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

Abstract

BACKGROUND: 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. METHODS: 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. RESULTS: = 0.022). No statistically significant differences were observed between the active and the blank control arms. CONCLUSIONS: Receiving basic information on ML-based CDSS in hospital psychiatry may increase patient trust in such systems.

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