699U	Phoenix Children’s rapid genome analysis	January 1, 2027	Not effective in 2026	Not in the workbook

The effective dates are documented in CMS code-update materials and AMA’s October 2026 PLA release.

0094U "Rapid" currently receives $2,551 more than standard genome sequencing—approximately a 51% premium.  MolDx set this by the Gapfill method several years ago.  Ultra-rapid code 0426U has the same current rate as 0094U. The $4,094.39 rate for 0425U applies to each comparator genome, rather than representing an alternative price for the patient’s primary genome analysis.

The established codes are associated with Rady Children’s Institute for Genomic Medicine’s services. They should not all be labeled “ultra-rapid”: that designation specifically belongs to 0426U.

A blank median does not establish the 2027 payment

For 0094U, 0425U, and 0426U, CMS’s workbook explicitly indicates that it obtained no private-payor data. The median cells and corresponding 2027 payment calculations are blank.

Those blanks establish a limitation of this dataset. They do not establish a zero payment, a permanent exemption from PAMA, or automatic continuation of the current price.

CMS has prevented any codes with blank PAMA data from being used as crosswalk codes.

CMS states that existing tests without applicable information are considered through crosswalking or gapfilling. Crosswalking uses comparable existing tests to establish payment; gapfilling develops prices through Medicare contractors. The no-data codes therefore require attention to CMS’s separate payment determinations, rather than an assumption drawn from empty spreadsheet cells.

Genome codes 0658U, 0659U

Both of these new codes are in the summer crosswalk/gapfill new code process (not PAMA), both have CMS re commendations for gapfill in 2027. As noted, 0699U comes even later, for initial price discussion June 2027.

A new rapid genome code enters in January

AMA’s October 2026 release introduces 0699U for Phoenix Children’s Clinical Genomics Laboratory, effective January 1, 2027. AMA identifies its clinical descriptor as identical to that of 0094U and adds reciprocal cross-references between the codes.

The separate code reflects the proprietary service and laboratory identification. An identical clinical descriptor does not itself establish an identical Medicare price. Because 0699U becomes effective in 2027, it has no 2026 rate in the workbook and no code-specific median in this PAMA dataset.

CMS expects to publish final 2027 rates in November. Standard exome and genome already have a clear preliminary trajectory: both decline, and genome’s lower median ultimately reverses their payment relationship. For the rapid and ultra-rapid proprietary services, current prices are known, but the missing medians leave their next payment determination as a separate issue to follow.

Source workbook for the numerical comparisons: 

https://www.cms.gov/medicare/payment/fee-schedules/clinical-laboratory-fee-schedule/clfs-pama-reporting-resources

https://www.cms.gov/files/zip/cy-2027-preliminary-weighted-median-2027-clfs-rate-calculations-raw-vs-trimmed-rates.zip

Mammography AI Sold Straight to Consumers! (Parallels in Labs?)

Everywell will be the agent for seeling Clairity Breast mammography software results to consumers, according to a press release.   

In the future, will patients be able to upload a pathology needle biopsy DICOM and VISA card and get AI assistance?  To be continued.

I'll clip the press release below.

https://radiologybusiness.com/topics/artificial-intelligence/health-and-wellness-company-begins-selling-mammography-ai-directly-consumers-249

Health and wellness company begins selling mammography AI

directly to consumers for $249

• Marty Stempniak | October 09, 2026 | Radiology Business | Artificial Intelligence
• A health and wellness company has begun selling a key mammography artificial intelligence solution directly to consumers. 

Austin, Texas-based Everlywell recently announced the nationwide rollout and commercial availability of Clairity Breast to its users. Founded in 2015, Everlywell sells lab testing kits and digital diagnostic solutions to patients, “transforming care” for some 60 million people. 

The company said it is now offering Clairity Breast for $249 a pop, allowing women to upload their mammogram to the platform. Authorized by the Food and Drug Administration in 2025, the solution predicts patients’ five-year risk of developing breast cancer based on a routine screening. 

"Women deserve clear answers about their health, and access to the most advanced tools in healthcare, not years from now, but today. That belief is why I built Everlywell, and it's why we're so proud to partner with Clairity Breast,” entrepreneur Julia Cheek, founder and CEO of Everlywell, said in a statement Oct. 1. “This partnership expands access to cutting-edge preventive care, and that means a woman can learn more about her future breast cancer risk from the mammogram she already gets and walk into her doctor's office with information she's never had before."

Paired with Everlywell’s nationwide reach, those involved believe the partnership will help give women more info about their health and risk of cancer. They note that about 85% of breast cancers occur in women with no family history of the disease. 

First AI Tool with FDA De Novo for Breast Cancer Risk

Clairity was founded in 2020 by radiologist Constance “Connie” Lehman, MD, PhD, a Harvard professor of radiology and former chief of breast imaging at Massachusetts General. Last year its flagship product became the first AI-based tool to receive the FDA’s De Novo authorization for predicting breast cancer risk. See FDA home page and review here as DEN240047.

It has scored additional wins since then, including updated National Comprehensive Cancer Network guidelines introducing the product as an option for identifying women at increased risk beginning at age 35. 

• [Note that this software predicts cancer risk based on a non-cancer mammogram; there are also software products to predict metastasis or relapse in women with a breast cancer H&E biopsy, e.g. "Oncotype via DICOM."]
• [I believe FDA does not allow sales of DTC RUO/LDT lab tests to consumers; a reason 23andMe got FDA review several years ago.]

The two companies note that Clairity Breast has had limited availability through health systems such as Beth Israel Deaconess Medical Center in Massachusetts and Invision Sally Jobe in Colorado. With the new distribution deal, patients across the U.S. with a qualifying recent mammogram from the past year can access the risk assessment tool via Everlywell’s platform. This includes review and >>>> ordering by a licensed provider in Everlywell’s affiliated clinician network and results reviewed and released with next steps. 

“The information we need to understand a woman's breast cancer risk has been in her mammogram all along—we just haven't been able to interpret it until Clairity Breast. Now, it can also inform conversations with your healthcare provider about the best screening and risk reduction options for you,” Dr. Lehman said in the announcement. 

The companies are encouraging individuals to share the results with their own providers to “inform her screening and risk reduction plan.” Clairity joins other cancer screening options in the Everlywell platform, including an at-home collection colon cancer screening kit and recently launched HPV self-exam option for cervical cancer screening. 

