{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T20:55:15Z","timestamp":1790974515409,"version":"4.1.0"},"reference-count":46,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Hum Factors"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n AI has the potential to enhance clinical decision-making in high-acuity settings such as intensive care units (ICUs) and emergency departments (EDs). However, despite promising performance, many AI-driven clinical decision support systems (AI-CDSSs) face poor adoption due to issues of trust, workflow disruption, and alert fatigue. Understanding the human factors that shape clinician acceptance is critical to guide safe and effective implementation of AI-CDSS in acute care. Theoretical frameworks, including the Systems Engineering Initiative for Patient Safety (SEIPS) 2.0 model and the technology acceptance model (TAM), suggest that successful adoption requires addressing sociotechnical interactions among clinician trust, system design, organizational readiness, and task complexity, yet few empirical studies have applied these frameworks to AI-CDSSs in acute care settings.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aimed to evaluate emergency medicine and critical care clinicians\u2019 perceptions of AI-CDSSs and to identify key factors influencing adoption, including trust, design preferences, and workflow integration.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n A SEIPS 2.0\u2013informed mixed methods study evaluated ICU and ED clinicians from Emory Healthcare on perceptions of AI in clinical practice. An expert-reviewed survey (N=57) assessed clinician perceptions, trust, and implementation preferences. Semistructured interviews (n=11) included A\/B testing of AI-CDSSs and clinical sepsis scenarios to explore decision-making in context. Transcripts were thematically analyzed using the Braun and Clarke framework in ATLAS.ti (version 26, ATLAS.ti Scientific Software Development). Quantitative data were analyzed descriptively. This study assessed clinician perceptions using mock alerts and hypothetical scenarios rather than real-world AI-CDSS deployment.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Trust in AI varied significantly by patient acuity (Cochran Q=30.40,\n P<\/jats:italic>\n <.001): stable patients (43\/57, 75%, 95% CI 63%\u201085%), deteriorating patients (27\/57, 47%, 95% CI 35%\u201060%), and critically ill patients in the ICU and undifferentiated patients in the ED (25\/57, 44%, 95% CI 32%\u201057% for each scenario). Internal consistency was acceptable-to-good across three scales (Cronbach \u03b1: AI Perception=.891, Trust=.743, Implementation=.740; McDonald\n \u03c9<\/jats:italic>\n : AI Perception=0.895, Trust=0.782, Implementation=0.746). Barriers included overreliance, insufficient training, and data quality concerns. For the exploratory AI-CDSS design, clinicians preferred opt-in alerts (10\/11, 91%), evidence-linked recommendations (7\/11, 64%), and avoiding overt mention of increased AI acceptance (8\/11, 73%). Thematic analysis yielded 36 themes across six domains: trust and transparency, alert usability, workflow fit, data concerns, training needs, and perceived clinical impact. Clinicians favored AI-CDSSs that preserved autonomy, minimized disruption, and provided transparent rationales.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Adoption of AI-CDSSs in critical care is not solely a technical issue but a human-factors challenge centered on trust, transparency, and workflow compatibility. These findings support future testing of a phased implementation approach\u2014beginning with lower-acuity applications where clinician trust is highest, then gradually extending to higher-acuity scenarios with enhanced transparency and override mechanisms. This graduated strategy addresses the critical interdependencies among people (trust), tools (design), organizations (training), and tasks (clinical complexity) identified in this study.