Enterprise AI Analysis
A framework for using AI to drive care model transformation: building cars rather than faster horses
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.
Authors: Ron C. Li, Leah Rosengaus, Lisa Gohil, Christopher Sharp, Michael A. Pfeffer, Niraj Sehgal, David Entwistle & Lawrence V. Hofmann
Published: January 2026
Executive Impact
This paper introduces a critical framework for healthcare leaders to move beyond simple AI deployment towards systematic AI-enabled care model engineering. It emphasizes that AI should be seen as an engine for designing entirely new 'vehicles' of care delivery, rather than merely enhancing existing 'horse-drawn carriages'. The proposed four-layer framework—Knowledge, Intelligence, Application, and Workflow—provides a structured approach to integrate AI effectively, transforming how healthcare expertise is delivered and scaled. This strategic shift is crucial for addressing growing population demands, escalating care complexity, and workforce limitations.
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Deep Analysis & Enterprise Applications
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Layers in AI-enabled Care Model Framework
| Feature | Traditional Care Model | AI-enabled Care Model |
|---|---|---|
| Knowledge Base |
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| Decision-Making |
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| User Interface |
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| Workflows |
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AI-enabled eConsult Program Flow
PCP Submits Consultation Question
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AI Extracts & Synthesizes EHR Data
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AI Drafts Response (Grounded in Knowledge Layer)
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Specialist Reviews & Refines AI Draft
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Specialist Communicates with PCP/Patient
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AI Learns from Completed Consult
Stanford Health Care: AI-enabled eConsults
Stanford Health Care implemented an AI-enabled eConsult program leveraging the four-layer framework. This transformed their specialty consultation service by embedding AI across Knowledge (AI-assisted templates learning from completed consults), Intelligence (generative AI models for data extraction and drafting responses), Application (integrated EHR interface with visualizations), and Workflow (redistribution of specialist cognitive labor towards review and engagement). The initiative significantly improved access to specialty care, scalable expertise delivery.
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Your AI Transformation Roadmap
A phased approach to integrating AI for systematic care model engineering, ensuring sustainable impact.
Discovery & Strategy
Assess current care models, identify AI integration points, define transformation goals, and develop a strategic roadmap aligned with organizational objectives.
Framework Design & Development
Design the four layers (Knowledge, Intelligence, Application, Workflow), build AI models, develop interfaces, and establish data pipelines for continuous learning.
Pilot & Iteration
Implement AI-enabled care models in a controlled pilot environment, gather feedback, iterate on designs, and refine AI performance and workflows.
Scaling & Continuous Improvement
Expand successful models across the health system, establish robust monitoring, and embed continuous learning mechanisms for ongoing optimization and evolution.
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