ArticleJMIR medical informatics2020
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.
Article in JMIR medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Exploring the role of professional identity in the implementation of clinical decision support systems-a narrative review.Implementation science : IS · 2024Pooled it
- A Systematic Review of Factors Affecting Utilization of Decision Support Systems: The Interplay Between Technology, Users, and the Healthcare Environment.Health science reports · 2026Review
- Acceptance of artificial intelligence clinical assistant decision support system to prevent and control venous thromboembolism among healthcare workers: an extend Unified Theory of Acceptance and Use of Technology Model.Frontiers in medicine · 2025Article
- 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 · 2024Article
- Barriers to Adoption of Tailored Drug-Drug Interaction Clinical Decision Support.Applied clinical informatics · 2023Article
- Pharmacists' Perceptions on Safety Alerts of the Drug Utilization Review (DUR) in Electronic Health Records in a Tertiary Healthcare Hospital.Pharmacy (Basel, Switzerland) · 2023Article
- 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 · 2023Article
- Implementation status of safety measures to prevent errors with non-oncologic methotrexate: surveys in community and hospital pharmacies.International journal of clinical pharmacy · 2023Article
- Potential Drug-Related Problems in Pediatric Patients-Describing the Use of a Clinical Decision Support System at Pharmacies in Sweden.Pharmacy (Basel, Switzerland) · 2023Article
- Acceptance, Barriers, and Facilitators to Implementing Artificial Intelligence-Based Decision Support Systems in Emergency Departments: Quantitative and Qualitative Evaluation.JMIR formative research · 2022Article
- Health Professionals' Perspectives on Electronic Medical Record Infusion and Individual Performance: Model Development and Questionnaire Survey Study.JMIR medical informatics · 2021Article
- Clinical Utility and Functionality of an Artificial Intelligence-Based App to Predict Mortality in COVID-19: Mixed Methods Analysis.JMIR formative research · 2021Article
- Current Knowledge about Providing Drug-Drug Interaction Services for Patients-A Scoping Review.Pharmacy (Basel, Switzerland) · 2021Article
- Factors That Impact the Adoption of Clinical Decision Support Systems (CDSS) for Antibiotic Management.International journal of environmental research and public health · 2021Article
- Review
Corrections and comments
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Authors and funding
7 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAdverse drug events (ADEs) resulting from medication error are some of the most common causes of iatrogenic injuries in hospitals. With the appropriate use of medication, ADEs can be prevented and ameliorated. Efforts to reduce medication errors and prevent ADEs have been made by implementing a medication decision support system (MDSS) in electronic health records (EHRs). However, physicians tend to override most MDSS alerts.
objectiveIn order to improve MDSS functionality, we must understand what factors users consider essential for the successful implementation of an MDSS into their clinical setting. This study followed the implementation process for an MDSS within a comprehensive EHR system and analyzed the relevant barriers and facilitators.
methodsA mixed research methodology was adopted. Data from a structured survey and 15 in-depth interviews were integrated. Structural equation modeling was conducted for quantitative analysis of factors related to user adoption of MDSS. Qualitative analysis based on semistructured interviews with physicians was conducted to collect various opinions on MDSS implementation.
resultsQuantitative analysis revealed that physicians' expectations regarding ease of use and performance improvement are crucial. Qualitative analysis identified four significant barriers to MDSS implementation: alert fatigue, lack of accuracy, poor user interface design, and lack of customizability.
conclusionsThis study revealed barriers and facilitators to the implementation of MDSS. The findings can be applied to upgrade MDSS in the future.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.