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

Clinical Information and Decision Making (1359) Knowledge Translation and Implementation Science (381) Adoption and Change Management of eHealth Systems (689) Decision Support for Health Professionals (1153)

Published on in Vol 22, No 8 (2020): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18388, first published .
A Conceptual Framework to Study the Implementation of Clinical Decision Support Systems (BEAR): Literature Review and Concept Mapping

A Conceptual Framework to Study the Implementation of Clinical Decision Support Systems (BEAR): Literature Review and Concept Mapping

A Conceptual Framework to Study the Implementation of Clinical Decision Support Systems (BEAR): Literature Review and Concept Mapping

Authors of this article:

Jhon Camacho1, 2 Author Orcid Image ;   Manuela Zanoletti-Mannello3 Author Orcid Image ;   Zach Landis-Lewis4 Author Orcid Image ;   Sandra L Kane-Gill5 Author Orcid Image ;   Richard D Boyce1 Author Orcid Image
Article Authors Cited by (23) Tweetations (5) Metrics

Journals

  1. Laka M, Milazzo A, Merlin T. Factors That Impact the Adoption of Clinical Decision Support Systems (CDSS) for Antibiotic Management. International Journal of Environmental Research and Public Health 2021;18(4):1901 View
  2. Kane-Gill S, Barreto E, Bihorac A, Kellum J. Development of a Theory-Informed Behavior Change Intervention to Reduce Inappropriate Prescribing of Nephrotoxins and Renally Eliminated Drugs. Annals of Pharmacotherapy 2021;55(12):1474 View
  3. Bertl M, Ross P, Draheim D. A survey on AI and decision support systems in psychiatry – Uncovering a dilemma. Expert Systems with Applications 2022;202:117464 View
  4. Gottlieb E, Mendu M. Clinical Decision Support to Prevent Acute Kidney Injury After Cardiac Catheterization. JAMA 2022;328(9):831 View
  5. Kukhareva P, Weir C, Del Fiol G, Aarons G, Taft T, Schlechter C, Reese T, Curran R, Nanjo C, Borbolla D, Staes C, Morgan K, Kramer H, Stipelman C, Shakib J, Flynn M, Kawamoto K. Evaluation in Life Cycle of Information Technology (ELICIT) framework: Supporting the innovation life cycle from business case assessment to summative evaluation. Journal of Biomedical Informatics 2022;127:104014 View
  6. Hauschildt J, Lyon-Scott K, Sheppler C, Larson A, McMullen C, Boston D, O’Connor P, Sperl-Hillen J, Gold R. Adoption of shared decision-making and clinical decision support for reducing cardiovascular disease risk in community health centers. JAMIA Open 2023;6(1) View
  7. Yang J, Shu K, Peng Y, Hsu S, Chiu Y, Pai M, Wu H, Tsai W, Tung K, Kuo R. Physician Compliance With a Computerized Clinical Decision Support System for Anemia Management of Patients With End-stage Kidney Disease on Hemodialysis: Retrospective Electronic Health Record Observational Study. JMIR Formative Research 2023;7:e44373 View
  8. Goldstein J, Weitzman D, Lemerond M, Jones A. Determinants for scalable adoption of autonomous AI in the detection of diabetic eye disease in diverse practice types: key best practices learned through collection of real-world data. Frontiers in Digital Health 2023;5 View
  9. Hysong S, Yang C, Wong J, Knox M, O'Mahen P, Petersen L. Beyond Information Design: Designing Health Care Dashboards for Evidence-Driven Decision-Making. Applied Clinical Informatics 2023;14(03):465 View
  10. Kashani K, Awdishu L, Bagshaw S, Barreto E, Claure-Del Granado R, Evans B, Forni L, Ghosh E, Goldstein S, Kane-Gill S, Koola J, Koyner J, Liu M, Murugan R, Nadkarni G, Neyra J, Ninan J, Ostermann M, Pannu N, Rashidi P, Ronco C, Rosner M, Selby N, Shickel B, Singh K, Soranno D, Sutherland S, Bihorac A, Mehta R. Digital health and acute kidney injury: consensus report of the 27th Acute Disease Quality Initiative workgroup. Nature Reviews Nephrology 2023;19(12):807 View
  11. Yamada J, Kouri A, Simard S, Lam Shin Cheung J, Segovia S, Gupta S. Improving computerized decision support system interventions: a qualitative study combining the theoretical domains framework with the GUIDES Checklist. BMC Medical Informatics and Decision Making 2023;23(1) View
  12. Kleine A, Kokje E, Lermer E, Gaube S. Attitudes Toward the Adoption of 2 Artificial Intelligence–Enabled Mental Health Tools Among Prospective Psychotherapists: Cross-sectional Study. JMIR Human Factors 2023;10:e46859 View
  13. Wong A, Berenbrok L, Snader L, Soh Y, Kumar V, Javed M, Bates D, Sorce L, Kane-Gill S. Facilitators and Barriers to Interacting With Clinical Decision Support in the ICU: A Mixed-Methods Approach. Critical Care Explorations 2023;5(9):e0967 View
  14. Bellón J, Rodríguez-Morejón A, Conejo-Cerón S, Campos-Paíno H, Rodríguez-Bayón A, Ballesta-Rodríguez M, Rodríguez-Sánchez E, Mendive J, López del Hoyo Y, Luna J, Tamayo-Morales O, Moreno-Peral P. A personalized intervention to prevent depression in primary care based on risk predictive algorithms and decision support systems: protocol of the e-predictD study. Frontiers in Psychiatry 2023;14 View
  15. Fernando M, Abell B, Tyack Z, Donovan T, McPhail S, Naicker S. Using Theories, Models, and Frameworks to Inform Implementation Cycles of Computerized Clinical Decision Support Systems in Tertiary Health Care Settings: Scoping Review. Journal of Medical Internet Research 2023;25:e45163 View
  16. Godefroid M, Borghoff V, Plattfaut R, Niehaves B. Teleworking antecedents: an exploration into availability bias as an impediment. Information Systems and e-Business Management 2024 View
  17. Sarıköse S, Şenol Çelik S. The Effect of Clinical Decision Support Systems on Patients, Nurses, and Work Environment in ICUs. CIN: Computers, Informatics, Nursing 2024 View
  18. Hu Z, Wang M, Zheng S, Xu X, Zhang Z, Ge Q, Li J, Yao Y. Clinical Decision Support Requirements for Ventricular Tachycardia Diagnosis Within the Frameworks of Knowledge and Practice: Survey Study. JMIR Human Factors 2024;11:e55802 View
  19. Nabelsi V, Lévesque-Chouinard A. Successful Electronic Consultation Service Initiative in Quebec, Canada With Primary Care Physicians’ and Specialists’ Experiences on Acceptance and Use of Technological Innovation: Cross-Sectional Exploratory Study. JMIR Formative Research 2024;8:e52921 View
  20. Zhang H, Li S, Ma X. Transforming Healthcare with Nanomedicine: A SWOT Analysis of Drug Delivery Innovation. Drug Design, Development and Therapy 2024;Volume 18:3499 View
  21. El Tantawi M, Ammar N, Mariño R, Uribe S, Manton D, Hugo F, Clément C, Sim C, Maret D, Kopycka-Kedzierawski D, Mbende E, Kruger E, Lan R, Doghri L, Castelaz M, Alam M, Ibiyemi O, Naidoo S, Schwarz E, Priya H, Braga M, Giraudeau N, Foláyan M. Developing the teledentistry acceptance survey for dentists – TAS-D: a Delphi study. BMC Oral Health 2024;24(1) View

Books/Policy Documents

  1. Boyce R, Camacho J, Liang W, Wiisanen K, Devine B. Clinical Decision Support for Pharmacogenomic Precision Medicine. View
  2. Jiang H. Artificial Intelligence in Anesthesiology. View