Published on in Vol 9, No 2 (2022): Apr-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/33960, first published .
Factors Influencing Clinician Trust in Predictive Clinical Decision Support Systems for In-Hospital Deterioration: Qualitative Descriptive Study

Factors Influencing Clinician Trust in Predictive Clinical Decision Support Systems for In-Hospital Deterioration: Qualitative Descriptive Study

Factors Influencing Clinician Trust in Predictive Clinical Decision Support Systems for In-Hospital Deterioration: Qualitative Descriptive Study

Journals

  1. Kim J, Kim B, Kim M, Hyun H, Kim H, Chang H. Prediction of inappropriate pre-hospital transfer of patients with suspected cardiovascular emergency diseases using machine learning: a retrospective observational study. BMC Medical Informatics and Decision Making 2023;23(1) View
  2. van der Vegt A, Scott I, Dermawan K, Schnetler R, Kalke V, Lane P. Deployment of machine learning algorithms to predict sepsis: systematic review and application of the SALIENT clinical AI implementation framework. Journal of the American Medical Informatics Association 2023;30(7):1349 View
  3. Choi A, Choi S, Chung K, Chung H, Song T, Choi B, Kim J. Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency department. Scientific Reports 2023;13(1) View
  4. Bergquist M, Rolandsson B, Gryska E, Laesser M, Hoefling N, Heckemann R, Schneiderman J, Björkman-Burtscher I. Trust and stakeholder perspectives on the implementation of AI tools in clinical radiology. European Radiology 2023;34(1):338 View
  5. Fritz B, Pugazenthi S, Budelier T, Tellor Pennington B, King C, Avidan M, Abraham J. User-Centered Design of a Machine Learning Dashboard for Prediction of Postoperative Complications. Anesthesia & Analgesia 2024;138(4):804 View
  6. King C, Shambe A, Abraham J. Potential uses of AI for perioperative nursing handoffs: a qualitative study. JAMIA Open 2023;6(1) View
  7. Barwise A, Curtis S, Diedrich D, Pickering B. Using artificial intelligence to promote equitable care for inpatients with language barriers and complex medical needs: clinical stakeholder perspectives. Journal of the American Medical Informatics Association 2024;31(3):611 View
  8. Shevtsova D, Ahmed A, Boot I, Sanges C, Hudecek M, Jacobs J, Hort S, Vrijhoef H. Trust in and Acceptance of Artificial Intelligence Applications in Medicine: Mixed Methods Study. JMIR Human Factors 2024;11:e47031 View
  9. Giddings R, Joseph A, Callender T, Janes S, van der Schaar M, Sheringham J, Navani N. Factors influencing clinician and patient interaction with machine learning-based risk prediction models: a systematic review. The Lancet Digital Health 2024;6(2):e131 View
  10. Anthonimuthu D, Hejlesen O, Zwisler A, Udsen F. Application of Machine Learning in Multimorbidity Research: Protocol for a Scoping Review. JMIR Research Protocols 2024;13:e53761 View
  11. Berkhout M, Smit K, Versendaal J. Decision discovery using clinical decision support system decision log data for supporting the nurse decision-making process. BMC Medical Informatics and Decision Making 2024;24(1) View
  12. Bear Don't Walk O, Paullada A, Everhart A, Casanova-Perez R, Cohen T, Veinot T. Opportunities for incorporating intersectionality into biomedical informatics. Journal of Biomedical Informatics 2024;154:104653 View
  13. Secor A, Justafort J, Torrilus C, Honoré J, Kiche S, Sandifer T, Beima-Sofie K, Wagner A, Pintye J, Puttkammer N. “Following the data”: Perceptions of and willingness to use clinical decision support tools to inform HIV care among Haitian clinicians. Health Policy and Technology 2024;13(3):100880 View
  14. Griffin A, Wang K, Leung T, Facelli J. Recommendations to promote fairness and inclusion in biomedical AI research and clinical use. Journal of Biomedical Informatics 2024;157:104693 View
  15. Kamboj N, Metcalfe K, Chu C, Conway A. Designing the User Interface of a Nitroglycerin Dose Titration Decision Support System: User-Centered Design Study. Applied Clinical Informatics 2024;15(03):583 View
  16. Johnson R, Li M, Noori A, Queen O, Zitnik M. Graph Artificial Intelligence in Medicine. Annual Review of Biomedical Data Science 2024;7(1):345 View
  17. Yuan H, Yu K, Xie F, Liu M, Sun S. Automated machine learning with interpretation: A systematic review of methodologies and applications in healthcare. Medicine Advances 2024;2(3):205 View
  18. Liao X, Yao C, Jin F, Zhang J, Liu L. Barriers and facilitators to implementing imaging-based diagnostic artificial intelligence-assisted decision-making software in hospitals in China: a qualitative study using the updated Consolidated Framework for Implementation Research. BMJ Open 2024;14(9):e084398 View
  19. Xu Z, Evans L, Song J, Chae S, Davoudi A, Bowles K, McDonald M, Topaz M. Exploring home healthcare clinicians’ needs for using clinical decision support systems for early risk warning. Journal of the American Medical Informatics Association 2024;31(11):2641 View
  20. Bedford J, Fields K, Collins G, Lip G, Clifton D, O’Brien B, Muehlschlegel J, Watkinson P, Redfern O. Atrial fibrillation after cardiac surgery: identifying candidate predictors through a Delphi process. BMJ Open 2024;14(9):e086589 View
  21. Högberg C, Larsson S, Lång K. Engaging with artificial intelligence in mammography screening: Swedish breast radiologists’ views on trust, information and expertise. DIGITAL HEALTH 2024;10 View
  22. Owoyemi A, Okpara E, Salwei M, Boyd A. End user experience of a widely used artificial intelligence based sepsis system. JAMIA Open 2024;7(4) View
  23. Preti L, Ardito V, Compagni A, Petracca F, Cappellaro G. Implementation of Machine Learning Applications in Health Care Organizations: Systematic Review of Empirical Studies. Journal of Medical Internet Research 2024;26:e55897 View
