Sunday, March 3, 2024

Literary Note: Dr Abraham Verghese Discusses Life, Career

HEADER: I rarely mention literatry events but here is one.  Dr. Abraham Verghese, clinician and novelist, in a 45 minute interview on C-Span.

Dr Abraham Verghese was raised in Ethiopia, where he started medical school until a revolution closed it. (One of his classmates become a guerrila and eventually prime minister).

Verghese finished medical school in India and has had most of his career in the United States, currently as a professor at Stanford.

He's written many notable essays and two memoirs and two novels (one of which was a NY Times bestseller for months).  Online biography here.



See him discuss his wide-ranging global life on CSpan/BookTV, a 45 minute interview from January 2024.


He also mentions an autobiographical essay form 1997 in New Yorker; a summary is online and the full essay is on subscription.

Saturday, March 2, 2024

NOTEWORTHY: FDA Sends Final LDT Rule to White House

Header: FDA Sends Finalized LDT Rule to White House OMB/OIRA


Moving at a fast pace, FDA has finalized its review of comments on the LDT regulation, and the final regulation was logged at the White House on March 1, 2024.   Specifically, the rule is at the Office of Management and Budget (OMB) in the Office of Information and Regulatory Affairs (OIRA).

OIRA Meetings

The OIRA page logs all official meetings on the rule, called EO-12866 meetings.  Find them here:

Nine meetings were logged, the first on August 4 (Cara Tenenbaum and Center for Science in the Public Interest, which submitted a 4-page letter demanding LDT regulation in May 2023).  (See also ProPublica June 14). 

The last one was September 26 (William Signer of Carmen Group and ACELT, Academic Coalition for Effecitve Laboratory Tests). ACELT brought speakers from U Chicago, U Colorado, Harvard, Yale, and Wash U. Each of the meetings shows cloud copies of any documents presented. For example, ACELT included a 25-page legal memo against the FDA's authority to regulate LDTs.


The rule's page when it was a draft rule is here.

Friday, March 1, 2024

SCISPACE: An AI tool for biomedical paper research

Like SCITE, "SCISPACE" is a sort of PubMed on AI steroids.  You'll get AI-driven bells and whistles, compared to a plain-jane PubMed search.


On February 22, 2024, I highlighted SCITE, a tool for studying who's cited a key biomedical paper, how they've cited it, and seeing related papers. 

Here, a few words about another tool in this space, SCISPACE. Find it here:  [*]   

You can ask SCISPACE a biomedical question, and it comes back with five or six AI sentences with citations.  It also comes back with bibliographies of topical papers, and you can filture for features like "Top tier papers" or "Open access."

It does more, but that's a thumbnail sketch.


I asked it a topical question, "What is the evidence for the clinical utility of minimal residual disease (MRD) monitoring in cancer management?"

It came back instantly with this "Insight from Top 5 Papers."

  • Minimal residual disease (MRD) detection has shown clinical utility in cancer management. MRD monitoring is a powerful predictor of disease-free and overall survival, particularly in acute lymphoblastic leukemia (ALL) [1]. Different methods, such as polymerase chain reaction (PCR) and droplet digital PCR (ddPCR), have been used for MRD detection with high sensitivity and precision [2] [3]. MRD detection using ddPCR has been shown to detect micro-residual disease missed by PCR in one patient [4]. In non-small cell lung cancer (NSCLC), ultrasensitive fragmentomic assays using whole-genome sequencing (WGS) have demonstrated the ability to detect MRD and predict recurrence with high sensitivity [5]. In breast cancer, circulating tumor DNA (ctDNA) assays have allowed for the detection of molecular residual disease (MRD) prior to clinical evidence of recurrence, providing a lead time for intervention . MRD detection using ctDNA has shown promise in guiding therapy escalation in ongoing clinical trials . In colorectal cancer, tumor-informed ctDNA testing has been used to detect and quantify MRD, with higher rates of detection in liver metastasis . These findings highlight the clinical utility of MRD detection in cancer management, including early intervention and guiding treatment decisions.


