A framework for using AI to drive care model transformation: building cars rather than faster horses

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Authors: Ron Li, Leah Rosengaus, Lisa Gohil, Christopher Sharp, Michael A. Pfeffer, Niraj L. Sehgal, David Entwistle, Lawrence V. Hofmann
Cited by: 0
References: 19

Abstract

Despite advances in science and technology, persistent challenges in the delivery of healthcare call for care model transformations that have yet to be realized. Artificial intelligence could drive these transformations, but has yet to do so at scale. We present a four-layer framework for leveraging AI to design new care models: Knowledge (clinical content and institutional expertise), Intelligence (AI-powered synthesis and reasoning), Application (user interfaces), and Workflow (redesigned care processes). These layers are modular yet tightly interdependent, requiring cross-functional teams to design across the full stack. We illustrate this framework through an AI-enabled specialty consultation service deployed within Stanford Health Care, a quaternary academic medical center, that integrates all four layers to transform how expertise is delivered. This framework offers health system leaders a roadmap for moving beyond technology deployment toward systematic care model engineering—an organizational capability that will help shape the future of healthcare delivery.

Fields of Study & Subjects

Artificial Intelligence in Healthcare and Education Technology and Human Factors in Education and Health Artificial Intelligence Applications

Publication details

Journal / source
npj Health Systems
Volume
3
Issue
1
Publication date
28 January 2026
Language
EN