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

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. ### Competing Interest Statement AAD has received a speaker honorarium from Otsuka Pharmaceutical. SDØ received the 2020 Lundbeck Foundation Young Investigator Prize and SDØ owns/has owned units of mutual funds with stock tickers DKIGI, IAIMWC, SPIC25KL and WEKAFKI, and owns/has owned units of exchange traded funds with stock tickers BATE, TRET, QDV5, QDVH, QDVE, SADM, IQQH, IQQJ, USPY, EXH2, 2B76, IS4S, OM3X and EUNL. ### Funding Statement There was no specific funding for this study. Outside this study, SDØ reports funding from the Lundbeck Foundation (grants R358-2020-2341 and R344-2020-1073), the Novo Nordisk Foundation (grant NNF20SA0062874), the Danish Cancer Society (grant R283-A16461), the Central Denmark Region Fund for Strengthening of Health Science (grant 1-36-72-4-20), the Danish Agency for Digitisation Investment Fund for New Technologies (grant 2020-6720), and Independent Research Fund Denmark (grant 7016-00048B and 2096-00055A). These funders played no role in the design or conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Research studies based on surveys are exempt from ethical review board approval in Denmark (waiver no. 1-10-72-138-22 from the Central Denmark Region Committee on Health Research Ethics). The study was approved by the Legal Office in the Central Denmark Region (reg. no. 1-45-70-21-23) and registered on the internal list of research projects having the Central Denmark Region as data steward (reg. no. 1-16-02-170-23). Prior to the survey, to ensure that it was appropriate for the study population, we received feedback on the questionnaire and the intervention and active control information pamphlets from two patients having received treatment in the Psychiatric Services in the Central Denmark Region. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The data cannot be shared as the participants have not consented to data sharing.