This paper is in the following e-collection/theme issue:

Focus Groups and Qualitative Research for Human Factors Research (880) Decision Support for Health Professionals (1210) Clinical Information and Decision Making (1434) Nursing (101) Natural Language Processing (706) Machine Learning (1501) Safety and Error Prevention in Health (206) Design and Usability of Clinical Software and EHRs (271)

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

Authors of this article:

Jessica M Schwartz1, 2 Author Orcid Image ;   Maureen George2 Author Orcid Image ;   Sarah Collins Rossetti1, 2 Author Orcid Image ;   Patricia C Dykes3, 4 Author Orcid Image ;   Simon R Minshall5 Author Orcid Image ;   Eugene Lucas1, 6 Author Orcid Image ;   Kenrick D Cato2, 7 Author Orcid Image
Article Authors Cited by (25) Tweetations (10) Metrics

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 2023 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