It may not escape attention that the above instantly-created paragraph about MRD could be nearly cut-and-pasted for various purposes that are not super-demanding.  


In addition, SCISPACE came back with 10 papers by name, with links and PDFs if available.  

It also instantly provided a one sentence summary of each paper. Click below to see the top of this ten-row table.

Check YouTube for training and review videos for SciSpace or register with email for some entry level features.  More by subscription.


I asked the question again but specified I wanted results in lung cancer.

  • MRD detection has shown to be useful in lung cancer management. Studies have demonstrated that the traditional circulating tumor DNA mutation-based approach has limited performance in MRD detection, especially for early-stage cancer after surgery [1] [2]. However, a cfDNA fragmentomics-based method using whole-genome sequencing has shown great sensitivity in predicting prognosis and detecting patients with a high risk of recurrence [3] [4]. This method has been found to be more sensitive in predicting patient recurrence compared to the traditional circulating mutation approach [5] . Additionally, longitudinal MRD detection using circulating tumor DNA has shown high sensitivity and can potentially identify patients who may benefit from consolidation therapy . These findings highlight the potential of MRD detection in guiding adjuvant therapeutics and improving outcomes in lung cancer patients .

When I filtered in my query for "top tier papers," it came back in a couple seconds with an updated 10-paper list and again a write-up of first 5 papers.

  • Molecular residual disease (MRD) detection has shown promise in the management of lung cancer. Studies have demonstrated that circulating tumor DNA (ctDNA) analysis can be used to detect MRD in non-small cell lung cancer (NSCLC) patients after surgery or radiotherapy [1] [2]. Longitudinal surveillance of ctDNA has been found to be effective in predicting relapse and guiding adjuvant therapy decisions [3]. The sensitivity and specificity of ctDNA MRD detection have been shown to be high, making it a promising biomarker for relapse prediction [4]. Additionally, ctDNA analysis has been used to identify patients with MRD who may benefit from targeted therapies or chemotherapy [5]. These findings suggest that MRD detection using ctDNA analysis can play a valuable role in the management of lung cancer, allowing for early detection of relapse and personalized treatment strategies.


SCITE versus SCISPACE?  I haven't used each enough to give a masterful comparison.  But, on the surface, SCISPACE is designed to be driven by questions, as in the examples above, give you 10 citations, sort them by "open access" if you want, and give you a paragraph summary of the field driven by the top citations, as well as one-sentence summaries-at-a-glance of all the papers it pulled.

SCITE works better if you already know a key paper.  It gives you every paper that has cited it, which you could get elsewhere, but it shows you HOW each citing paper has cited your index paper.   That can be pretty darn powerful and it's  available in a second or two.  

SCITE pretty much depends on a subscription, as I recall.  SCISPACE has a free tier that is pretty good, but will cut you off after a limited number of queries.   The upgrade at SCISPACE is $12/mo but sold as $120/yr.   


Other resources of potential interest are Semantic Scholar, and Google Biomed Explorer. They're both free.
See also the general AI  sources and  The latter 2 are both general AI's, with a free tier, but which are especially adept with citations.  I showed an example of drafting a footnoted article with Perplexity in an earlier blog.

* At some point the address will be

Webinar March 7; Telehealth, Genetics, Precision Medicine

 See a webinar on March 7, 2024, sponsore by the California Telehealth Resource Center:

"Success and Sustainability: Digitally-provided Genetic Services Overcome Obstacles to Patient Access to Precision Medicine."

Find more info here:

Thanks for Sylvia Trujillo for highlighting on LinkedIn.  It's 12 pacific on a Thursday.

Thursday, February 29, 2024

CMS Update Notice - MolDx Updates a Major Transplant Article, CMS Highlights in New Press Release

 I've had a number of questions about the February 22 WSJ Op Ed about Medicare and CareDx, and I've been putting together notes for an essay.