STAT News also highlighted the new offering in a report published Thursday. It notes that this is part of a growing trend of preventive medicine products being consumerized online. However, physicians are expressing concern about the idea of putting the onus on individual patients to pay for a new test whose real-world impact has not yet been studied.  Firewall at Stat Plus.  Writing, "Clairity’s launch on Everlywell marks the first time an AI-based imaging algorithm has used telehealth to reach self-pay patients at a national scale — part of a growing trend of preventive medicine products being consumerized online."  This STAT PLUS article is #5 in a series.

Bonus:
500 word AI summary of 22-page FDA Decision Summary for DEN240047. 

FDA granted De Novo request DEN240047 for Clairity’s Allix5, an artificial intelligence software device that estimates five-year breast cancer risk from screening mammograms. The decision establishes a Class II device category, “radiological software device to predict future breast cancer risk,” under 21 CFR 892.8500, product code SEZ.

Intended use and workflow. Allix5 analyzes four standard, directly acquired 2D mammographic views from compatible Hologic Lorad Selenia and Selenia Dimensions systems. Synthetic 2D images are excluded. The software returns a numerical probability of breast cancer diagnosis or development within five years, without adjusting for competing mortality. Eligible patients have no known breast cancer at screening. Results are considered after the radiologist completes interpretation; the device is not intended to detect or diagnose cancer, recommend care, or serve as the sole basis for clinical decisions.

Clinical evidence. The pivotal retrospective study evaluated 77,511 examinations from 44,112 patients at five U.S. sites independent of algorithm development. Cancer outcomes were biopsy-confirmed, and statistical methods addressed incomplete follow-up and uncertainty about cancer onset. The study assessed both discrimination—distinguishing patients with different outcomes—and calibration—agreement between predicted and observed risk.

Overall five-year discrimination was AUC 0.70 (95% CI, 0.69–0.72), exceeding a prespecified performance goal of 0.64 informed by traditional risk models. After excluding cancers diagnosed within 90 days following an abnormal index screening assessment, AUC declined to 0.66 (95% CI, 0.66–0.68) but still met the goal. Both populations met the study’s aggregate calibration criterion. This was comparison against a performance benchmark, rather than a direct head-to-head demonstration of superiority over conventional models.

Important limitations. Acceptable aggregate calibration did not ensure accuracy across all risk levels or subgroups. Scores above 5% may substantially overestimate risk, particularly above 10%. Acceptable calibration was not demonstrated in Asian patients, patients younger than 50 or aged 65 and older, or patients with breast implants. Effectiveness in patients with implants was not established. FDA nevertheless found clinically acceptable performance in several other groups, including Black and White patients and patients with dense breasts.

Regulatory significance. FDA concluded that probable benefits outweighed risks with general and special controls. These require independent clinical validation, discrimination and calibration analyses, subgroup and reproducibility testing, software validation, appropriate labeling, and ongoing postmarket performance monitoring, including communication of current performance to users. The evidence supports risk prediction; the retrospective study did not establish that using Allix5 improves patient outcomes.

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Pubs

AI found this 2022 publication but AI stressed it does not seem to be the same thing as the May 2026 FDA product ["Mirai" here as Yala et al, "Allix5" at FDA]:

Yala A, Mikhael PG, Strand F, et al. Multi-Institutional Validation of a Mammography-Based Breast Cancer Risk Model. Journal of Clinical Oncology. 2022;40(16):1732–1740. Published online November 12, 2021. doi:10.1200/JCO.21.01337. PMID: 34767469

AI also found this insurer quotation, which I insert:

Arkansas Blue Cross and Blue Shield. Mammography-Based AI (Image-only) Risk Prediction Models. Coverage Policy 2026013. Last reviewed September 2026; effective January 1, 2027.

Its Rationale section states that the literature search was updated through May 1, 2026, and reports:

“No published studies reported the validation of the mammography-based AI risk predication model, Clairity Allix5, only the pivotal study that led to the De Novo authorization.”

NCCN

AI found that NCCN seems to endorse the free population based risk calculator (with a cut off of 1.7% x 5 years) or AI to do a similar calculation.  Quoting methods 4 and 5, "(4) those over 35 years of age with a 5 year risk of invasive breast cancer >1.7% per Gail model, (5) same "as calculated by an imaging based risk model." 

https://www.nccn.org/professionals/physician_gls/pdf/breast-screening.pdf

 

PAMA and PAP Smears: Strange Goings-On

PAMA 2027: When More Cytology Work Brings Less Payment

Article by : Chat GPT

CMS’s preliminary laboratory pricing data for 2027 offer an unexpected lesson in cervical cytology reimbursement. Adding manual review to an automated Pap screening service could result in lower payment. Two conventional manual-screening codes, distinguished principally by reporting terminology, have dramatically different private-payor medians. Meanwhile, an unusual statutory payment floor prevents several low medians from becoming Medicare fees.

The findings illustrate why laboratory prices deserve examination both individually and in relation to neighboring codes.

Automated screening: the price relationship reverses!

CPT 88174 describes automated screening of a cervical or vaginal specimen prepared using an automated thin-layer method. 88175 adds manual rescreening or review. Both operate under physician supervision.

The current Medicare rates follow an intuitive relationship: the service incorporating additional manual work receives slightly more payment. The preliminary 2027 medians reverse that relationship.

Code	Service, abbreviated	2026 Medicare rate	Preliminary 2027 median	Change from 2026
88174	Automated screening	$25.37	$27.43	+8.1%
88175	Automated screening plus manual rescreening or review	$26.61	$24.86	−6.6%

The additional manual review currently receives a $1.24 premium. Under the new medians, the more extensive service would receive $2.57 less.

This establishes a payment inversion, rather than demonstrating that either service becomes unprofitable. The data do not measure laboratory costs or margins. Nevertheless, the prospect of receiving less payment for the service with additional manual work is striking.  And 88175 is more commonly used than 88174.

These figures come from CMS’s preliminary weighted-median workbook, released September 21, 2026. They are preliminary medians, rather than final Medicare payment determinations. However, (1) both exceed the current Pap payment minimum of about $18, and (2) the decrease for 88175 is below the PAMA 15% annual reduction limit. So if CMS retains the medians, the reversal appears likely to carry into payment.

The two medians represent very different populations

The reporting data reveal a substantial imbalance.

