Early Investments for Teaming Dividends: A Human-Centered Approach to a Patient Decompensation Prediction Algorithm

Deployments of artificial intelligence (AI) and machine learning (ML) in healthcare can both help and harm patient outcomes, amplifying calls for a human-centered approach to AI/ML development. This paper details one approach guided by three principles: (1) pursue human-machine team (HMT) performanc...

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Published inProceedings of the International Symposium of Human Factors and Ergonomics in Healthcare Vol. 13; no. 1; pp. 7 - 11
Main Authors Morey, Dane A., Gifford, Ryan C., Rayo, Michael F.
Format Journal Article
LanguageEnglish
Published Los Angeles, CA SAGE Publications 01.06.2024
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ISSN2327-8595
2327-8595
DOI10.1177/2327857924131046

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Summary:Deployments of artificial intelligence (AI) and machine learning (ML) in healthcare can both help and harm patient outcomes, amplifying calls for a human-centered approach to AI/ML development. This paper details one approach guided by three principles: (1) pursue human-machine team (HMT) performance, not algorithm performance, (2) build interpretability throughout, and (3) constrain development to deconstrain interactions. We describe how these principles influenced our development of two algorithms predicting patient decompensation events five minutes into the future. These algorithms showed comparable performance to other similar models with enhanced interpretability that greatly expanded HMT interaction possibilities. Our early investments in the potential for teaming appeared to pay dividends for the resultant HMT.
ISSN:2327-8595
2327-8595
DOI:10.1177/2327857924131046