  24. Brankovic A, Cook D, Rahman J, Khanna S, Huang W. Benchmarking the most popular XAI used for explaining clinical predictive models: Untrustworthy but could be useful. Health Informatics Journal 2024;30(4) View
  25. Tahtali M, Snijders C, Dirne C, Le Blanc P. Prioritizing Trust in Podiatrists’ Preference for AI in Supportive Roles Over Diagnostic Roles in Health Care: Qualitative Interview and Focus Group Study. JMIR Human Factors 2025;12:e59010 View
  26. Leivaditis V, Beltsios E, Papatriantafyllou A, Grapatsas K, Mulita F, Kontodimopoulos N, Baikoussis N, Tchabashvili L, Tasios K, Maroulis I, Dahm M, Koletsis E. Artificial Intelligence in Cardiac Surgery: Transforming Outcomes and Shaping the Future. Clinics and Practice 2025;15(1):17 View
  27. Landau A, Blanchard A, Kulkarni P, Althobaiti S, Idnay B, Patton D, Topaz M, Cato K. Designing a Machine Learning-Based Model Integrating Clinical Orders for Child Abuse and Neglect Identification with Focus on Reducing Socio-economic Bias. International Journal on Child Maltreatment: Research, Policy and Practice 2025;8(2):209 View
  28. Nibbelink C, Mendoza K, Harding H, Fields W. How Fast Is My Patient Deteriorating? A Qualitative Description Study of A Concern Factor Tool to Support Nurses’ Communication and Prioritization Decision Making. Advances in Nursing Science 2025;48(3):E98 View
  29. Binuya M, Linn S, Boekhout A, Schmidt M, Engelhardt E. Bridging the Gap: A Mixed-Methods Study on Factors Influencing Breast Cancer Clinicians’ Decisions to Use Clinical Prediction Models. MDM Policy & Practice 2025;10(1) View
  30. Rossetti S, Dykes P, Knaplund C, Cho S, Withall J, Lowenthal G, Albers D, Lee R, Jia H, Bakken S, Kang M, Chang F, Zhou L, Bates D, Daramola T, Liu F, Schwartz-Dillard J, Tran M, Bokhari S, Thate J, Cato K. Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial. Nature Medicine 2025;31(6):1895 View
  31. Antweiler D, Fuchs G. Trust at every step: Embedding trust quality gates into the visual data exploration loop for machine learning-based clinical decision support systems. Computers & Graphics 2025;128:104212 View
  32. Afshar M, Resnik F, Joyce C, Oguss M, Dligach D, Burnside E, Sullivan A, Churpek M, Patterson B, Salisbury-Afshar E, Liao F, Goswami C, Brown R, Mundt M. Clinical implementation of AI-based screening for risk for opioid use disorder in hospitalized adults. Nature Medicine 2025;31(6):1863 View
  33. Azadi A, García-Peñalvo F. A Synergistic Bridge Between Human–Computer Interaction and Data Management Within CDSS. Data 2025;10(5):60 View
  34. Hobensack M, Davoudi A, Song J, Cato K, Bowles K, Topaz M. Comparing the influence of social risk factors on machine learning model performance across racial and ethnic groups in home healthcare. Nursing Outlook 2025;73(3):102431 View
  35. Sun X, Nakashima M, Nguyen C, Chen P, Tang W, Kwon D, Chen D. FairICP: identifying biases and increasing transparency at the point of care in post-implementation clinical decision support using inductive conformal prediction. Journal of the American Medical Informatics Association 2025;32(8):1299 View
  36. Ardito V, Cappellaro G, Compagni A, Petracca F, Preti L. Adoption of artificial intelligence applications in clinical practice: Insights from a Survey of Healthcare Organizations in Lombardy, Italy. DIGITAL HEALTH 2025;11 View
  37. Nycklemoe S, Devarapu S, Gao Y, Carey K, Kuehnel N, Munjal N, Jani P, Churpek M, Dligach D, Afshar M, Mayampurath A. Explaining alerts from a pediatric risk prediction model using clinical text. Journal of the American Medical Informatics Association 2025;32(9):1445 View
  38. Agius S, Cassar V, Magri C, Khan W, Topham L. Position paper: advocating for a structured methodology in developing data-driven predictive models for healthcare – evidence from a large-scale national study. Health and Technology 2025 View
  39. Nguyen O, Churpek M, Wiegmann D. Factors Associated with Purpose-, Process-, and Performance-related Trust for Artificial Intelligence Technologies for Clinical Practice Among Healthcare Professionals: A Review of Empirical Research. Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 2025;14(1):33 View
  40. Christensen M, Reale C, Anders S, Coffman T, Alaw H, Mathe J, Albert D, Sachs A, McCoy A, Liu D, Storrow A, Kripalani S, Novak L. “I worry we'll blow right by it:” Barriers to Uptake of the STRATIFY-CDS for Acute Heart Failure. Applied Clinical Informatics 2025;16(04):1014 View
  41. Kunz L, Metzger M, Schaefer C, Pohlmeier R, Petrovic Vorkapic J, Nosch M. User experience study to evaluate a clinical decision support system prototype supporting continuous kidney replacement therapy in a simulated ICU environment. BMC Medical Informatics and Decision Making 2025;25(1) View
  42. Badawy W, Shaban M, Zinhom H. Artificial intelligence in nursing practice and education: A 10-year bibliometric analysis of trends, gaps and emerging frontiers. Nurse Education in Practice 2025;88:104554 View
  43. Su W, Wang X, Jia H, Chang W, Jiang S, Ge H, Dong S, Yu J, Ma G, Meng Y. Explainable prediction of hypothermia risk in laparoscopic surgery: a retrospective cross-sectional study using machine learning. BMC Surgery 2025;25(1) View
  44. Parsons C, Zuiderwijk A, Orchard N, Oosterhoff J, de Reuver M. Task-Technology Fit of Artificial Intelligence-based clinical decision support systems: a review of qualitative studies. BMC Medical Informatics and Decision Making 2025;25(1) View
  45. Yuan S, Guo L, Xu F. Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment. International Urology and Nephrology 2025 View