Private-payor data before additional QC	88174	88175
Reporting entities, measured by TIN	19	480
Reporting laboratory NPIs	20	730
Reported test volume	20,956	3,711,248

The reported volume for 88175 is approximately 177 times greater. These are private-payor tests submitted for PAMA pricing. They are not Medicare claim counts, Medicare expenditures, or estimates of all Pap testing nationwide.

The difference does not establish that either median is wrong. It does show that the two prices arise from substantially different reporting populations. Laboratories, payer contracts, negotiated discounts, and volume distributions could differ between those populations. The summary tables cannot establish which factors produced the reversal.

PAMA calculates a volume-weighted median independently for each code. That process can produce a valid median for each submitted distribution without preserving the expected price relationship between related services. A private-payor median also does not establish the resources required to perform the service.

CMS Quality Control does not resolve the inversion

CMS published medians calculated before and after additional "quality-control exclusions."  These are meant to remove duplicate reports from the same TIN/NPI and erase bizarrely high values (100X price).  The change in PAMA median is usually 0 or a few pennies.  Neither median changes: 88174 remains $27.43, and 88175 remains $24.86.

For 88175, the reporting-TIN count falls from 480 to 479 after QC, but its median again stays the same. The reversal therefore survives the additional data cleaning.

A related screening code, G0145, illustrates how small the QC effect can be. Its median changes from $22.84 to $22.79, a five-cent difference downward. Compared with its 2026 Medicare rate of $26.49, the adjusted median is $3.70 lower, or approximately 14%.

Manual screening introduces further surprises!

The manual-screening codes broaden the story beyond a single automated pair.

Code	Service, abbreviated	2026 Medicare rate	Preliminary 2027 median	Change from 2026
88142	Liquid-based preparation, manual screening	$20.26	$20.26	0%
88143	Liquid-based preparation, manual screening and rescreening	$23.04	$30.00	+30.2%
88150	Conventional smear, manual screening	$18.54	$29.55	+59.4%
88164	Conventional smear, manual screening, Bethesda reporting	$18.54	$10.72	−42.2%

These percentages compare preliminary medians with current Medicare rates. They are not necessarily the payment changes Medicare will implement.  Note that $10.72 is below the $18.54 legal floor so $18.54 would be used.  (See below)

Holding the liquid-based preparation method constant provides another revealing comparison. Manual screening and rescreening, 88143, currently receives less than automated screening with manual review, 88175: $23.04 versus $26.61. Their preliminary medians reverse that relationship to $30.00 versus $24.86.

The reporting populations differ here as well. The 88143 median rests on 2,880 reported tests from 15 TINs, compared with 3.7 million tests from 480 TINs for 88175.

Bethesda reporting: a large price drop for comparable screening work

The conventional-smear comparison is particularly curious. 88150 and 88164 currently receive the same Medicare payment—$18.54—yet their new PAMA medians differ by $18.83.

Both describe manual screening. The distinguishing feature is principally the reporting system. Bethesda standardizes terminology and specimen-adequacy reporting; it does not substitute a fundamentally cheaper screening technology. Separate Bethesda reporting codes are longstanding: AMA’s discussion of the 1999 CPT changes identifies the introduction of codes 88164–88167.

On the unadjusted medians alone (see below!), Bethesda reporting would have been associated with approximately 64% less payment than the neighboring manual-screening code.

That comparison comes with an important population qualification. The $29.55 median for 88150 rests on just 96 reported private-payor tests from nine TINs. The $10.72 median for 88164 rests on 942 tests from 39 TINs.

QC changes neither median.

The statutory minimum limits the disparity—but does not eliminate it

Pap smear laboratory tests have an unusual protection under Social Security Act §1833(h)(7). Congress established a $14.60 national minimum payment in 2000, with annual adjustments. The provision expressly applies notwithstanding §1834A, the PAMA pricing statute. Consequently, the minimum survives private-payor-based pricing.

The 2026 minimum is $18.54, following a 1.9% increase. CMS’s annual payment instructions explicitly include all the screening codes discussed above. The protection spans conventional and liquid-based preparations, manual and automated screening, and Bethesda reporting. Payment remains limited by the actual billed charge.

For 88174 and 88175, the floor appears unlikely to change the outcome because both medians exceed it comfortably. But for 88164, the statutory flow is decisive: the $10.72 median cannot simply become the Medicare fee. Since the code already pays at the floor, its sharply lower median need not produce an actual payment reduction.

Other codes illustrate the same protection. 88148, automated screening with manual rescreening of conventional smears, has a preliminary median of $5.28. P3000, screening Pap smears by a technician under physician supervision, has a median of $7.94. Both currently pay $18.54 and appear on CMS’s minimum-payment list.

The floor prevents the most extreme Bethesda outcome, but leaves a substantial disparity. Using the current minimum as a reference, $18.54 is approximately 37% below $29.55. The eventual difference would depend on the updated 2027 minimum and final pricing decisions. Comparable manual screening services could therefore acquire markedly different payments largely associated with the reporting distinction, even after statutory protection is applied.

What's Next?

CMS expects to publish final 2027 rates in November. The preliminary cytology results deserve awareness across the code family: additional manual review can receive less payment, closely related reporting variants can diverge sharply, and small reporting populations can generate large apparent increases. The statutory floor protects against some reductions, while leaving the unexpected relationships between other prices intact.

Source workbook for the numerical comparisons:

https://www.cms.gov/medicare/payment/fee-schedules/clinical-laboratory-fee-schedule/clfs-pama-reporting-resources

https://www.cms.gov/files/zip/cy-2027-preliminary-weighted-median-2027-clfs-rate-calculations-raw-vs-trimmed-rates.zip

Sepsis: ARPA Grant for Treatable Subtypes; Review

ARPA-H Backs a Precision-Immunology Platform for the ICU

An award of up to $41.4 million will support a Stanford-led effort to measure changing immune states in critical illness and use them to guide treatment. The scientific premise is gaining support, but turning immune profiles into reliable bedside decisions remains a major challenge.

 AI Guest Author: Chat GPT 6.

A Stanford Medicine team led by Purvesh Khatri has received an award of up to $41.4 million from the Advanced Research Projects Agency for Health (ARPA-H) to develop an immune-monitoring and treatment-guidance system for critically ill patients. Inflammatix, which announced its role on October 8, will serve as the team’s commercialization collaborator and bedside-platform developer.

