ArticleJournal of the American Medical Informatics Association : JAMIA2020
A machine learning-based clinical decision support system to identify prescriptions with a high risk of medication error.
Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 3 of them syntheses that pooled it.
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The trial behind it
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
51 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Using machine learning or deep learning models in a hospital setting to detect inappropriate prescriptions: a systematic review.European journal of hospital pharmacy : science and practice · 2024Pooled it
- Artificial intelligence in healthcare: transforming patient safety with intelligent systems-A systematic review.Frontiers in medicine · 2024Pooled it
- Pooled it
- Assessment of a hybrid decision support system using machine learning with artificial intelligence to safely rule out prescriptions from medication review in daily practice.International journal of clinical pharmacy · 2022Trial
- A Hybrid Rule-Based and Machine Learning-Based Clinical Decision Support System to Support Prescription Review: Development and External Validation Study.Journal of medical Internet research · 2026Article
- Integration of artificial intelligence applications in clinical pharmacy services: A scoping review.Future healthcare journal · 2026Review
- Large Language Model-Based Clinical Decision Support for Antibiotic Selection and Dose Recommendation in Hospitalized Patients With Pneumonia: Multicenter Retrospective Study.JMIR medical informatics · 2026Article
- Leakage-Controlled and Survey-Weighted Machine Learning for Neonatal Mortality Risk Prediction Using NFHS-5 Data.Healthcare (Basel, Switzerland) · 2026Article
- A clinical decision support system for predicting drug-related adverse effects in pediatric patients with congenital heart disease: Development and retrospective evaluation.The Journal of international medical research · 2026Article
- The effectiveness of intravenous (IV) to oral (PO) antibiotic switch (IVOS) interventions in managing community- and hospital-acquired pneumonia-a systematic review.JAC-antimicrobial resistance · 2026Review
- Personalised Approach to the Management of Older People with Type 2 Diabetes Mellitus-A Comprehensive Narrative Review.Journal of personalized medicine · 2026Review
- When Silence Signals Safety: Governance and Responsibility in AI-Enabled Prescription Verification.Cureus · 2026Article
- Analytical approaches for medication management for patient safety: a scoping review.npj health systems · 2025Article
- Large language model as clinical decision support system augments medication safety in 16 clinical specialties.Cell reports. Medicine · 2025Article
- Review
- The impact of the bedside check of medication appropriateness (BED-CMA) at the hematology ward: a mixed-methods study.International journal of clinical pharmacy · 2025Article
- Evaluation of a score for identifying hospital stays that trigger a pharmacist intervention: integration into a clinical decision support system.JAMIA open · 2025Article
- Evaluation of Diagnostic Recommendations Embedded in Medication Alerts: Prospective Single-Arm Interventional Study.Journal of medical Internet research · 2025Article
- Machine learning for modeling and identifying risk factors of pancreatic fistula.World journal of gastrointestinal oncology · 2025Article
- Artificial Intelligence-Assisted Clinical Decision-Making: A Perspective on Advancing Personalized Precision Medicine for Elderly Diabetes Patients.Journal of multidisciplinary healthcare · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo improve patient safety and clinical outcomes by reducing the risk of prescribing errors, we tested the accuracy of a hybrid clinical decision support system in prioritizing prescription checks. MATERIALS AND
methodsData from electronic health records were collated over a period of 18 months. Inferred scores at a patient level (probability of a patient's set of active orders to require a pharmacist review) were calculated using a hybrid approach (machine learning and a rule-based expert system). A clinical pharmacist analyzed randomly selected prescription orders over a 2-week period to corroborate our findings. Predicted scores were compared with the pharmacist's review using the area under the receiving-operating characteristic curve and area under the precision-recall curve. These metrics were compared with existing tools: computerized alerts generated by a clinical decision support (CDS) system and a literature-based multicriteria query prioritization technique. Data from 10 716 individual patients (133 179 prescription orders) were used to train the algorithm on the basis of 25 features in a development dataset.
resultsWhile the pharmacist analyzed 412 individual patients (3364 prescription orders) in an independent validation dataset, the areas under the receiving-operating characteristic and precision-recall curves of our digital system were 0.81 and 0.75, respectively, thus demonstrating greater accuracy than the CDS system (0.65 and 0.56, respectively) and multicriteria query techniques (0.68 and 0.56, respectively). DISCUSSION: Our innovative digital tool was notably more accurate than existing techniques (CDS system and multicriteria query) at intercepting potential prescription errors.
conclusionsBy primarily targeting high-risk patients, this novel hybrid decision support system improved the accuracy and reliability of prescription checks in a hospital setting.
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