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9 - Practical Advice for Clinician–Engineer Partnerships for the Use of AI, Optimization, and Analytics for Healthcare Delivery

from Part II - Optimizing Healthcare Systems

Published online by Cambridge University Press:  21 April 2022

By
David Scheinker ,
Robert A. Harrington  and
Fatima Rodriguez
Edited by
Sze-chuan Suen ,
David Scheinker  and
Eva Enns
Sze-chuan Suen
Affiliation:
University of Southern California
David Scheinker
Affiliation:
Stanford University, California
Eva Enns
Affiliation:
University of Minnesota
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Summary

This chapter offers lessons from engineering and other industries that promise developments in healthcare, and practical guidance for clinician-engineer partnerships. Section 1 provides guidance on how to establish a shared vocabulary and common understanding between engineers and clinicians of what terms such as AI and ML do and don’t mean. Section 2 identifies challenges clinician-engineering partnerships must overcome to deliver sustained value and ways to avoid common causes of failure. Section 3 provides specific advice on how to design projects to produce value at a series of stages rather than rely on the success of one, ambitious final model. Section 4 concludes by drawing on cautionary lessons from healthcare and other industries.

Keywords

Clinician–engineer partnershipscautionary lessonshealthcare
Type
Chapter
Information
Artificial Intelligence for Healthcare
Interdisciplinary Partnerships for Analytics-driven Improvements in a Post-COVID World
 , pp. 182 - 192
Publisher: Cambridge University Press
Print publication year: 2022

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References

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