Receiving information on machine learning-based clinical decision support systems in psychiatric services may increase patient trust in these systems: A randomised survey experiment

Abstract Background Clinical decision support systems 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, we examined whether receiving basic information about ML-based clinical decision support systems increased trust in them. Methods We conducted an online randomised survey experiment among patients receiving treatment in the Psychiatric Services of the Central Denmark Region. The participants were randomised to one of three arms, receiving different types of information: Intervention = information on clinical decision making supported by an ML model; Active control = information on a standard clinical decision process without ML-support; Blank control = no information. The participants were unaware of the randomization and the experiment. Subsequently, the participants were asked about different aspects of trust/distrust in ML-based clinical decision support systems. The effect of the intervention was assessed by comparing pairwise comparisons between all arms on component scores of trust and distrust. Findings Out of 5800 invitees, 992 completed the survey experiment. The intervention increased trust in ML-based clinical decision support systems when compared to the active control (mean absolute difference in trust: 5% [95%CI: 1%;9%], p-value= 0·009) and the blank control arm (mean absolute difference in trust: 4% [1%;8%], p-value=0·015). Similarly, the intervention significantly reduced distrust in ML-based clinical decision support systems when compared to the active control (mean absolute difference in distrust -3%[-5%; -1%], p-value=0·021) and the blank control arm (mean absolute difference in distrust -4% [-8%; -1%], p-value=0·022). For both trust and distrust, there were no material or statistically significant differences between the active and the blank control arms. Interpretation Receiving information on ML-based clinical decision support systems in hospital psychiatry may increase patient trust in such systems. Hence, implementation of this technology could ideally be accompanied by information to patients. Funding None. Research in context Evidence before this study Clinical decision support systems based on machine learning (ML) models are emerging within psychiatry. However, if patients do not trust this technology, its implementation may disrupt the patient-clinician relationship. Unfortunately, there is only little knowledge on opinions on ML models as decision support among patients receiving treatment in psychiatric services. Also, it remains unknown whether receiving basic information about ML-based clinical decision support systems increases patients’ trust in them. We searched PubMed on Sep 12, 2023, with the terms “((survey) OR (survey experiment)) AND (patients) AND ((opinions) OR (attitudes) OR (trust)) AND ((machine learning) OR (artificial intelligence)) AND ((Psychiatry) OR (Mental Disorders) OR (Mental Health))” with no language restrictions. This yielded a total of 73 records, none of which surveyed a patient population from psychiatric services. Only two studies were directly relevant for the topic at hand. One surveyed patients from a general hospital system in the United States about the use of ML-based prediction of suicide risk based on electronic health record data. The results showed that patients were generally supportive of this data use if based on consent and if there was an opportunity to opt out. The other study surveyed women from the general population about their opinion on the use of artificial intelligence (AI)-based technologies in mental healthcare. The results showed that the respondents were generally open towards such technologies but concerned about potential (medical harm) and inappropriate data sharing. Furthermore, the respondents identified explainability, i.e., understanding which information drives AI predictions, as being of particular importance. Added value of this study To the best of our knowledge, this is the first study to investigate opinions on ML-based clinical decision-support systems among patients receiving treatment in psychiatric services. On average, patients were open towards the use of ML-based clinical decision-support systems in psychiatry. Furthermore, the results suggest that providing basic information about this technology seems to increase patient trust in it, albeit with a small effect size. Finally, the results support prior reports on the importance of explainability for acceptance. Implications of all the available evidence Receiving information on ML-based clinical decision support systems in hospital psychiatry, including how they work (explainability), may increase patient trust in such systems. Hence, successful implementation of this technology likely requires information of patients.