UPDATE - On February 29, 2024, see


For CareDx news from the day before, Feb 28, here.

See coverage at Genomeweb:

Wednesday, February 28, 2024

Brief Blog: CareDx and Medicare; 4Q2023 10-K Cites "Medicare" 215 Times. CMS Updates Press Release.

I've had a number of questions about the February 22 WSJ Op Ed about Medicare and CareDx, and I've been putting together notes for an essay.


UPDATE - On February 29, see

updated press release from CMS 

and updated billing article A58019 from MolDx.


But meanwhile, on February 28, CareDx held its 4Q-2023 investor call, and issued its updated SEC 10-K form.

Hint:  The 10-K report uses the term "Medicare" 215 times.  ("Coverage" appears 166 times, MolDx appears 82 times, Palmetto 16).

I fed the 10K and the WSJ into GPT4 and asked, what did it make of it?  It popped back with this illustration:

AI Corner

Besides the photo illustration, I asked GPT4 to "read" the 10-k and WSJ and write up an AI summary of them.   Find it here.  

In an auto transcript of the 50 minute investor call, 8500 words, we tallied "coverage" 35 times, "Medicare" 22 times, and "LCD" 4 times.  ("Capitol Hill" once.)  

In discussing operating expenses, CareDx remarked for the winter quarter, "The sales and marketing spend increased $1.6 million, primarily related to our targeted policy efforts to restore Medicare coverage." (That's about $100K a week for 12 weeks).

Between the 10-k report and 50-minute discussion, terms I've cited like "Medicare" and "Coverage" occurred some 540 times.

For comparison, Natera's Feb 28 10k mentions Medicare 52 times and the transcript, mentions Medicare 6 times.  

Out of curiousity I generated these pie and bar charts inside GPT4 rather than in Excel or PPT.

Nerd Note: AMA Explains "Image Bundling"

Coding in AMA CPT may include optional language, such as "Sequencing, including duplication-deletion analysis if performed."

One area of healthcare where separate coding, add-on coding, and bundled coding may fly together is imaging plus procedures.  The term of art is "image bundling" and it is an area of advanced policy at AMA CPT.

See their entertaining flow chart and instructions:

A JPEG is not as good as the PDF link above, but I include a JPEG also:

click to enlarge

Let's ask artificial intelligence (Chat GPT4) to have a crack at it for us:

Monday, February 26, 2024

Very Brief Blog: Natera Wins MolDx Coverage for MRD in Neoadjuvant Breast Cancer and in Ovarian Cancer

Header:  Natera, which provides the bespoke (patient-tailored) Signtera test for minimal residual disease, wins MolDx coverage for breast and ovarian cancer.


Natera's Signatera test, which screens for minimal residual disease with a bespoke test that is custom-tuned to each patient's prior tumor exome or large panel, adds more Medicare coverage from MolDx.

Coverage is for use in neoadjuvant breast cancer therapy (chemotherapy given prior to the initial lumpectomy), the first coverage of this category for MolDx.   In another first, Natera also announced coverage for MRD in ovarian cancer.

See the press release here:

The Natera Press Release - Structure and Function

In January, I wrote a blog article about how the Natera CEO presentation at JP Morgan was richly infused with facts and capabilities about their reimbursement expertise and tactics (here).  

Natera's new press release, just cited, is worth studying for its use of carefully footnoted facts, call-outs to guidelines, and links to key supporting publications (#4, #7).  The press release even includes selected citations to the size of population studied, number of unique biosamples, and some statistics.  The study cites pivotal publications in 2022 and 2023, which is related to MolDx reliance on published trials, and MolDx timeline (counted in quarters) for review of new indications not covered before.

Both key publications (Hou 2022 in ovarian and Magbanua 2023 in breast) are open access.  Example of the data power are shown in Hou 2022, Fig 3, where ctDNA+ relapse rate was 13%, vs 75% in ctDNA+.  Although the "N" gets smaller +20 months, relapse rate for ctDNA- remains low.

