Important article from Drs. Kolodziej and Klein. See a new six-page review in Journal of Clinical Oncology / Oncology Practice, on lost opportunities in cancer policy from a payer perspective. Also a fair bit of discussion of CMS efforts like Oncology Care Model (OCM). The authors revisit topics with reference to their 2013 viewpoints and predictions. Open Access.
The authors make clear the review is going to be rocky one:
- The cost of health care as a proportion of the US economy has increased dramatically.(fn) The cost of cancer care is a major contributor to these cost trends.
- Predictions made in 2013 about how public and private payers would interact to build future models of cancer care and reimbursement were off the mark.(fn)
- A clear-eyed review of where those predictions went awry and where the health care industry has changed over the past 10 years can inform as to how to best move forward.
I've included an AI summary:
"Private Payers and Cancer Care: Revisiting the Land of Opportunity" by Michael A. Kolodziej and Ira Klein, discusses significant changes in the landscape of cancer care and payment models over the past decade.
Here are seven key points:
Missed Opportunities in Value-Based Oncology Programs: The article reviews the last ten years of experiments in value-based oncology programs by both public and private payers, noting their limited success in large-scale adoption and cost reduction.
Influence of the Oncology Care Model (OCM): The authors analyze the impact of the OCM, its goals, and its shortcomings, particularly in generating savings for the Medicare program.
Evolving Oncology Marketplace: The article discusses changes in the oncology marketplace, including consolidation in healthcare systems and the increasing influence of payers and pharmaceutical companies. [BQ - consolidation includes vertical integration; their Table 1. Cf also, NEJM 2024, Bruch, Financialization of health care.]
Cost and Innovation in Cancer Treatment: There's an emphasis on the cost of innovation in cancer treatment, particularly the high R&D expenses and the financial burden of new therapies. [BQ - CAR-T and checkpoint inhibitors are highlighted].
Data and Information Technology: The authors explore the unrealized potential of data and information technology in oncology, highlighting limitations in electronic medical records and data accessibility.
Future Oncology Payment Models: The article suggests improvements for future oncology payment models, including more innovative and mandatory approaches, quality-focused measures, and leveraging healthcare consolidation for better population health management. [BQ - See new cancer care navigator payments.]
Technological Advances and Patient Engagement: Lastly, the authors discuss the role of technology in patient engagement and monitoring, including telemedicine and electronic patient-reported outcomes, and the potential of AI in oncology care.
Their 2013 article is here:
In addition to the above summary of the new article, I think Chat GPT 4 did a fairly good job in a second task, reading both articles together and giving a "contrast and compare" summary. Here:
Chat GPT 4 Contrast & Compare
The 2014 article "Private Payers and Cancer Care: Land of Opportunity" by Ira Klein and Michael Kolodziej presents a vision for oncology care and payment reform, driven primarily by private payers. It emphasized the role of private payers in spearheading oncology payment reform, leveraging measurement and information technology, and using pathways and guideline support tools. The focus was on creating new models of care and reimbursement, enhancing quality, reducing costs, and preparing for future public-payer episodes-of-care programs.
In contrast, the 2024 article "Private Payers and Cancer Care: Revisiting the Land of Opportunity" by the same authors reflects on the missed opportunities and shortcomings of these initiatives over the past decade. It discusses the limited success in large-scale adoption and cost reduction of value-based oncology programs, critiques the impact of the Oncology Care Model (OCM), and notes the changes in the oncology marketplace. The article also highlights the high costs of innovation in cancer treatment, the unrealized potential of data and information technology, and suggests improvements for future oncology payment models.
The biggest "misses" of the 2014 outlook compared to the 2024 result include:
- Overestimation of the impact and adoption of value-based oncology programs.
- Underestimation of the challenges in implementing and scaling the OCM.
- An optimistic view of the potential of information technology in oncology, which was not fully realized.
- A lack of foresight into the evolving dynamics of the healthcare system, particularly the consolidation of healthcare systems and the increasing influence of pharmaceutical companies.
Overall, the viewpoint in 2024 is more critical and reflective, acknowledging the complexities and challenges in oncology care and payment reform that were not fully anticipated in 2014.
Finally, I asked GPT4 if AI could help any of the problems highlighted by the authors. The authors highlight in the conclusions, "AI has tremendous potential to improve patient care, but to date has minimally affected cancer care. Watson..failed spectacularly."
Can any of the "misses" or problems be addressed by wider use of large language models and other forms of artificial intelligence in clinical oncology?
Large language models and other forms of artificial intelligence could potentially address some of the "misses" or problems in clinical oncology identified in the articles:
- Data Analysis and Insights: AI can analyze vast amounts of clinical data to provide insights, identify patterns, and support decision-making, potentially improving treatment outcomes and cost-efficiency.
- Personalized Treatment Plans: AI can help in tailoring treatment plans to individual patients, increasing the effectiveness of treatments while possibly reducing costs.
- Streamlining Clinical Trials: AI could streamline the process of clinical trials, making it easier to identify suitable candidates and analyze results, accelerating the availability of innovative treatments.
- Predictive Analytics: AI can be used for predictive analytics in oncology, helping to anticipate disease progression and response to treatment.
- Enhancing Electronic Medical Records: AI could improve the functionality and interoperability of electronic medical records, making data more accessible and usable for healthcare providers.
- Patient Engagement and Monitoring: AI, through apps and monitoring tools, can enhance patient engagement and provide continuous health monitoring, which is crucial in cancer care.
In summary, AI offers numerous opportunities to address the challenges and shortcomings in clinical oncology care and payment models identified over the past decade.
A bit more AI Q&A in a sidebar.