The project, called IMPRINT—Immune Mapping and Perturbation for Real-time Identification and Treatment—is part of ARPA-H’s broader CIRCLE program. The team plans to combine immune and organ-specific measurements with electronic health record data in a mechanistic “digital twin” intended to model a patient’s evolving illness and help identify potential therapies. ARPA-H describes the Stanford project as including transcriptomic markers and an organoid-based system for testing model predictions. Inflammatix says it will develop an investigational, rapid whole-blood readout that can be repeated as a patient’s condition changes. (ARPA-H award description; Inflammatix announcement) ARPA-H  Sweeney at Linked In.

ARPA-H CIRCLE is designed for critical illness broadly, including sepsis, trauma and other conditions—not sepsis alone. Its central premise is that patients who appear to have the same clinical syndrome may have substantially different immune biology, and may therefore respond differently to immune-modulating treatments. The program’s stated goal is to develop integrated systems for measuring immune function, modeling its course and identifying potential points for intervention. ARPA-H

Sepsis is a syndrome with varied immune responses

Sepsis is often discussed as though it were a single disease, but its clinical definition describes organ dysfunction arising from a dysregulated response to infection. The infection’s source and organism matter; so does the host response. Some patients show prominent inflammatory activation, while others show features of impaired immune function. These patterns can overlap, and a patient’s immune state can change over time.

That heterogeneity has complicated trials of therapies aimed at the host response. A treatment that helps one biologic subgroup might have little effect—or cause harm—in another. Averages across a broad sepsis population can obscure those differences.

Research over the past decade has begun to define these subgroups. In a 2016 prospective study, Davenport and colleagues identified two blood gene-expression signatures, one of which was associated with immunosuppressive features and higher short-term mortality. A 2017 study by Scicluna and colleagues identified four genomic endotypes across sepsis cohorts; one was consistently associated with worse outcomes. These studies established that the host response could be classified in biologically meaningful ways, although they did not by themselves establish treatment-selection rules for routine care. (Davenport et al., Lancet Respiratory Medicine, 2016; Scicluna et al., Lancet Respiratory Medicine, 2017) PubMed

Clinical data have also been used to define sepsis phenotypes. In a 2019 JAMA study, Seymour and colleagues identified four groups—alpha, beta, gamma and delta—with differing patterns of organ dysfunction, inflammatory biomarkers and mortality. Simulations suggested that variation in phenotype frequency could affect whether a clinical trial appeared to show benefit, harm or no effect. The authors emphasized that further research was needed before the classifications could guide clinical care. (Seymour et al., JAMA, 2019) PubMed

A more recent effort, the Hi-DEF framework, shifts attention toward the degree of dysregulation in myeloid and lymphoid immune compartments. In an analysis drawing on more than 7,000 samples from 37 cohorts, those dimensions were associated with severity and mortality across sepsis and other critical illnesses. The framework is relevant to CIRCLE’s broader ambition: to characterize immune dysfunction in ways that may apply across diagnoses rather than relying solely on the label that brought a patient to the ICU. The framework remains a research tool, and its authors describe it as a potential aid to understanding and treatment development. (Moore et al., Nature Medicine, 2025) PubMed

A clinical trial offers an early treatment signal

The case for immune-guided therapy is no longer based only on observational studies. In the 2026 ImmunoSep randomized clinical trial, 276 adults with sepsis were assigned to biomarker-guided immunotherapy or placebo. Patients classified as having macrophage activation-like syndrome received anakinra; those classified as having sepsis-induced immunoparalysis received interferon gamma.

By day 9, the prespecified improvement in organ dysfunction, measured by the SOFA score, occurred in 35.1% of the precision-immunotherapy group and 17.9% of the placebo group. The trial did not find a statistically significant difference in 28-day mortality. The result is an important proof of concept for matching immune-directed treatment to measured biology, while leaving open questions about patient selection, the biomarker thresholds, safety and effects on outcomes that matter beyond short-term organ function. (Giamarellos-Bourboulis et al., JAMA, 2026) PubMed

From immune measurement to bedside decisions

IMPRINT’s challenge will be to make the proposed measurements fast and dependable enough to inform care while the patient’s condition is changing. The digital twin will also need to provide recommendations that clinicians can interpret and that prospective studies can test. A biological pattern associated with poor outcomes is not automatically a marker that predicts benefit from a specific therapy.

For now, the award supports development and testing of an investigational platform. It does not establish a new clinical standard for sepsis treatment. Antibiotics, source control and organ support remain the foundations of sepsis care; immune-modulating therapies guided by these emerging classifications require further clinical validation.

The program’s larger bet is that repeated immune measurement, combined with clinical data and mechanistic modeling, could help move critical care from syndrome-based categories toward treatment decisions informed by a patient’s biology. The ImmunoSep results suggest that this is a testable clinical strategy. CIRCLE will now attempt to build the measurement and modeling infrastructure to pursue it across critical illness.

##

This is an AI generated article.  Errors may occur.  It should not be taken as ground truth.  It can serve as an example of current AI abilities to take a topic suggestion and research it and write an autonomous article. 

The Stark Law and Pathologist-Owned Labs...and IHC Orders

We all know that the Stark law prohibits doctors from referring lab tests to labs they have an ownership in.   How about the daily occurence of a pathologist owning his ab business, and every hour referring something to it - IHC stains on a breast cancer case, special stains for unexpected possible organisms, and so on.  Why don't these orders "to a lab the referring doctor has an ownership interest in" trigger Stark?

It may seem like a puzzle, and the answers are buried in Stark law definitions and details.

NOTE: The essay below is AI-generated, and should NEVER be construed as actual legal opinion or advice.  Take it as the current ability of AI to understand a situation and write about it.  For legal answers to actual situations, get a lawyer.

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The Stain Order That Wasn’t a Stark Referral

A case study in pathology, ownership, and the meaning of “referral”

Dr. Dan Michaels had barely settled into Oak Street Pathology Group when a colleague brought him a question from the golf course.

“Everyone knows Stark prohibits doctors from sending lab work to a lab they own,” the colleague said. “So how can we own this laboratory and order stains on our own cases all day long? We do the work, bill Medicare, and Medicare pays. Isn’t that exactly what the law was meant to stop?”

The example was concrete. Michaels had received a breast biopsy for consultation. After examining the tissue, he ordered additional levels, special stains, and immunostains. Oak Street’s laboratory performed the work. The group billed Medicare, which paid the claims. Seven pathologists owned the practice and shared equity in the lab.