Hou 2022; Ovarian; Click to enlarge

The Magbanua paper includes online supplemental data such as patient-level Excel charts.

Magbanua 2023; open access patient level data in Excel


See open access coverage at Genomeweb here;

Friday, February 23, 2024

Journal Club: Peabody's 2021 Paper on Success and Failure in Diagnostic Startups

Header:  John Peabody founded the medical consultancy QURE.  In 2021, they published a paper on diagnostic startups, successes and failures, in "BMJ Innovations."


John Peabody MD founded the consultancy QURE (later acquired, still active).   

I stumbled across an interesting 2021 paper of his - find the article online here.  (Firewall).  For the BMJ Innovations journal home page - here.  

I clip the title and abstract below.   One of the coauthors,  Mickey Urdea, revisited in 2023.  See a 45 minute vlog.


Postmortems on diagnostic testing start-ups: 

Reports of commercial successes and failures and the case of the Zombie life science company.


Background From 2014 to 2017, more than 1000 diagnostic companies were launched, securing more than US$10 billion in investment.

Methods We performed an in-depth exploration of 28 diagnostic companies to differentiate successful and failed startups, plus a third ‘Zombie’ state where companies have achieved financial solvency but without long-term viability.

Results From these data, we created a five-phase, 13-item framework indicating the corporate health of a diagnostic company as it progresses from conception to commercialisation. We found 6 successful companies, 14 failures and 8 Zombies. On a scale of 0–26 points (two points per item), successful companies averaged 24.5 points (range 22–26), failures averaged 4.5 (range 0–16) and Zombies averaged 12.3 (range 3–23) (p<0.001).

To determine if there was any predictivity to this framework, we looked at only the first two phases (concept and feasibility/planning) of progress and found a distinct gradient in success potential based solely on these first two phases.

Conclusion Our five-phase framework generated a score that could predict diagnostic companies more likely to successfully and sustainably enter the market from those more likely to fail.


Take-home lesson, reading the business features left to right across the five columns, successes had mostly "green lights" as seen in the top rows. Failures had lots of "red lights" (bottom rows).


Not too surprisingly, QURE, which focused on helping startups with early commercial planning, found that higher quality early commercial planning was important.  (I focus on some of the same in a 2024 white paper on valuation and due diligence for genomics, here).

I also looked up the QURE paper at Semantic Scholar, a free resource..  This provides an interesting feature for every article - TLDR - which means "Too Long Didn't Read," essentially a 1-sentence summary of every paper it hits.  The webpage at Semantic Scholar for the paper should be here. And this is the automatic TLDR:

  • TLDR- "An in-depth exploration of 28 diagnostic companies to differentiate successful and failed startups, plus a third ‘Zombie’ state where companies have achieved financial solvency but without long-term viability. Generated a five-phase, 13-item framework indicating the corporate health of a diagnostic company as it progresses from conception to commercialisation."

Semantic Scholar lets you save the hit in a private library, get code to input into reference managers, etc.



I ran the PDF through  (More on Perplexity).


The paper titled "Postmortems on diagnostic testing start-ups: reports of commercial successes and failures and the case of the Zombie life science company" by Peabody et al. (2021) explores the outcomes of 28 diagnostic companies, categorizing them as successful, failed, or in a 'Zombie' state. The study presents a five-phase framework to assess the corporate health of diagnostic companies from conception to commercialization. Successful companies scored high across all phases, while failures lacked in meeting key criteria. Notably, failures often stemmed from issues like inadequate market understanding, technical barriers, or lack of clinical utility evidence. The study emphasizes the importance of thorough planning, market understanding, and meeting clinical requirements for successful diagnostic startups

Main Argument?