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/95472","type":"journal-article","created":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T18:15:06Z","timestamp":1788891306000},"page":"e95472-e95472","source":"Crossref","is-referenced-by-count":0,"title":["Clinician Trust and Human Factors in AI-Enabled Clinical Decision Support in Acute Care: Mixed Methods Study"],"prefix":"10.2196","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4948-037X","authenticated-orcid":false,"given":"Meghana","family":"Darla","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danielle","family":"Miltz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khushboo","family":"Chandnani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3625-534X","authenticated-orcid":false,"given":"Saptarshi","family":"Purkayastha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5605-2983","authenticated-orcid":false,"given":"John W","family":"Diehl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sivasubramanium V","family":"Bhavani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,10,2]]},"reference":[{"issue":"4","key":"R1","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.57728","article-title":"AI-driven clinical decision support systems: an ongoing pursuit of potential","volume":"16","author":"Elhaddad","journal-title":"Cureus"},{"issue":"1","key":"R2","doi-asserted-by":"publisher","DOI":"10.1136\/bmjhci-2023-100971","article-title":"Achieving large-scale clinician adoption of AI-enabled decision support","volume":"31","author":"Scott","journal-title":"BMJ Health Care Inform"},{"issue":"Suppl 3","key":"R3","doi-asserted-by":"publisher","first-page":"i68","DOI":"10.1136\/qshc.2010.042085","article-title":"A new sociotechnical model for studying health information technology in complex adaptive healthcare systems","volume":"19 Suppl 3","author":"Sittig","journal-title":"Qual Saf Health Care"},{"key":"R4","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-0221-y","article-title":"An overview of clinical decision support systems: benefits, risks, and strategies for success","volume":"3","author":"Sutton","journal-title":"NPJ Digit Med"},{"issue":"2","key":"R5","doi-asserted-by":"publisher","DOI":"10.2196\/12469","article-title":"Interruptive versus noninterruptive clinical decision support: usability study","volume":"6","author":"Blecker","journal-title":"JMIR Hum Factors"},{"issue":"9","key":"R6","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2024.34197","article-title":"The Precision Resuscitation With Crystalloids in Sepsis (PRECISE) trial: a trial protocol","volume":"7","author":"Bhavani","journal-title":"JAMA Netw Open"},{"issue":"2","key":"R7","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1197\/jamia.M2334","article-title":"A roadmap for national action on clinical decision support","volume":"14","author":"Osheroff","journal-title":"J Am Med Inform Assoc"},{"key":"R8","doi-asserted-by":"publisher","DOI":"10.2196\/69678","article-title":"Trust in artificial intelligence-based clinical decision support systems among health care workers: systematic review","volume":"27","author":"Tun","journal-title":"J Med Internet Res"},{"issue":"6","key":"R9","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1080\/10447318.2020.1741118","article-title":"Human-centered artificial intelligence: reliable, safe & trustworthy","volume":"36","author":"Shneiderman","journal-title":"Int J Hum Comput Interact"},{"issue":"11","key":"R10","doi-asserted-by":"publisher","first-page":"1669","DOI":"10.1080\/00140139.2013.838643","article-title":"SEIPS 2.0: a human factors framework for studying and improving the work of healthcare professionals and patients","volume":"56","author":"Holden","journal-title":"Ergonomics"},{"issue":"3","key":"R11","doi-asserted-by":"publisher","first-page":"319","DOI":"10.2307\/249008","article-title":"Perceived usefulness, perceived ease of use, and user acceptance of information technology","volume":"13","author":"Davis","journal-title":"MIS Q"},{"issue":"1","key":"R12","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1207\/S15327566IJCE0401_04","article-title":"Foundations for an empirically determined scale of trust in automated systems","volume":"4","author":"Jian","journal-title":"Int J Cogn Ergon"},{"issue":"3","key":"R13","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1177\/0018720814547570","article-title":"Trust in automation: integrating empirical evidence on factors that influence trust","volume":"57","author":"Hoff","journal-title":"Hum Factors"},{"issue":"2","key":"R14","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1258\/jhsrp.2007.007074","article-title":"The quality of mixed methods studies in health services research","volume":"13","author":"O\u2019Cathain","journal-title":"J Health Serv Res Policy"},{"issue":"3","key":"R15","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.6.3.e34","article-title":"Improving the quality of web surveys: the Checklist for Reporting Results of Internet E-Surveys (CHERRIES)","volume":"6","author":"Eysenbach","journal-title":"J Med Internet Res"},{"issue":"3","key":"R16","doi-asserted-by":"publisher","first-page":"425","DOI":"10.2307\/30036540","article-title":"User