Books/Policy Documents

  1. Li Y, Lallemand C, Bernhaupt R. Human-Computer Interaction – INTERACT 2025. View

Conference Proceedings

  1. Ormazabal A, Berry D, Hederman L. 2024 IEEE 37th International Symposium on Computer-Based Medical Systems (CBMS). Co-development of a tool to help clinicians decide upon the trustworthiness of Patient Generated Health Data View
  2. Zhang T, Chung T, Dey A, Bae S. 2024 International Conference on Activity and Behavior Computing (ABC). Exploring Algorithmic Explainability: Generating Explainable AI Insights for Personalized Clinical Decision Support Focused on Cannabis Intoxication in Young Adults View
  3. Berkhout M, van Barneveld A, Smit K, van Houwelingen T. 38th Bled eConference: Empowering Transformation: Shaping Digital Futures for All: Conference Proceedings. “Rage Against the Machine?”: The Impact of Clinical Decision Support Systems on Hospital Nursing Decision-Making, Workflow Efficiency, and Patient Outcomes: A Rapid Review View
  4. Samimi R, Bhattacharya A, Gosak L, Stiglic G, Verbert K. Proceedings of the 7th ACM Conference on Conversational User Interfaces. Visual-Conversational Interface for Evidence-Based Explanation of Diabetes Risk Prediction View