The main argument of the paper "Postmortems on diagnostic testing start-ups: reports of commercial successes and failures and the case of the Zombie life science company" by Peabody et al. (2021) is to present a comprehensive framework consisting of five phases and 13 items to evaluate the corporate health of diagnostic companies from inception to commercialization. The study aims to differentiate successful companies, failed startups, and those in a 'Zombie' state, where companies achieve financial solvency but lack long-term viability. By analyzing 28 diagnostic companies, the paper demonstrates that successful companies scored significantly higher across all phases compared to failures and Zombies. The framework developed in the study can predict which diagnostic companies are more likely to enter the market successfully and sustainably, highlighting the importance of strategic planning, market understanding, and meeting clinical requirements for the success of diagnostic startups

Key Findings?

The key findings of the paper "Postmortems on diagnostic testing start-ups: reports of commercial successes and failures and the case of the Zombie life science company" by Peabody et al. (2021) are as follows:

  1. The study analyzed 28 diagnostic companies and categorized them into successful companies, failed startups, and those in a 'Zombie' state.
  2. A five-phase, 13-item framework was developed to assess the corporate health of diagnostic companies from conception to commercialization.
  3. Successful companies scored significantly higher across all phases compared to failures and Zombies, with an average score of 24.5 out of 26.
  4. Failures often stemmed from issues such as inadequate market understanding, technical barriers, or lack of clinical utility evidence.

The framework created in the study could predict which diagnostic companies are more likely to enter the market successfully and sustainably, emphasizing the importance of strategic planning, market understanding, and meeting clinical requirements for the success of diagnostic startups

Weaknesses? Next steps?

The weaknesses of the paper "Postmortems on diagnostic testing start-ups: reports of commercial successes and failures and the case of the Zombie life science company" by Peabody et al. (2021) include:

  1. The study's sample size is relatively small, analyzing only 28 diagnostic companies, which may limit the generalizability of the findings.
  2. The paper does not provide detailed information on the specific characteristics or profiles of the companies analyzed, which could enhance the depth of the analysis.
  3. The study focuses on diagnostic companies in a specific timeframe (2014-2017), potentially limiting its relevance to current or future trends in the industry.

Good next steps for this research could involve:

  1. Conducting a larger-scale study involving a more extensive sample of diagnostic companies to increase the robustness and generalizability of the findings.
  2. Including more detailed case studies or profiles of the companies analyzed to provide a richer understanding of the factors contributing to success, failure, or 'Zombie' states.
  3. Extending the analysis to include diagnostic companies from a broader timeframe to capture evolving trends and dynamics in the industry.

These steps could further enhance the insights provided by the study and offer a more comprehensive understanding of the factors influencing the success or failure of diagnostic startups

As a Haiku?

Diagnostic startups,
Success, failure, Zombie states,
Framework predicts fate.

Thursday, February 22, 2024

AMA Posts Lab Test Agenda for May 2024 CPT Meeting

Header:  Months ahead of each CPT meeting, AMA publishes for comment the lab test agenda.


Months ahead of each CPT meeting, and before other codes, AMA publishes for comment the lab test agenda.  Find it here:

If you want to comment:

Read the PDF.  You must request a code of interest by February 29, and submit your comment by March 4.

In brief, lab codes are:

  1. MAAA code / bacterial vaginosis.
  2. CPT code - Neurofilament light chain
  3. CPT code - Pneumocyst. Jirovecii PCR
  4. CPT code - Optical genome mapping
  5. CPT code - Cell enrichment by beads (for FISH)
  6. 0500T > CPT Code - HPV genotyping
  7. CPT code - Strep Ab
  8. CPT code - cytokine panel
  9. CPT code - Revise carrier genetic testing (81412, 81433)
  10. CPT code - Revise cancer risk panels (81432, 81435, others)
Under #10, there are current cancer risk panels for HBOC (e.g. BRCA et al.) and Lynch (most often colorectal risk).  Those panels look to be revised, plus a new pan cancer panel 81X01.

