价值(数学)
经济
业务
计算机科学
精算学
机器学习
作者
Özge Yapar,Stephen E. Chick,Noah Gans
出处
期刊:Management Science
[Institute for Operations Research and the Management Sciences]
日期:2024-11-19
标识
DOI:10.1287/mnsc.2022.03628
摘要
Health technology assessments often inform decisions made by public payers, such as the UK’s National Health Service, as they negotiate the pricing of companies’ new health technologies. A common assessment mechanism compares the incremental cost-effectiveness ratio (ICER) of the new health technology, relative to a standard of care, to a maximum threshold on the cost per quality-adjusted life year. In much research and practice, these assessments may not distinguish between cost-per-patient and negotiated price, effectively ignoring the value-based-pricing principle that better health outcomes merit higher prices. Other research makes this distinction, but it does not account for uncertainty in the ICER associated with clinical trial data that are limited in size and scope. This paper models the strategic behavior of a payer and a company as they price a new health technology, and it considers the use of conditional approval (CA) schemes whose post-marketing trials reduce ICER uncertainty before final pricing decisions are made. Analytical results suggest a very different view of the value-based pricing negotiations underlying these schemes: interim prices used during CA post-marketing trials should reflect cost-sharing for the CA scheme, not just cost-effectiveness goals for a treatment. Moreover, the types of caps on interim prices used by entities such as the UK Cancer Drugs Fund may hinder the development of new technologies and lead to suboptimal CA designs. We propose a new risk-sharing mechanism to remedy this. Numerical results, calibrated to approval data of an oncology drug, illustrate the issues in a practical setting. This paper was accepted by Stefan Scholtes, healthcare management. Funding: Financial support from the Mack Institute for Innovation Management at the Wharton School to the authors and the support of Dr. Simba Gill and Sabi Dau to the INSEAD Healthcare Management Initiative are gratefully acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.03628 .
科研通智能强力驱动
Strongly Powered by AbleSci AI