M-RCBG Associate Working Paper No. 258
Harnessing Artificial Intelligence to Advance Value-Based Contracting in Health Care
Roshini Moodley Naidoo
Mohammed Y Safirulla Karamathulla
Amine Mernissi
Introduction
The US Health Care System is extraordinarily advanced, with a lattice of providers, payers, policy-makers, and intermediaries. The traditional fee for service (FFS) model, by which most care is funded in the nation, is recognized as a significant contributor to health care costs, incentivising volume over value. Value-Based Contracting (VBC) has been implemented as an alternate funding model to FFS, where value is a function of risk-sharing between payers and providers targeting quality and cost improvements. Arguably an over-simplification, and perhaps even an over-reach of what is practically possible within medium-term horizons, the defining VBC principles still provide a strategic and operational compass to guide strategy. In the US, the Affordable Care Act (ACA 2010) fostered a prioritization of Value-Based Care, where VBC programs introduced as part of the ACA, linked provider reimbursement to performance on quality and cost. While aspirational, VBC models continue to face challenges in conceptualisation and implementation, contributing to slower than expected adoption.
This Working Paper is structured in two parts. The first part deals with the evolution of VBC in the US and provides an analysis of a Medicare program – the Merit Based Incentive Payment System (MIPS) which offers physicians financial incentives based on their performance on selected metrics. Though MIPS is not a VBC program in its purest form, it provides an opportunity to understand implementation challenges when shifting from FFS towards VBC that impact adoption of the latter. The second part of the paper explores the opportunities presented by emerging AI technologies by examining four Use Cases. We assess the potential of AI technologies to improve the dual goals of VBC – increasing quality and reducing cost. Additionally, our paper examines the potential of AI solutions to increase the shift to VBC strategies by mitigating implementation challenges.