Looking for clinician involvement under the wrong lamp post: The need for collaboration measures

Abstract

In a recent review published by Schwartz et al,1 an interdisciplinary team examines clinician involvement in machine learning research. The review includes 80 studies describing predictive clinical decision support systems (CDSSs) targeting clinicians for prognostic or treatment decision making in the hospital using electronic health record data. The objective of the review is to describe clinician involvement in these 80 studies and to map involvement across Stead’s 5 stages of system design.2 Unfortunately, the review makes 2 assumptions about interdisciplinary collaboration that undermine the analysis and interpretation of results. First, the review relies on a novel, highly constrained definition of clinician involvement. The constraints neglect substantial documentation of interdisciplinary collaboration, resulting in dramatic underestimates of collaboration. Second, the review misinterprets missing data. Studies that do not meet the constrained definition of clinician involvement are assumed to have been conducted without any clinician involvement. We highlight the weaknesses of...

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