Using AI to Empower Collaborative Team Workflows: Two Implementations for Advance Care Planning and Care Escalation

Abstract

SummaryTo facilitate the development of machine-learning (ML) models in care delivery, which remain poorly understood and executed, Stanford Medicine targeted an effort to address this implementation gap at the health system by addressing three key challenges: developing a framework for designing integration of artificial intelligence (AI) into complex health care work systems; identifying and building the teams of people, technologies, and processes to successfully develop and implement AI-enabled systems; and executing in a manner that is sustainable and scalable for the health care enterprise. In this article, the authors describe two pilots of real-world implementations that integrate AI into care delivery: one to improve advance care planning and the other to decrease unplanned escalations of care. While these two implementations used different ML models for different use cases, they shared a set of principles for integrating AI into care delivery. The authors describe how these shared principles were applied to the health system, the barriers and facilitators encountered, and how these experiences guided processes for collaboratively designing and implementing user-centered AI-enabled solutions.

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