The apparent contradiction seemed hard to escape:

1. Stark generally prohibits a physician from referring Medicare patients for designated health services to an entity with which the physician has a financial relationship, unless an exception applies.
2. Clinical laboratory services are designated health services, and pathologists commonly own or invest in the laboratories where pathology work is performed.
3. Pathologists routinely order additional work on referred specimens, and their laboratories bill for it.

Michaels tried Google. The broad summaries he found repeated the general prohibition but did not explain the pathology workflow. 

His nephew Brad, a law student writing an honors thesis on physician self-referral, took the question to two health-law professors. Two months later, Brad returned with a short answer and a long regulatory history.

The answer was in the word referral. The Stark statute contains a specific rule under which certain orders by a pathologist do not count as referrals at all. That is different from saying the pathologist’s ownership interest is irrelevant, and it's also different from relying on the familiar in-office ancillary services exception.

First, sharpen the premise

Stark does not simply say, “A physician may never order a laboratory test from a lab the physician owns.” It generally prohibits a physician from making a referral for a Medicare-covered designated health service to an entity with which the physician or an immediate family member has a financial relationship, unless a statutory or regulatory exception applies. Ownership can create the financial relationship; the prohibited referral is the other essential part of the analysis.

The law defines clinical laboratory services broadly enough to include pathological examinations. Thus, the issue cannot be dismissed by saying that pathology stains are outside Stark. They may be designated health services. The question is whether the pathologist’s order is a “referral” within the law’s definition.

The statute answers that question for a defined class of specialist services. Under Social Security Act § 1877(h)(5)(C), a pathologist’s request for clinical diagnostic laboratory tests and pathological examination services is not a referral when the services are furnished by or under the supervision of the pathologist pursuant to a consultation requested by another physician. The statute names parallel provisions for diagnostic radiologists and radiation oncologists. www.govinfo.gov

The implementing regulation is 42 C.F.R. § 411.351, definition of “Referral,” paragraph (2). In the current text, the request must result from a consultation initiated by another physician; the consultation may be requested from a particular pathologist or from an entity with which the pathologist is affiliated. The tests must be furnished by or under the pathologist’s supervision, or under the supervision of a pathologist in the same group practice. www.law.cornell.edu

So, in the ordinary referred-specimen case, the sequence is:

Treating physician requests pathology consultation → pathologist evaluates the case → pathologist orders stains or other pathology work needed for the examination → the pathologist or another pathologist in the group supervises the services.

When those conditions are met, the pathologist’s order is excluded from the definition of a Stark referral. The fact that Oak Street’s laboratory performs and bills for the stains does not, by itself, turn that excluded order into a prohibited referral.

“Consultation” is a legal term here

The rule does not require a pathologist to see the patient or conduct a conventional bedside consultation. For Stark purposes, § 411.351 defines a consultation as a professional service in which another physician requests the pathologist’s opinion or advice about a specific medical problem; the request and need are documented in the record; and the pathologist prepares a written report for the requesting physician. www.law.cornell.edu

That definition was shaped in rulemaking partly because the ordinary Medicare consultation model fit pathology poorly. A pathologist usually evaluates a specimen rather than taking a patient history or performing a physical examination. CMS ultimately said that the Stark consultation definition does not require either step. It is a Stark-specific definition and does not change Medicare coverage or payment rules for services billed as consultations. www.federalregister.gov

The original physician’s role matters. CMS described the protected specialist as responding to a consultation while the requesting physician remains involved in deciding how to proceed with the patient’s care. If the specialist instead takes over care, or if a patient simply walks in without a consultation initiated by another physician, the special definition may not apply. The pathologist’s subsequent orders then need to be analyzed under the ordinary Stark rules and any applicable exception. www.govinfo.gov

How the rule reached Oak Street

The statute came first

The pathologist language is not a CMS-created loophole. Congress placed the specialist provision in the Stark statute. The 1995 rulemaking was the first major regulatory step that incorporated the clinical-laboratory provisions into 42 C.F.R. § 411.351. Later statutory amendments expanded the corresponding language for radiology and radiation oncology, and CMS incorporated those changes through subsequent rulemaking. www.federalregister.gov

The 1995 final rule confronted the ownership problem directly

In the 1995 final rule, commenters asked CMS about a pathologist who provided professional services to one laboratory but sent specimens to a second laboratory in which the pathologist had a financial interest. They also asked whether the rule should protect a pathologist who recommended additional laboratory work to the attending physician.

CMS drew a practical line. If the pathologist performed or supervised the laboratory testing at the second lab, the pathologist’s request was not a referral under the statutory provision. If the pathologist sent the work to a lab in which the pathologist had a financial interest but did not perform or supervise the services, CMS said the referral could be prohibited unless another exception applied. CMS separately said that a pathologist’s recommendation for more testing was not itself a referral: the attending physician ultimately decides whether the testing is needed and where to order it. www.govinfo.gov

The rulemaking therefore addressed the kind of two-laboratory arrangement that can look superficially like Oak Street’s. The deciding fact was not simply that the pathologist owned part of a lab. It was whether the request arose from another physician’s consultation and whether the pathologist performed or supervised the service as the law requires.

The 2001 final rule defined the consultation boundary

CMS proposed a broader Stark framework in 1998 and received comments that the consultation concept was unclear. Commenters questioned whether a consultant had to take a history and examine the patient, and how to distinguish consultation from a transfer of care.

In the 2001 Phase I final rule, CMS adopted the three central criteria now reflected in § 411.351: another physician requests an opinion or advice about a specific medical problem; the request and need are documented; and the consultant provides a written report. CMS expressly rejected a patient-history or physical-examination requirement for Stark consultations, recognizing that pathologists rarely see patients. www.federalregister.gov

CMS also emphasized that the statutory protection was narrow: it applied to the named specialists and named categories of services. It did not extend the carve-out to all physicians who order diagnostic tests.

The 2007 rule clarified group supervision and walk-in cases

The 2007 Phase III final rule clarified that qualifying services could be supervised by another pathologist in the same group practice. It also rejected a request to extend the carve-out to walk-in patients. CMS reasoned that the consultation provision assumes another physician has already determined that the patient likely needs the specialist’s services. That rationale is absent when the patient initiates the encounter. www.govinfo.gov

CMS further confirmed that the consultation exclusion can cover technical components ordered by hospital-based pathologists when the other requirements are met. But it declined to extend the exclusion to every physician employed by the same hospital. The agency pointed instead to other Stark exceptions, such as the bona fide employment exception, where appropriate. www.govinfo.gov

Why this is not just the in-office ancillary exception?