acceptance of information technology: toward a unified view","volume":"27","author":"Venkatesh","journal-title":"MIS Q"},{"issue":"6","key":"R17","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1093\/intqhc\/mzm042","article-title":"Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups","volume":"19","author":"Tong","journal-title":"Int J Qual Health Care"},{"issue":"3","key":"R18","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1111\/bjop.12046","article-title":"From alpha to omega: a practical solution to the pervasive problem of internal consistency estimation","volume":"105","author":"Dunn","journal-title":"Br J Psychol"},{"issue":"2","key":"R19","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1191\/1478088706qp063oa","article-title":"Using thematic analysis in psychology","volume":"3","author":"Braun","journal-title":"Qual Res Psychol"},{"issue":"13","key":"R20","doi-asserted-by":"publisher","first-page":"1753","DOI":"10.1177\/1049732315617444","article-title":"Sample size in qualitative interview studies: guided by information power","volume":"26","author":"Malterud","journal-title":"Qual Health Res"},{"issue":"4","key":"R21","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1177\/1049732316665344","article-title":"Code saturation versus meaning saturation: how many interviews are enough?","volume":"27","author":"Hennink","journal-title":"Qual Health Res"},{"issue":"5","key":"R22","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1038\/s41591-022-01772-9","article-title":"Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI","volume":"28","author":"Vasey","journal-title":"Nat Med"},{"key":"R23","doi-asserted-by":"publisher","DOI":"10.2196\/53207","article-title":"How explainable artificial intelligence can increase or decrease clinicians\u2019 trust in AI applications in health care: systematic review","volume":"3","author":"Rosenbacke","journal-title":"JMIR AI"},{"issue":"3","key":"R24","doi-asserted-by":"publisher","DOI":"10.2196\/39234","article-title":"Electronic diagnostic support in emergency physician triage: qualitative study with thematic analysis of interviews","volume":"9","author":"Sibbald","journal-title":"JMIR Hum Factors"},{"issue":"1","key":"R25","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1007\/s00330-023-09967-5","article-title":"Trust and stakeholder perspectives on the implementation of AI tools in clinical radiology","volume":"34","author":"Bergquist","journal-title":"Eur Radiol"},{"issue":"1","key":"R26","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-020-01332-6","article-title":"Explainability for artificial intelligence in healthcare: a multidisciplinary perspective","volume":"20","author":"Amann","journal-title":"BMC Med Inform Decis Mak"},{"issue":"CSCW","key":"R27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3359206","article-title":"\u201cHello AI\u201d: uncovering the onboarding needs of medical practitioners for human-AI collaborative decision-making","volume":"3","author":"Cai","journal-title":"Proc ACM Hum-Comput Interact"},{"key":"R28","doi-asserted-by":"publisher","DOI":"10.2196\/46020","article-title":"Roles and competencies of doctors in artificial intelligence implementation: qualitative analysis through physician interviews","volume":"7","author":"Tanaka","journal-title":"JMIR Form Res"},{"key":"R29","doi-asserted-by":"publisher","DOI":"10.2196\/54811","article-title":"Evaluating the prevalence of burnout among health care professionals related to electronic health record use: systematic review and meta-analysis","volume":"12","author":"Wu","journal-title":"JMIR Med Inform"},{"issue":"10","key":"R30","doi-asserted-by":"publisher","DOI":"10.2196\/19676","article-title":"Integrating the practical robust implementation and sustainability model with best practices in clinical decision support design: implementation science approach","volume":"22","author":"Trinkley","journal-title":"J Med Internet Res"},{"issue":"2","key":"R31","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1111\/1742-6723.14345","article-title":"Attitudes towards artificial intelligence in emergency medicine","volume":"36","author":"Stewart","journal-title":"Emerg Med Australas"},{"issue":"9","key":"R32","doi-asserted-by":"publisher","first-page":"847","DOI":"10.3348\/kjr.2022.0193","article-title":"Successful implementation of an artificial intelligence-based computer-aided detection system for chest radiography in daily clinical practice","volume":"23","author":"Lee","journal-title":"Korean J