The meeting will be May 9-11, 2024, in Chicago, both in person and virtual.  It looks like regular (non lab) CPT codes open for comment on March 1, 2024.  "Summary of Panel Actions" from the prior meeting a few weeks ago in San Diego, should appear by early March, but here.

AMA CPT comment portal:

AI Corner: SCITE, A Subscription Tool for Science & Policy Writing

Header:  SCITE is a subscription tool that supercharges your ability to instantly see and evaluate citations to an index paper.  In a few seconds, you get "the buzz" of what a dozen later papers think of your index paper.


There are coming to be hundreds of special AI tools, and a whole YouTube genre of reviewing them.  One that I've found useful a number of times is SCITE.   You give it an index paper, and it shows you all the papers that cite it.  But that's not what's new.  

SCITE also shows you the context in which each paper cites your index paper.  And that is truly a new capability.  Instead of finding and downloading a dozen PDFs known to cite your index paper, on one screen in a few seconds you can see the context of their remarks on your index paper.


Take an example. You figured out that the go-to paper on the fizzle of the CMS Oncology Care Model is JAMA, 2021, Keating.   Here it is highlighed as a key paper in SCITE.

One of the articles that cites KEATING, is BROOKS 2022.  OK, now in Brooks 2022, Keating is reference #13.   And SCITE will show you all 6 places where Brooks refers to Keating 2021, and the context, like a half paragraph.

One more example.  Landom 2023 also Keating multiple times,  But in Landom 2023, the Keating article is "Ref #7".  So here, SCITE pulls up the places where Landom cites to Landom's Ref 7 which is Keating 2021.

There are many more bells and whistles in SCITE but this a key capability.
Other resources of potential interest are Semantic Scholar, Google Biomed Explorer.  See also the general AI  sources and  The latter 2 are both general AI's, with a free tier, but which are especially adept with citations.  I showed an example of drafting a footnoted article with Perplexity in an earlier blog.

See my mini review of SCISPACE, which is different but complemntary to SCITE.  

Nerd Note: CMS DeepSixes the Advanced Imaging "Appropriate Use Criteria" that Congress Gave It

Header:  The Lab Industry remembers PAMA 2014 for lab pricing law, but it also created Appropriate Use Criteria (AUC) for Imaging.  It's fallen down, as did a Radiation Oncology payment model.


PAMA #1 - Lab Price

PAMA, the Protecting Access to Medicare Act, in the lab industry is shorthand for a major new lab test repricing law, based on median community pricing of lab tests.  PAMA has been on hold and in limbo from 2020 to at least 2025.

PAMA #2, #3 - RVU Rules and AUC Project

Two other initiatives from PAMA were a section that gave CMS much more authority to set and revise RVU values, and a section that created mandatory "Appropriate Use Criteria" for advanced imaging (e.g. MRI).

The Advanced Imaging AUC Project

The AUC proceded in fits and starts and has finally been put on hold indefinitely, as per a recent notice to contractors from CMS.   More details could be found by perusing 8 years of Federal Register annual rulemakings on the topic.

Tie In to Genomics

I thought the AUC was worth watching becaise" if you could apply required AUC to orders for $600 MRI tests why not also apply required AUC rules to $3000 genomic tests.   


BTW - RO Model Also Deep Six'ed

Speaking of radiology, another issue was radiotherapy, where CMS proposed an unpopular Radiation Oncology bundled price model that was "modality agnostic."  This was extremely unpopular in the clinical community and was even delayed sometimes by an act of Congress.   See an extensive, 6-page letter on perceived severe problems with RO MODEL, written by numerous stakeholders to CMS in 2021.

See a 2021 article on RO MODEL by Milliman here.

In 2022, the RO MODEL was delayed or suspended to cancel a prior go live date in 2023, and stop or delay out to a future date "to be determined." 

See ASTRO,   

See also Fierce Healthcare on the same topic,

AI Corner

Perplexity discusses the 7-page ASTRO and multi stakeholder complaint to CMS, here.