The pathologist provision and the in-office ancillary services exception can lead to similar practical outcomes, but they work differently.

The pathologist rule is in the definition of “referral.” A qualifying request is not a referral under Stark in the first place. The in-office ancillary services exception, at § 411.355(b), assumes there is a referral and protects it if detailed requirements are met, including requirements concerning who furnishes or supervises the service, location, and billing.

CMS also distinguished the supervision standards. The pathologist provision requires “supervision” as defined by applicable Medicare coverage and payment requirements or relevant health-and-safety standards. The in-office ancillary exception has its own supervision requirements. In other words, one cannot assume the pathologist carve-out is merely an application of the ordinary in-office exception, or that every arrangement qualifying under one automatically qualifies under the other. www.govinfo.gov

What Brad’s memo says about Dr. Michaels

On the facts as presented, Dr. Michaels receives a specimen for pathology consultation from another physician. He determines that additional stains are needed to complete the pathologic examination. Oak Street’s laboratory performs them under his supervision, or under the supervision of another pathologist in the same group. The group bills Medicare for the services.

The pathologist’s request fits the statutory and regulatory exclusion from “referral,” assuming the consultation and supervision conditions are documented and satisfied. The answer is not that Stark has no application to pathology, nor that ownership is irrelevant. The answer is that the qualifying pathologist order is specifically excluded from the Stark definition of a referral.

The boundary cases remain important. A walk-in patient, testing outside the pathology consultation, an order sent to a lab without the required supervision, or a referring physician’s own financial relationship with the lab may change the analysis. The pathologist provision also does not resolve separate questions under the Anti-Kickback Statute, state self-referral laws, Medicare billing rules, or payer contracts.

Sidebar:
Is the carve-out limited to “diagnostic” tests?

The statute uses the phrase “clinical diagnostic laboratory tests and pathological examination services,” and the regulation refers to clinical diagnostic laboratory tests and pathological examination services. But the provision is not written as a special exemption only for conventional histology, special stains, or immunostains, nor does it separately distinguish diagnostic tests from prognostic tests or genomic profiling.

• Quote 411.351 - information for the diagnosis, prevention, or treatment of any disease or impairment of, or the assessment of the health of, human beings, including procedures to determine, measure, or otherwise describe the presence or absence of various substances or organisms in the body, as specifically identified by the List of CPT/HCPCS Codes.

The relevant categories are the services named in the statute and regulation. The Stark regulation defines clinical laboratory services to include pathological examinations and ties the category to the applicable CPT/HCPCS code list. Therefore, a prognostic or comprehensive genomic profiling service is not automatically in or out merely because it is called “prognostic” or “CGP.” The precise service and code, whether it is a designated health service, and whether it is part of the pathologist’s qualifying consultation must be examined. The label alone does not resolve the Stark question. www.law.cornell.edu

A cold corner of Stark law

This is a technically important but relatively quiet corner of Stark. The statutory carve-out dates to the law’s early history; CMS incorporated it into regulation in 1995, clarified “consultation” in 2001, and addressed group supervision and edge cases in 2007. The rule has not suddenly appeared to excuse a new business model. It reflects a long-standing distinction between a physician steering a patient to a financially connected lab and a pathologist completing the examination requested by the treating physician.

For Dr. Michaels, the golf-course puzzle is solved: the stains are not “referrals” under Stark when the case arises from a qualifying consultation and the pathologist or a pathologist in the group furnishes or supervises the work as the rule requires.

Primary sources

• Social Security Act § 1877, 42 U.S.C. § 1395nn — statutory referral definition and specialist provision.
• 42 C.F.R. § 411.351, Definitions — current definitions of “clinical laboratory services,” “consultation,” and “referral.”
• 42 C.F.R. § 411.355(b), In-office ancillary services — separate in-office ancillary services exception.
• 1995 final rule, 60 Fed. Reg. 41914 (Aug. 14, 1995) — initial regulatory incorporation of the clinical laboratory referral rules and CMS responses to comments on pathology arrangements.
• 1998 proposed rule, 63 Fed. Reg. 1659 (Jan. 9, 1998) — proposed Stark framework and discussion of consultations and supervision.
• 2001 Phase I final rule, 66 Fed. Reg. 856 (Jan. 4, 2001) — final consultation definition and responses to comments.
• 2004 Phase II interim final rule, 69 Fed. Reg. 16054 (Mar. 26, 2004) — next stage of Stark implementation.
• 2007 Phase III final rule, 72 Fed. Reg. 51012 (Sept. 5, 2007) — group-practice supervision, walk-in patients, and hospital-based technical components.

Regulatory Quotes

“Clinical laboratory services” — 42 C.F.R. § 411.351 (definitions):

Clinical laboratory services means the biological, microbiological, serological, chemical, immunohematological, hematological, biophysical, cytological, pathological, or other examination of materials derived from the human body for the purpose of providing information for the diagnosis, prevention, or treatment of any disease or impairment of, or the assessment of the health of, human beings, including procedures to determine, measure, or otherwise describe the presence or absence of various substances or organisms in the body, as specifically identified by the List of CPT/HCPCS Codes.

CPT/HCPCS List qualification — the next two sentences in the same definition:

All services so identified on the List of CPT/HCPCS Codes are clinical laboratory services for purposes of this subpart. Any service not specifically identified as a clinical laboratory service on the List of CPT/HCPCS Codes is not a clinical laboratory service for purposes of this subpart.

Source: 42 C.F.R. § 411.351, current eCFR text. www.ecfr.gov

Statute

There are two separate statutory provisions here, and only one is the pathologist rule.

1. The statute makes clinical laboratory services a designated health service

Social Security Act § 1877(h)(6)(A), 42 U.S.C. § 1395nn(h)(6)(A), says:

“The term ‘designated health services’ means any of the following items or services:
(A) Clinical laboratory services.”

