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

Formative Evaluation of Digital Health Interventions (3229) Clinical Informatics (1325) Clinical Information and Decision Making (1915) Human Factors and Usability Case Studies (1005) E-Health / Health Services Research and New Models of Care (718) Quality Improvement (294) Adoption and Change Management of eHealth Systems (894) New Technologies (108) Artificial Intelligence (2129) Decision Support for Health Professionals (1539)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/47335, first published .
Barriers and Enablers for Implementation of an Artificial Intelligence–Based Decision Support Tool to Reduce the Risk of Readmission of Patients With Heart Failure: Stakeholder Interviews

Barriers and Enablers for Implementation of an Artificial Intelligence–Based Decision Support Tool to Reduce the Risk of Readmission of Patients With Heart Failure: Stakeholder Interviews

Barriers and Enablers for Implementation of an Artificial Intelligence–Based Decision Support Tool to Reduce the Risk of Readmission of Patients With Heart Failure: Stakeholder Interviews

Authors of this article:

Monika Nair1 Author Orcid Image ;   Jonas Andersson2 Author Orcid Image ;   Jens M Nygren1 Author Orcid Image ;   Lina E Lundgren3 Author Orcid Image
Article Authors Cited by (10) Tweetations (2) Metrics

Journals

  1. Nair M, Lundgren L, Soliman A, Dryselius P, Fogelberg E, Petersson M, Hamed O, Triantafyllou M, Nygren J. Machine Learning Model for Readmission Prediction of Patients With Heart Failure Based on Electronic Health Records: Protocol for a Quasi-Experimental Study for Impact Assessment. JMIR Research Protocols 2024;13:e52744 View
  2. Siira E, Tyskbo D, Nygren J. Healthcare leaders’ experiences of implementing artificial intelligence for medical history-taking and triage in Swedish primary care: an interview study. BMC Primary Care 2024;25(1) View
  3. Wang Y, Liu S, Spiteri A, Huynh A, Chu C, Masters C, Goudey B, Pan Y, Jin L. Understanding machine learning applications in dementia research and clinical practice: a review for biomedical scientists and clinicians. Alzheimer's Research & Therapy 2024;16(1) View
  4. Tun H, Rahman H, Naing L, Malik O. Trust in Artificial Intelligence–Based Clinical Decision Support Systems Among Health Care Workers: Systematic Review. Journal of Medical Internet Research 2025;27:e69678 View
  5. An Q, Sandlund M, Lundell S, Kuenen C, Chastin S, Helleday R, Messiha K, Loisel Q, Wadell K. Transition design: Co-creating system solutions for chronic obstructive pulmonary disease (COPD) care. Design Studies 2025;98:101297 View
  6. Sharma S, Singh M, Pradhan K. Balancing Innovation and Safety in Digital Healthcare. Global Clinical Engineering Journal 2025;7(1):5 View
  7. Arends B, McCormick J, van der Harst P, Heus P, van Es R. Barriers, facilitators and strategies for the implementation of artificial intelligence‐based electrocardiogram interpretation: A mixed‐methods study. European Journal of Clinical Investigation 2025;55(S1) View
  8. Theiakou F, Kastanioti C, Zavras D, Rekkas D. Beyond the Pill: Mapping Process-Oriented Decision Support Models in Pharmaceutical Policy. Healthcare 2025;13(15):1861 View
  9. Mehraeen E, Siami H, Montazeryan S, Molavi R, Feyzabadi A, Parvizy I, Masjedlu Z, Dehkalani M, Mahmoudi S, Ahmadipour A. Artificial Intelligence Challenges in the Healthcare Industry: A Systematic Review of Recent Evidence. Healthcare Technology Letters 2025;12(1) View

Books/Policy Documents

  1. Sjöström J, Dryselius P, Nygren J, Nair M, Soliman A, Lundgren L. Design Science Research for a Resilient Future. View