Wednesday, February 21, 2024

A Different AI: "" Talks About Value-Based Care in Oncology

Header:  In a few seconds, an alternative, open-access AI (Perplexity) generates several short essays about the outlook for value-based oncology.


A few weeks ago, this blog highlighted a new article by two oncologists, Klein & Kolodziej, here.  Their new article is a look-back and look-forward relative to their ten-year-old forecast for value based care and value based payment in oncology.


Fast forward.  This week, I've had several sessions playing with the free level of   In the example below, I asked it three questions about value based care in oncology.  1) What's a general overview; 2) What's the main barriers, and 3) What's the five year outlook?   

In each case, Perplexity combines very rapid internet research and a strong general knowledge database (as in "regular" AI like Chat GPT).  

Perplexity also extensively footnotes its reports.   One of the big concerns of 2023, that AI's would not give sources and would make things up, is largely left in the rear view mirror, when you use Perplexity.   

Except for the text in red, everything below was written in a few seconds by Perplexity.   Some of it is sharp; some of it is harmless general prose.   It would certainly jump-start someone who had to think about this topic and didn't have a starting place.


Unfortunately, I couldn't get the Perplexity report to word-wrap correctly on this blog, so to read the report with the important web links, I'll need to send you either to Plexplexity (in open access mode) or to Google Doc, below.




All the text below, except that in red, were generated in a few seconds by Perplexity. To click on links, use the cloud based document resources above, not the passage below.


Part 1 - 

Overview of a Report on Value-Based Oncology

To write a comprehensive report on value-based oncology care, focusing on patient outcomes, cost-effectiveness, provider incentives, and challenges, it's essential to integrate findings from recent studies and analyses. Here's a structured approach to crafting your report:


Begin with an overview of value-based care in oncology, emphasizing the shift from volume-based to value-based models. Highlight the global need for affordable, accessible cancer care that produces optimal patient outcomes1.

Patient Outcomes

Discuss how value-based care aims to improve patient outcomes by focusing on the quality rather than the quantity of care. Mention the lack of standardization in measuring long-term patient outcomes and the need for universal clinical and patient-reported outcome measures (PROMs)1. Also, address the challenges in implementing these measures, especially in less developed countries1.


Examine the financial aspects of value-based care, including the rising cost of cancer care and the financial toxicity associated with it2. Discuss strategies to improve cost-effectiveness, such as using composite tools to assess the value of drugs and increasing cost communication between patients and oncologists2. Highlight the importance of frameworks like the ASCO Value Framework and the ESMO Magnitude of Clinical Benefit Scale in assessing the value of clinical interventions2.

Provider Incentives

Explore how reimbursement incentives influence physician practice in oncology. Mention that value-based reimbursement policies may align physician incentives with patient needs more effectively3. However, also note the skepticism among physicians regarding the impact of financial incentives on their practice3.


Identify the primary challenges in implementing value-based care models, such as navigating the payer landscape, identifying cost-reduction opportunities4, and accounting for patient and disease complexity5. Highlight specific examples where value-based models like the Oncology Care Model failed to account for these complexities, leading to financial losses5.


Conclude by emphasizing the potential of value-based care in oncology to align healthcare delivery with patient needs, improve outcomes, and manage costs. However, also acknowledge the significant challenges that need to be addressed, including standardizing outcome measures, ensuring cost-effectiveness, aligning provider incentives, and overcoming implementation barriers.

Throughout the report, use examples from the provided studies to illustrate both successful implementations and areas where value-based care models have fallen short. This balanced approach will provide a comprehensive view of the current state of value-based oncology care, its potential benefits, and the obstacles that must be overcome.

Part 2 - 

What are the key challenges to implementing value-based care in oncology?