That makes clinical laboratory services a category of DHS under Stark. It does not give the detailed definition of which specific tests or codes qualify. That detail will however appear in the regulation at 42 C.F.R. § 411.351, including its reference to the CPT/HCPCS list. ssa.gov

2. The pathologist provision is an exception to the statutory definition of “referral”

Section 1877(h)(5)(C), 42 U.S.C. § 1395nn(h)(5)(C), says:

“A request by a pathologist for clinical diagnostic laboratory tests and pathological examination services … if such services are furnished by (or under the supervision of) such pathologist … pursuant to a consultation requested by another physician does not constitute a ‘referral’ by a ‘referring physician’.”

So the carve-out is about whether the pathologist’s request counts as a referral. The statute itself requires both that the request arise “pursuant to a consultation requested by another physician” and that the services be furnished by or under the pathologist’s supervision. The regulation further specifies the consultation and supervision requirements. ssa.gov

In short: the statute lists clinical laboratory services as DHS, and separately excludes certain pathologist requests from “referral.” The detailed clinical-laboratory-service definition is regulatory; the pathologist carve-out is statutory and turns on the consultation.

A Pathologist [Walks Into a Bar and] Orders a Test...  In case there was any question whether a pathologist can order a lab test, see 1877(h)(5)(c) above. He can order both clin lab and pathology tests.  Black and white. This overrides text at 42 CFR 410.32 that "only a treating physician can order a test."  (Benefit Manual 15: 80.6.5 also overrides 410.32 and its "manualized exceptions" to 410.32 were contemplated when the CFR 410.32 was written.)

AI Reviews: Three-Day Value Based Cancer Care Conference (VBCC), NYT October 2026

 

AVBCC 2026 and the work of making cancer care deliver value

The 2026 summit of the Association for Value-Based Cancer Care, scheduled for October 7–9 at the New York Athletic Club, presents an oncology system confronting a widening gap between scientific possibility and practical delivery. Its detailed agenda connects drug pricing, community practice economics, artificial intelligence, patient access, and the difficult task of measuring benefit. Across three days, a recurring question emerges: what must change so that better science reliably produces better care? This report examines the program’s themes and inferred priorities, then considers how the conference might evolve by 2029 as today’s proposed solutions face tests of implementation and accountability. AVBCC

GUEST AUTHOR:  Chat GPT 6 Sol 6.1 High.

This analysis uses the supplied October 2 agenda. The meeting is a no-media event; the report describes scheduled topics and draws inferences from their organization, without reporting speakers’ remarks or session conclusions. File: AVBCC 2026 Agenda 10.2

Payment reform reaches into the foundations of oncology practice. Drug economics runs through the program, connecting Wednesday’s sessions on Medicare negotiation, rebates, and outcome contracts with Thursday’s discussions of price transparency, biosimilars, supply channels, and prescribing incentives. Friday’s examination of practices’ dependence on drug margins brings these issues back to care delivery. Read together, these sessions suggest a concern broader than the price of any particular medicine: how to finance oncology services when the economics surrounding drug acquisition and reimbursement change.

That distinction matters. A lower drug price and a sustainable care system are related objectives, but achieving one does not automatically achieve the other. The agenda repeatedly raises the possibility that changes intended to improve affordability could destabilize the revenue supporting clinical infrastructure. The inferred priority is to make those dependencies visible and develop payment arrangements that support the actual work of care. The program does not establish that an era beyond average sales price payment has arrived; it asks how stakeholders should prepare for substantial changes to familiar economics.

Outcome contracts require agreement on who can deliver the outcome. Sessions on pharmaceutical contracting, legal structures, data limitations, and commercial use cases recur across Wednesday and Thursday. Their accumulation suggests frustration with the distance between announcing a value-based agreement and operating one. A contract needs a measurable endpoint, reliable data, an attribution method, and parties able to influence the result. The agenda’s separate examination of who owns the outcome makes this problem explicit.

The underlying difficulty is shared responsibility. A manufacturer supplies a therapy; clinicians select and manage it; patients need access, support, and the ability to continue treatment. A payment arrangement that assigns responsibility without accounting for these dependencies can create disputes rather than improvement. The scheduled contracting examples, including a private-sector application of an Oncology Care Model approach, offer a useful counterweight to broad forecasts. Their presence suggests a program interested in operational experience, although the agenda alone cannot establish whether any arrangement succeeded or produced net savings.

AI is being considered as infrastructure across the care journey. The breadth of AI programming is striking. Wednesday covers strategy, investment, payer applications, clinical pathways, and the movement from individual tools to practice infrastructure. Thursday extends the discussion into revenue cycle operations, specialty pharmacy, human judgment, and longitudinal patient data. Friday combines a demonstration showcase with the question of whether better measurement can make cancer care genuinely value-based.

This distribution suggests that AI’s perceived role is expanding beyond assistance with isolated tasks. The program explores whether it can connect treatment planning, monitoring, reimbursement, and evidence generation. That is a larger organizational proposition than automating documentation.

It also creates a demanding test of value. Faster billing, more complete records, and better symptom control are distinct achievements; evidence for one does not establish the others. The agenda’s attention to implementation, return on investment, security, and human partnership points toward scrutiny of the whole workflow. A reasonable inferred priority is to evaluate what changes after a tool is deployed, including the work it creates for clinicians and staff.

Measurement connects the technology discussion to payment reform. Wednesday’s review of the Oncology Care Model and Enhancing Oncology Model and Friday’s session on AI and digital quality measurement form a thread across the summit. The latter explicitly asks whether electronic health record data, electronic patient-reported outcomes, and oncology interoperability standards can make performance clinically meaningful, auditable, and actionable.

The important distinction is between observing expenditure and understanding care. Claims can describe paid services, but the questions raised elsewhere in the program concern treatment toxicity, delayed access, adherence, appropriate testing, and patient experience. Those issues require more clinical context.

Better measurement would have to guide a response: identifying a deteriorating patient early enough to intervene, detecting a missed testing opportunity, or determining whether a care model improves outcomes. The agenda presents measurement as a possible explanation for the limitations of earlier approaches. That remains a hypothesis, rather than a demonstrated account of why value-based oncology has struggled. Payment design, staffing, and responsibility still need to align with whatever the measures reveal.

Community oncology is the recurring test of whether innovation can scale. The program returns to community delivery through cell and gene therapies, radiopharmaceuticals, bispecific antibodies, oral medicines, molecular testing, and medically integrated pharmacy. The recurring concern is that scientific capability can advance faster than the infrastructure needed to use it safely and consistently.

Across these sessions, access becomes a practical question involving site readiness, staffing, financing, monitoring, distribution, and coordination. Approval and coverage are important milestones, but they do not complete that chain. A therapy must reach an appropriate patient through a service capable of managing it.

