Literature DB >> 34888469

Precision reimbursement for precision medicine: the need for patient-level decisions between payers, providers and pharmaceutical companies.

Sanjay Budhdeo1, Michael Ruhl2, Paul M Agapow3, Nikhil Sharma4, Parker Moss5.   

Abstract

Healthcare costs have been dramatically rising in developed economies worldwide. A key driver of cost increases has been high-cost drugs. The current model of reimbursement is not configured for drugs with uncertain outcomes. Future reimbursement will require better allocation of available healthcare system funds. Technological advancements have opened the door to a new type of outcomes-based reimbursement, enabling value exchange between payers and pharmaceutical companies, which we term precision reimbursement. Precision reimbursement extends beyond value-based contracts, with decisions at individual rather than aggregate level. For precision reimbursement to be adopted, there are data, computation and infrastructure requirements. All stakeholders benefit in moving to precision reimbursement for optimal resource allocation, risk sharing and, ultimately, improved outcomes. There are implementation challenges including cost, change management, information governance and development of surrogate markers. The overarching trend in medicine is toward personalised interventions, with precision reimbursement as the logical consequence. © Royal College of Physicians 2021. All rights reserved.

Entities:  

Keywords:  precision medicine; reimbursement; stratified medicine; value-based healthcare

Year:  2021        PMID: 34888469      PMCID: PMC8651321          DOI: 10.7861/fhj.2021-0066

Source DB:  PubMed          Journal:  Future Healthc J        ISSN: 2514-6645


  12 in total

1.  It takes a planet.

Authors:  Eric Topol; Kai-Fu Lee
Journal:  Nat Biotechnol       Date:  2019-08       Impact factor: 54.908

2.  The Evolving Uses of "Real-World" Data.

Authors:  Ethan Basch; Deborah Schrag
Journal:  JAMA       Date:  2019-04-09       Impact factor: 56.272

3.  Real-World Evidence and Real-World Data for Evaluating Drug Safety and Effectiveness.

Authors:  Jacqueline Corrigan-Curay; Leonard Sacks; Janet Woodcock
Journal:  JAMA       Date:  2018-09-04       Impact factor: 56.272

4.  Innovation in the pharmaceutical industry: New estimates of R&D costs.

Authors:  Joseph A DiMasi; Henry G Grabowski; Ronald W Hansen
Journal:  J Health Econ       Date:  2016-02-12       Impact factor: 3.883

5.  Deep learning-based classification of mesothelioma improves prediction of patient outcome.

Authors:  Pierre Courtiol; Charles Maussion; Françoise Galateau-Sallé; Gilles Wainrib; Thomas Clozel; Matahi Moarii; Elodie Pronier; Samuel Pilcer; Meriem Sefta; Pierre Manceron; Sylvain Toldo; Mikhail Zaslavskiy; Nolwenn Le Stang; Nicolas Girard; Olivier Elemento; Andrew G Nicholson; Jean-Yves Blay
Journal:  Nat Med       Date:  2019-10-07       Impact factor: 53.440

6.  The Role of Patient-Reported Outcome Measures in Value-Based Payment Reform.

Authors:  Lee Squitieri; Kevin J Bozic; Andrea L Pusic
Journal:  Value Health       Date:  2017-03-22       Impact factor: 5.725

Review 7.  High-performance medicine: the convergence of human and artificial intelligence.

Authors:  Eric J Topol
Journal:  Nat Med       Date:  2019-01-07       Impact factor: 53.440

8.  Predicting conversion to wet age-related macular degeneration using deep learning.

Authors:  Jason Yim; Reena Chopra; Terry Spitz; Jim Winkens; Annette Obika; Christopher Kelly; Harry Askham; Marko Lukic; Josef Huemer; Katrin Fasler; Gabriella Moraes; Clemens Meyer; Marc Wilson; Jonathan Dixon; Cian Hughes; Geraint Rees; Peng T Khaw; Alan Karthikesalingam; Dominic King; Demis Hassabis; Mustafa Suleyman; Trevor Back; Joseph R Ledsam; Pearse A Keane; Jeffrey De Fauw
Journal:  Nat Med       Date:  2020-05-18       Impact factor: 53.440

9.  Machine learning application for development of a data-driven predictive model able to investigate quality of life scores in a rare disease.

Authors:  Ottavia Spiga; Vittoria Cicaloni; Cosimo Fiorini; Alfonso Trezza; Anna Visibelli; Lia Millucci; Giulia Bernardini; Andrea Bernini; Barbara Marzocchi; Daniela Braconi; Filippo Prischi; Annalisa Santucci
Journal:  Orphanet J Rare Dis       Date:  2020-02-12       Impact factor: 4.123

Review 10.  The future of digital health with federated learning.

Authors:  Nicola Rieke; Jonny Hancox; Wenqi Li; Fausto Milletarì; Holger R Roth; Shadi Albarqouni; Spyridon Bakas; Mathieu N Galtier; Bennett A Landman; Klaus Maier-Hein; Sébastien Ourselin; Micah Sheller; Ronald M Summers; Andrew Trask; Daguang Xu; Maximilian Baust; M Jorge Cardoso
Journal:  NPJ Digit Med       Date:  2020-09-14
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