Abstract
Decision support systems embodying machine learning models offer the promise of an improved standard of care for major depressive disorder, but little is known about how clinicians’ treatment decisions will be influenced by machine learning recommendations and explanations. We used a within-subject factorial experiment to present 220 clinicians with patient vignettes, each with or without a machine-learning (ML) recommendation and one of the multiple forms of explanation. We found that interacting with ML recommendations did not significantly improve clinicians’ treatment selection accuracy, assessed as concordance with expert psychopharmacologist consensus, compared to baseline scenarios in which clinicians made treatment decisions independently. Interacting with incorrect recommendations paired with explanations that included limited but easily interpretable information did lead to a significant reduction in treatment selection accuracy compared to baseline questions. These results suggest that incorrect ML recommendations may adversely impact clinician treatment selections and that explanations are insufficient for addressing overreliance on imperfect ML algorithms. More generally, our findings challenge the common assumption that clinicians interacting with ML tools will perform better than either clinicians or ML algorithms individually.
Original language | English (US) |
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Article number | 108 |
Journal | Translational psychiatry |
Volume | 11 |
Issue number | 1 |
DOIs | |
State | Published - Jun 2021 |
Funding
T.H.M. receives research funding from the Brain and Behavior Research Foundation (26489), National Institute of Mental Health (Supplement to R01MH104488), Telefonica Alfa, and the Stanley Center at the Broad Institute. R.H.P. holds equity in Psy Therapeutics and Outermost Therapeutics; serves on the scientific advisory boards of Genomind and Takeda; and consults to RID Ventures. R.H.P. receives research funding from NIMH, NHLBI, NHGRI, and Telefonica Alfa. R.H.P. is an associate editor for JAMA Network Open. F.D.V. consults with Davita Kidney Care and Google Health via Adecco. K.Z.G. receives research funding from Biogen. This study was funded by the Harvard Data Science Initiative.
ASJC Scopus subject areas
- Psychiatry and Mental health
- Biological Psychiatry
- Cellular and Molecular Neuroscience