The sessions on technology-supported toxicity management and anticipatory monitoring connect directly to this problem. Moving treatment closer to home requires dependable escalation and clinical response, alongside data collection. Advanced-practitioner reimbursement and Friday’s physician compensation discussion add the workforce dimension: expanding access depends on paying and organizing the people who perform that work. The inferred priority is to build delivery capacity alongside therapeutic innovation.

Precision medicine faces both an implementation test and an evidence test. Wednesday’s session on biomarker testing for clinical trial eligibility, Thursday’s genomics-first care discussion, and Friday’s molecular medicine case studies identify failures along the route from specimen to treatment. Testing must be ordered, financed, completed, interpreted, and returned in time to affect a decision. Tissue insufficiency and fragmented workflows can interrupt that route before a promising result becomes useful.

The agenda also questions whether increasingly sensitive information necessarily improves care. Its examination of molecular residual disease surveillance asks about clinical utility and spending. The screening session sets out an evidence chain connecting detection to changed management, patient benefit, and possible harms.

These topics belong together. Appropriate testing can be underused while other applications remain insufficiently supported. The useful question is which test, for which patient, at which decision point, with what consequential action. Clinical trial access and routine-care evidence generation extend that inquiry beyond testing itself. The program’s separate attention to international research competition suggests concern about the system’s capacity to generate future evidence as well as apply existing knowledge.

Transparency is also a debate about power. Employers, benefit consultants, pharmacy benefit managers, group purchasing organizations, distributors, management services organizations, and risk-bearing care partners receive sustained attention. Sessions on ownership, direct contracting, intermediary value, and utilization management suggest a changing map of who controls cancer care and who is accountable for its results.

The tension is that additional organizations may supply capabilities practices need while also creating additional costs, contractual restrictions, or administrative layers. The agenda raises both possibilities. Its discussions of prior authorization alternatives and pathway organizations sharpen the question: if one form of utilization control recedes, which decisions move elsewhere, and who can challenge them?

Data adds another dimension. Wednesday’s pathway-to-value session explicitly considers whether the ability to demonstrate outcomes could favor larger networks over smaller practices. An inference follows: the same infrastructure that enables value contracts could make scale a condition of participation. Shared standards and accessible services may therefore matter to competition and practice independence, as well as technical interoperability.

The patient perspective broadens what counts as value. Patient-focused sessions appear throughout the program, from Wednesday’s assessment of the patient’s situation and unequal access to insider knowledge to Friday’s examination of the gap between institutional ratings and lived experience. Second opinions, financial assistance, survivorship, geriatric assessment, and site-of-care decisions make that perspective concrete.

Together, these topics challenge a definition of value confined to drug selection or episode spending. Timely expertise, manageable symptoms, continuity after treatment, and care suited to functional status can materially shape the patient’s experience. The prevention session on obesity and GLP-1 therapies also extends the program’s horizon toward future cancer burden, while posing scientific questions rather than settling them.

Patient-centeredness becomes most consequential when it changes an operating decision: which service receives funding, what triggers intervention, how treatment intensity is chosen, or whether a proposed cost saving introduces a new access barrier. The agenda’s repeated return to these issues suggests that affordability and patient benefit need to be assessed together.

Policy implementation receives attention alongside policy ambition. Scheduled CMS leadership discussions and multiple sessions on Medicare arrangements, drug pricing, and commercial insurance place government policy throughout the summit. The Medicare Part B effectuation working-group town hall is especially revealing because it addresses the machinery of implementation. Related 340B sessions examine claims identification, verification, financial transactions, and reconciliation.

These subjects show why policy design and policy delivery need separate examination. A pricing framework can leave difficult questions about acquisition costs, payment timing, information exchange, and disputes. The agenda’s inclusion of a working-group report and a regional collaborative readout suggests interest in organized work beyond the annual meeting. It does not establish that recommendations were adopted or problems resolved. It does indicate that the program makes room for the operational details through which policy becomes a daily reality for practices and patients.

A 2029 conference could put results under greater scrutiny. The following possibilities are projections from the 2026 agenda, rather than announced plans or predictions of particular policy outcomes.

AI could move from demonstrations to comparative evaluation. A more mature program might ask which deployments reduced treatment delays, improved toxicity management, or saved staff time after accounting for oversight and integration. Sessions could compare sustained results across settings and examine failures, model changes, and the ability to audit recommendations. Demonstrations would remain useful, but would sit beside evidence of clinical and operational performance.

Outcome contracting could be judged through completed experience. Instead of concentrating on contract design, the conference might examine settlement results, attribution disputes, administrative costs, and patient outcomes. A crucial question would be whether savings survive after paying for the data, navigation, monitoring, and clinical infrastructure used to produce them. Contracts that endure would provide more informative examples than announcements alone.

Community delivery could become a measurable system capability. The discussion might move toward the proportion of eligible patients actually receiving complex therapies, geographic access, treatment delays, and safety across sites. That would allow scrutiny of whether community expansion improves access and lowers total costs while maintaining quality. Readiness assessments could become more specific about staffing, response capacity, and financial exposure.

Diagnostic debates could become more indication-specific. Molecular surveillance and screening sessions might distinguish applications supported by evidence of useful changes in management from those still requiring validation. Genomics-first care could be evaluated through testing completion, turnaround, treatment selection, and trial access. Stronger evidence could justify expansion in some settings and more selective use in others.

Practice sustainability and patient benefit could be evaluated together. A 2029 agenda might compare payment arrangements that explicitly finance navigation, monitoring, survivorship, and multidisciplinary care. It could also examine whether shared data infrastructure enables smaller practices to participate or whether the cost of demonstrating value increases concentration. Patient-facing measures would help assess what these organizational changes accomplish.

The strongest inference from the 2026 program is that oncology’s value problem spans several connected systems. Useful measurements need a clinical response; that response needs staff and financing; contracts need dependable data and defensible responsibility. By 2029, the conference could have a more concrete basis for judging whether these pieces work together. Its most informative reports would show who received better care, what made the improvement possible, and whether the arrangement can last.

Source and organization links: This report is based on AVBCC 2026 Agenda 10.2.docx, dated October 2, 2026. See the Association for Value-Based Cancer Care, the 2026 conference page, and the online agenda. The supplied agenda is the source for the session analysis; the online version may differ.