Physician Usage and Acceptance of a Machine Learning Recommender System for Simulated Clinical Order Entry.

Chiang J, Kumar A, Morales D, Saini D, Hom J, Shieh L, Musen M, Goldstein MK, Chen JH. Physician Usage and Acceptance of a Machine Learning Recommender System for Simulated Clinical Order Entry. AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science. 2020 May 30; 2020:89-97.

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Abstract:

Clinical decision support tools that automatically disseminate patterns of clinical orders have the potential to improve patient care by reducing errors of omission and streamlining physician workflows. However, it is unknown if physicians will accept such tools or how their behavior will be affected. In this randomized controlled study, we exposed 34 licensed physicians to a clinical order entry interface and five simulated emergency cases, with randomized availability of a previously developed clinical order recommender system. With the recommender available, physicians spent similar time per case (6.7 minutes), but placed more total orders (17.1 vs. 15.8). The recommender demonstrated superior recall (59% vs 41%) and precision (25% vs 17%) compared to manual search results, and was positively received by physicians recognizing workflow benefits. Further studies must assess the potential clinical impact towards a future where electronic health records automatically anticipate clinical needs.