Étude et rapport

Paying for artificial intelligence in medicine

Paying for artificial intelligence in medicine

GRATUIT

Auteur(s) :

Ravi B. Parikh, Lorens A. Helmchen

Éditeur(s) :

www.nature.com/npjdigitalmed

Date de publication :20/05/2022

05 pages

EN BREF ...

" Over the past 7 years, regulatory agencies have approved hundreds of artificial intelligence (AI) devices for clinical use. In late 2020, payers began reimbursing clinicians and health systems for each use of select image-based AI devices. The experience with traditional medical devices has shown that per-use reimbursement may result in the overuse use of AI. We review current models of paying for AI in medicine and describe five alternative and complementary reimbursement approaches, including incentivizing outcomes instead of volume, utilizing advance market commitments and time-limited reimbursements for new AI applications, and rewarding interoperability and bias mitigation. As AI rapidly integrates into routine healthcare, careful design of payment for AI is essential for improving patient outcomes while maximizing cost-effectiveness and equity..."

Rédacteur(s) de la fiche :

MISE À JOUR

01/01/2022

05 PAGES
PDF, 362koMo

  • AI IN THE CURRENT REIMBURSEMENT LANDSCAPE
  • CURRENT STATE OF AI REIMBURSEMENT IN HEALTHCARE
  • PER-USE AI REIMBURSEMENT MAY LEAD TO OVERUSE
  • FORGOING SEPARATE REIMBURSEMENT OF AI DEVICES
  • INCENTIVIZE OUTCOMES INSTEAD OF VOLUME
  • ADVANCE MARKET COMMITMENTS FOR NEW AI SOLUTIONS
  • TIME-LIMITED ADD-ON REIMBURSEMENTS FOR NOVEL AI
  • REWARD INTEROPERABILITY AND BIAS MITIGATION
  • CONCLUSION

"..AI in medicine offers the potential to improve patient outcomes, provider productivity, and equity in healthcare delivery. The European Commission’s Executive Agency for Small and Mediumsized Enterprises (EASME), the FDA’s Digital Health Center for Excellence, and CMS all have recognized reimbursement for AI as a major priority. The approaches we have described above can guide reimbursement policy to prioritize value and disincentivize overuse. In combination with careful regulation, a reimbursement model that recognizes AI’s rapid scalability and automation will reward value rather than volume—sending an important signal as AI rapidly integrates into routine healthcare to improve patient outcomes."