ArticleInteractive journal of medical research2016
An Observational Study to Evaluate the Usability and Intent to Adopt an Artificial Intelligence-Powered Medication Reconciliation Tool.
Article in Interactive journal of medical research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 4 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Optimizing Clinical Decision Support System Functionality by Leveraging Specific Human-Computer Interaction Elements: Insights From a Systematic Review.JMIR human factors · 2025Pooled it
- The impact of eHealth use on general practice workload in the pre-COVID-19 era: a systematic review.BMC health services research · 2024Pooled it
- Availability of Evidence for Predictive Machine Learning Algorithms in Primary Care: A Systematic Review.JAMA network open · 2024Pooled it
- Inclusion of Clinicians in the Development and Evaluation of Clinical Artificial Intelligence Tools: A Systematic Literature Review.Frontiers in psychology · 2022Pooled it
- AI-Based Automation for Medication Reconciliation: Scoping Review.Journal of medical Internet research · 2026Article
- Effect of Pharmacy Student Peer Supervision on the Accuracy of Admission Medication Reconciliation: Prospective Pre-Post Observational Study.JMIR human factors · 2026Observational
- Analytical approaches for medication management for patient safety: a scoping review.npj health systems · 2025Article
- Digital interventions in medication adherence: a narrative review of current evidence and challenges.Frontiers in pharmacology · 2025Review
- Medication reconciliationEuropean journal of hospital pharmacy : science and practice · 2023Article
- Effect of AI Explanations on Human Perceptions of Patient-Facing AI-Powered Healthcare Systems.Journal of medical systems · 2021Article
- Artificial Intelligence Solutions to Increase Medication Adherence in Patients With Non-communicable Diseases.Frontiers in digital health · 2021Review
- Medication Reconciliation: The Foundation of Medication Safety for Patients Requiring Dialysis.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2020Review
- Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review.JMIR medical informatics · 2020Review
- A web application to involve patients in the medication reconciliation process: a user-centered usability and usefulness study.Journal of the American Medical Informatics Association : JAMIA · 2018Observational
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMedication reconciliation (the process of creating an accurate list of all medications a patient is taking) is a widely practiced procedure to reduce medication errors. It is mandated by the Joint Commission and reimbursed by Medicare. Yet, in practice, medication reconciliation is often not effective owing to knowledge gaps in the team. A promising approach to improve medication reconciliation is to incorporate artificial intelligence (AI) decision support tools into the process to engage patients and bridge the knowledge gap.
objectiveThe aim of this study was to improve the accuracy and efficiency of medication reconciliation by engaging the patient, the nurse, and the physician as a team via an iPad tool. With assistance from the AI agent, the patient will review his or her own medication list from the electronic medical record (EMR) and annotate changes, before reviewing together with the physician and making decisions on the shared iPad screen.
methodsIn this study, we developed iPad-based software tools, with AI decision support, to engage patients to "self-service" medication reconciliation and then share the annotated reconciled list with the physician. To evaluate the software tool's user interface and workflow, a small number of patients (10) in a primary care clinic were recruited, and they were observed through the whole process during a pilot study. The patients are surveyed for the tool's usability afterward.
resultsAll patients were able to complete the medication reconciliation process correctly. Every patient found at least one error or other issues with their EMR medication lists. All of them reported that the tool was easy to use, and 8 of 10 patients reported that they will use the tool in the future. However, few patients interacted with the learning modules in the tool. The physician and nurses reported the tool to be easy-to-use, easy to integrate into existing workflow, and potentially time-saving.
conclusionsWe have developed a promising tool for a new approach to medication reconciliation. It has the potential to create more accurate medication lists faster, while better informing the patients about their medications and reducing burden on clinicians.
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