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