Receiving Information on Machine Learning-Based Clinical Decision Support Systems in Psychiatric Services Increases Staff Trust in These Systems: A Randomized Survey Experiment

Abstract

Background: Clinical decision support systems based on machine learning (ML) models are emerging within psychiatry. To ensure their successful implementation, healthcare staff needs to trust these systems. Here, we investigated if providing staff with basic information about ML-based clinical decision support systems enhances their trust in them. Methods: We conducted a randomised survey experiment among staff in the Psychiatric Services of the Central Denmark Region. The participants were allocated to one of three arms, receiving different types of information: An intervention arm (receiving information on clinical decision-making supported by an ML model); an active control arm (receiving information on standard clinical decision process without ML support); and a blank control arm (no information). Subsequently, participants responded to various questions regarding their trust/distrust in ML-based clinical decision support systems. The effect of the intervention was assessed by pairwise comparisons between all randomization arms on sum scores of trust and distrust. Results: Among 2838 invitees, 780 completed the survey experiment. The intervention enhanced trust and diminished distrust in ML-based clinical decision support systems compared with the active control arm (Trust: mean difference = 5% [95% confidence interval (CI): 2%; 9%], p value < 0.001; Distrust: mean difference = −4% [−7%; −1%], p value = 0.042) and the blank control arm (Trust: mean difference = 5% [2%; 11%], p value = 0.003; Distrust: mean difference = −3% [−6%; −1%], p value = 0.021). Conclusion: Providing information on ML-based clinical decision support systems in hospital psychiatry may increase healthcare staff trust in such systems.