A Mixed Methods Comparison of Artificial Intelligence-Powered Clinical Decision Support System Interfaces for Multiple Criteria Decision Making in Antidepressant Selection

BACKGROUND Artificial intelligence-powered clinical decision support systems (AI-CDSS) have recently become foci of research. When clinicians face decisions about treatment selection, they must contemplate multiple criteria simultaneously. The relative importance of these criteria often depends on the clinical scenario, as well as clinician and patient preferences. It remains unclear how AI-CDSS can optimally assist clinicians in making these complex decisions. In this work we explore clinician reactions to different presentations of AI results in the context of multiple criteria decision-making during treatment selection for major depressive disorder. METHODS We developed an online platform for depression treatment selection to test three interfaces. In the probabilities alone (PA) interface, we presented probabilities of remission and three common side effects for five antidepressants. In the clinician-determined weights (CDW) interface, participants assigned weights to each of the outcomes and obtained a score for each treatment. In the expert-derived weights interface (EDW), outcomes were weighted based on expert opinion. Each participant completed three clinical scenarios, and each scenario was randomly paired with one interface. We collected participants’ impressions of the interfaces via questionnaires and written and verbal feedback. RESULTS Twenty-two physicians completed the study. Participants felt that the CDW interface was most clinically useful (H=10.29, p<0.01) and more frequently reported that it had an impact on their decision making (PA: in 55.5% of experienced scenarios, CDW: in 59.1%, EDW: in 36.6%). Clinicians most often chose a treatment different from their original choice after reading the clinical scenario in the CDW interface (PA: 26.3%, CDW: 33.3%, EDW: 15.8%). CONCLUSION Clinicians found a decision support interface where they could set the weights for different potential outcomes most useful for multi-criteria decision making. Allowing clinicians to weigh outcomes based on their expertise and the clinical scenario may be a key feature of a future clinically useful multi-criteria AI-CDSS. ### Competing Interest Statement AK, and AR have received honoraria from Aifred Health (https://www.aifredhealth.com/). DB, GG and MTS are shareholders, option holders, employees and/or officers of Aifred Health. HCM has received research support from Aifred Health, SyneuRx and the Montreal General Hospital Foundation and is a speaker and/or consultant for AbbVie, HLS Therapeutics, Janssen, Lundbeck, Otsuka, and Teva.  ### Funding Statement This study was supported, in part, by the Data Science Institute at Bar-Ilan University (http://dsi.biu.ac.il/) . AK and AR were supported by the Chief Scientist Office, Israeli Ministry of Health (CSO-MOH, IL url: https://www.health.gov.il/English/Pages/HomePage.aspx) as part of grant #3-000015730 within Era-PerMed. DB was also funded by the Canadian arm of this grant (ERA-Permed Vision 2020 supporting IMADAPT) as well as by the IRAP Program provided by the National Research Council of Canada. The granting agencies had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ### 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: Ethics committee/IRB of the Approval of Research Involving the Participation of Human Subjects at Bar-Ilan University gave ethical approval for this work 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 and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors