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

Decision Support for Health Professionals (1185) Clinical Information and Decision Making (1407) Safety and Error Prevention in Health (202) Computerized Provider Order Entry (CPOE) (23) Electronic Health Records (1053) Pharmacovigilance (134)

Published on in Vol 8, No 7 (2020): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18758, first published .
Barriers and Facilitators to Implementation of Medication Decision Support Systems in Electronic Medical Records: Mixed Methods Approach Based on Structural Equation Modeling and Qualitative Analysis

Barriers and Facilitators to Implementation of Medication Decision Support Systems in Electronic Medical Records: Mixed Methods Approach Based on Structural Equation Modeling and Qualitative Analysis

Barriers and Facilitators to Implementation of Medication Decision Support Systems in Electronic Medical Records: Mixed Methods Approach Based on Structural Equation Modeling and Qualitative Analysis

Authors of this article:

Se Young Jung1, 2 Author Orcid Image ;   Hee Hwang1, 3 Author Orcid Image ;   Keehyuck Lee1, 2 Author Orcid Image ;   Ho-Young Lee1, 4 Author Orcid Image ;   Eunhye Kim1 Author Orcid Image ;   Miyoung Kim1 Author Orcid Image ;   In Young Cho2 Author Orcid Image
Article Authors Cited by (18) Tweetations (1) 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. Hammar T, Hamqvist S, Zetterholm M, Jokela P, Ferati M. Current Knowledge about Providing Drug–Drug Interaction Services for Patients—A Scoping Review. Pharmacy 2021;9(2):69 View
  3. Abdulaal A, Patel A, Al-Hindawi A, Charani E, Alqahtani S, Davies G, Mughal N, Moore L. Clinical Utility and Functionality of an Artificial Intelligence–Based App to Predict Mortality in COVID-19: Mixed Methods Analysis. JMIR Formative Research 2021;5(7):e27992 View
  4. Chen R, Hsiao J. Health Professionals’ Perspectives on Electronic Medical Record Infusion and Individual Performance: Model Development and Questionnaire Survey Study. JMIR Medical Informatics 2021;9(11):e32180 View
  5. Fujimori R, Liu K, Soeno S, Naraba H, Ogura K, Hara K, Sonoo T, Ogura T, Nakamura K, Goto T. Acceptance, Barriers, and Facilitators to Implementing Artificial Intelligence–Based Decision Support Systems in Emergency Departments: Quantitative and Qualitative Evaluation. JMIR Formative Research 2022;6(6):e36501 View
  6. Brühwiler L, Gresch S, Schwappach D. Implementation status of safety measures to prevent errors with non-oncologic methotrexate: surveys in community and hospital pharmacies. International Journal of Clinical Pharmacy 2023;45(3):739 View
  7. Zhang T, Gephart S, Subbian V, Boyce R, Villa-Zapata L, Tan M, Horn J, Gomez-Lumbreras A, Romero A, Malone D. Barriers to Adoption of Tailored Drug–Drug Interaction Clinical Decision Support. Applied Clinical Informatics 2023;14(04):779 View
  8. Alshehri N, Alanazi A. Pharmacists’ Perceptions on Safety Alerts of the Drug Utilization Review (DUR) in Electronic Health Records in a Tertiary Healthcare Hospital. Pharmacy 2023;11(4):119 View
  9. Fernández-Barceló C, Calvo-Cidoncha E, Sampietro-Colom L. VALIDATE approach on medication adequacy clinical decision support systems: Holistic assessment for optimal technology adoption. Health Policy and Technology 2023;12(4):100811 View
  10. Besculides M, Mazumdar M, Phlegar S, Freeman R, Wilson S, Joshi H, Kia A, Gorbenko K. Implementing a Machine Learning Screening Tool for Malnutrition: Insights From Qualitative Research Applicable to Other Machine Learning–Based Clinical Decision Support Systems. JMIR Formative Research 2023;7:e42262 View
  11. Ackerhans S, Huynh T, Kaiser C, Schultz C. Exploring the role of professional identity in the implementation of clinical decision support systems—a narrative review. Implementation Science 2024;19(1) View
  12. Baudet A, Brennstuhl M, Lizon J, Regad M, Thilly N, Demoré B, Florentin A. Perceptions of infection control professionals toward electronic surveillance software supporting inpatient infections: A mixed methods study. International Journal of Medical Informatics 2024;186:105419 View
  13. Mugisha M, Umubyeyi A, Ngenzi J, Richards J, Santas X, Condo J, Fraser H. Exploring user experience and use of the OpenMRS HIV electronic medical record in primary healthcare facilities of Rwanda: q qualitative study. (Preprint). JMIR Human Factors 2023 View
  14. Tegenaw G, Sori D, Teklemariam G, Verbeke F, Cornelis J, Jansen B. Evaluation of a Computer-Aided Clinical Decision Support System for Point-of-Care Use in Low-Resource Primary Care Settings: Acceptability Evaluation Study. JMIR Human Factors 2024;11:e47631 View
  15. Baudet A, Brennstuhl M, Charmillon A, Meyer F, Pulcini C, Thilly N, Demoré B, Florentin A. Hospital antimicrobial stewardship team perceptions and usability of a computerized clinical decision support system. International Journal of Medical Informatics 2024;192:105653 View
  16. Aljarboa S, Alaya B, Al-Ajlan A, Miah S. CDSS Adoption and the Role of Artificial Intelligence in Saudi Arabian Primary Healthcare. Informatics in Medicine Unlocked 2024:101596 View

Books/Policy Documents

  1. Noteboom C, Zeng D, Sutrave K, Behrens A, Godasu R, Chauhan A. Encyclopedia of Data Science and Machine Learning. View
  2. Jiang H. Artificial Intelligence in Anesthesiology. View