The main challenges and hurdles that have impeded the development of value-based oncology payments include:

  1. Lack of Standardization in Measuring Performance: It is difficult to measure performance from clinic to clinic, cancer site to cancer site, or physician to physician. This lack of standardization makes it challenging to define value in cancer care3.
  2. Difficulty in Defining Value in Cancer Care: There is no unanimous agreement on what "value" means in cancer care, making it challenging to establish value-based payment models3.
  3. Application of Quality Measures: The main challenge of value-based cancer care for payers and providers is the application of quality measures. There is a big push to use pathways and guidelines in the entire oncology arena, but putting these pathways into place is a challenge3.
  4. Problems with Benchmarks: It's hard to get into a value-based care contract if there is no historical data to show how a provider performs. Additionally, benchmarks can be problematic if something changes in the regular treatment of a disease4.
  5. Reporting Burden: The amount of data that needs to be reported to take part in value-based care programs is substantial, and it can be challenging to capture all the necessary data4.
  6. Difficulty in Defining "Value": It is difficult to define value-based care in any area, particularly in cancer care. There are challenges in defining the value of a life and determining the point of diminishing returns for certain therapies3.
  7. Financial Toxicity for Patients: The financial fallout of a cancer diagnosis can play a significant role in patients' outcomes, leading to severe financial toxicity for many patients5.

These challenges have contributed to the complexity and slow progress of implementing effective value-based oncology payment models. Overcoming these hurdles will be essential for the successful development and implementation of value-based care in oncology.

Part 3 - 
What is the 5 year outlook? 
You are chief commercial officer, for biotech, precision oncology, and genomics.

The five-year outlook for value-based care in oncology presents a landscape of both potential growth and persistent challenges. Specialists are expected to accelerate the adoption of value-based care models, leading to increased effectiveness and scalability, particularly in fields like nephrology and oncology2. However, the transition to value-based care in oncology is not without its hurdles. 

The Oncology Care Model (OCM), a major attempt to introduce value-based care into oncology, faced difficulties, with the successor model, Oncology Care First, now in doubt5. The OCM demonstrated challenges in managing the cost of new and expensive cancer therapies, impacting the ability to achieve cost savings5. Despite these obstacles, the potential for growth in value-based care remains significant, with the landscape evolving to address issues of quality, outcomes, and cost management2.

In the next five years, the value-based care market could reach a valuation of $1 trillion, driven by increased adoption and enhanced capabilities across providers, employers, and other healthcare stakeholders2. The continued traction in the value-based care market is expected to lead to improved quality of care and outcomes while providing benefits for patients, clinicians, and investors2. However, it's crucial to address the challenges related to cost management, equity issues, and the development of effective payment models to ensure the success of value-based care in oncology5.

In summary, the five-year outlook for value-based care in oncology presents a landscape of potential growth and continued challenges. While the adoption of value-based care models is expected to accelerate, addressing issues related to cost management and equity will be crucial for its long-term success. The evolving landscape of value-based care in oncology holds significant potential for improving patient outcomes and delivering benefits across the healthcare ecosystem.

click to enlarge

Separately, Perplexity discusses a 7-page letter that stakeholders sent to CMS in 2021 opposing a radiation oncology value based purchasing proposal, here.

 (More on Perplexity).  (Here too.)

Tuesday, February 20, 2024

Medicare to Tighten Access to Research Data; Some Are Upset

Header: Some groups are quite upset at CMS proposals to close or tighten access to research data.


CMS has very extensive public data resources as well as massive data resources available through agreements and contracting.

According to several recent news sources, CMS plans to make the data access harder, to say the least.   See an article at Forbes here:

At Medriva here:


See a CMS announcement that the news articles are building on:


See recent articles about how $2B in Medicare fraud was picked up by outside parties (like ACO's) through their access to relevant CMS data - here.

I don't believe this affects currently open access data such as, "Medicare Physician & Other Practitioners by Provider and Service."  What does that mean?  Physician by Provider - doctor by doctor name.  By Service - by CPT code.  Each doctor and each paid CPT code he got, by year.   Here.