Patient trust in the use of machine learning-based clinical decision support systems in psychiatric services: A randomized survey experiment.

Abstract

Clinical decision support systems (CDSS) based on machine-learning (ML) models are emerging within psychiatry. If patients do not trust this technology, its implementation may disrupt the patient-clinician relationship. Therefore, the aim was to examine whether receiving basic information about ML-based CDSS increased trust in them. We conducted an online randomized survey experiment in the Psychiatric Services of the Central Denmark Region. The participating patients were randomized into one of three arms: Intervention=information on clinical decision-making supported by an ML model; Active control=information on a standard clinical decision process, and Blank control=no information. The participants were unaware of the experiment. Subsequently, participants were asked about different aspects of trust and distrust regarding ML-based CDSS. The effect of the intervention was assessed by comparing scores of trust and distrust between the allocation arms. Out of 5800 invitees, 992 completed the survey experiment. The intervention increased trust in ML-based CDSS when compared to the active control (mean increase in trust: 5% [95% CI: 1%; 9%], p=0.0096) and the blank control arm (mean increase in trust: 4% [1%; 8%], p=0.015). Similarly, the intervention reduced distrust in ML-based CDSS when compared to the active control (mean decrease in distrust: -3%[-1%; -5%], p=0.021) and the blank control arm (mean decrease in distrust: -4% [-1%; -8%], p=0.022).No statistically significant differences were observed between the active and the blank control arms. Receiving basic information on ML-based CDSS in hospital psychiatry may increase patient trust in such systems.