Radiol"},{"issue":"3","key":"R33","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0282123","article-title":"Hospital-wide survey of clinical experience with artificial intelligence applied to daily chest radiographs","volume":"18","author":"Shin","journal-title":"PLoS One"},{"issue":"1","key":"R34","doi-asserted-by":"publisher","DOI":"10.1093\/jamiaopen\/ooae151","article-title":"Towards human-AI collaboration in radiology: a multidimensional evaluation of the acceptability of AI for chest radiograph analysis in supporting pulmonary tuberculosis diagnosis","volume":"8","author":"Hua","journal-title":"JAMIA Open"},{"issue":"12","key":"R35","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2022.47172","article-title":"Evaluation of an artificial intelligence model for detection of pneumothorax and tension pneumothorax in chest radiographs","volume":"5","author":"Hillis","journal-title":"JAMA Netw Open"},{"key":"R36","doi-asserted-by":"publisher","DOI":"10.1016\/j.jgar.2024.06.004","article-title":"Innovative strategies against superbugs: developing an AI-CDSS for precise Stenotrophomonas maltophilia treatment","volume":"38","author":"Lin","journal-title":"J Glob Antimicrob Resist"},{"key":"R37","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2025.3586555","article-title":"Conceptualizing clinicians\u2019 trust in artificial intelligence as a function of their expertise, workload, patient outcome, diagnosis difficulty, and AI accuracy: a systems thinking approach","volume":"13","author":"Shamszare","journal-title":"IEEE Access"},{"issue":"8","key":"R38","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0305949","article-title":"A comprehensive overview of barriers and strategies for AI implementation in healthcare: mixed-method design","volume":"19","author":"Nair","journal-title":"PLoS One"},{"issue":"4","key":"R39","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2024-092624","article-title":"Barriers to and facilitators of clinician acceptance and use of artificial intelligence in healthcare settings: a scoping review","volume":"15","author":"Scipion","journal-title":"BMJ Open"},{"issue":"3","key":"R40","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1097\/ACM.0000000000004963","article-title":"Competencies for the use of artificial intelligence-based tools by health care professionals","volume":"98","author":"Russell","journal-title":"Acad Med"},{"issue":"10","key":"R41","doi-asserted-by":"publisher","first-page":"1730","DOI":"10.1093\/jamia\/ocad120","article-title":"Electronic health record data quality assessment and tools: a systematic review","volume":"30","author":"Lewis","journal-title":"J Am Med Inform Assoc"},{"key":"R42","doi-asserted-by":"publisher","DOI":"10.3233\/SHTI250909","article-title":"Enhancing AI clinical decision support trust: design workshop insights from general practitioners","volume":"329","author":"Ranwala","journal-title":"Stud Health Technol Inform"},{"issue":"5","key":"R43","doi-asserted-by":"publisher","first-page":"1014","DOI":"10.1093\/jamia\/ocab291","article-title":"Applying requisite imagination to safeguard electronic health record transitions","volume":"29","author":"Sittig","journal-title":"J Am Med Inform Assoc"},{"issue":"1","key":"R44","doi-asserted-by":"publisher","DOI":"10.1136\/bmjhci-2024-101120","article-title":"Balancing act: the complex role of artificial intelligence in addressing burnout and healthcare workforce dynamics","volume":"31","author":"Pavuluri","journal-title":"BMJ Health Care Inform"},{"issue":"1","key":"R45","doi-asserted-by":"publisher","first-page":"51","DOI":"10.17294\/2330-0698.2029","article-title":"Patient perspectives on the use of artificial intelligence in health care: a scoping review","volume":"11","author":"Moy","journal-title":"J Patient Cent Res Rev"},{"key":"R46","doi-asserted-by":"publisher","DOI":"10.1016\/j.apergo.2019.103033","article-title":"SEIPS 3.0: Human-centered design of the patient journey for patient safety","volume":"84","author":"Carayon","journal-title":"Appl Ergon"}],"container-title":["JMIR Human Factors"],"original-title":[],"language":"en","deposited":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T20:45:02Z","timestamp":1790973902000},"score":1,"resource":{"primary":{"URL":"https:\/\/humanfactors.jmir.org\/2026\/1\/e95472"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,2]]},"references-count":46,"URL":"https:\/\/doi.org\/10.2196\/95472","relation":{},"ISSN":["2292-9495"],"issn-type":[{"value":"2292-9495","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,2]]},"article-number":"v13i4e95472"}}