ArticleJoint Commission journal on quality and patient safety2020
Using a Machine Learning System to Identify and Prevent Medication Prescribing Errors: A Clinical and Cost Analysis Evaluation.
Article in Joint Commission journal on quality and patient safety, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis 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
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 74 citations in OpenAlex.
- Calculating the cost of medication errors: A systematic review of approaches and cost variables.BMJ open quality · 2024Pooled 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
- Digital Health Revolution in Saudi Arabia: A Narrative Review of Healthcare Transformation.Healthcare (Basel, Switzerland) · 2026Review
- Machine learning for medication error detection: a scoping review.Research square · 2026Article
- The Financial and Workforce Impact of Medication Errors in the Finnish Public Healthcare System: A Pilot Study.Health services insights · 2026Article
- Partnerships to advance patient safety and address preventable harm: case studies from international health care leaders.Health affairs scholar · 2026Article
- Analytical approaches for medication management for patient safety: a scoping review.npj health systems · 2025Article
- Medical machine learning operations: a framework to facilitate clinical AI development and deployment in radiology.European radiology · 2025Review
- Large language model as clinical decision support system augments medication safety in 16 clinical specialties.Cell reports. Medicine · 2025Article
- Increasing the Impact and Value of Laboratory Medicine Through Effective and AI-Assisted Communication.EJIFCC · 2025Review
- A Machine Learning Application to Classify Patients at Differing Levels of Risk of Opioid Use Disorder: Clinician-Based Validation Study.JMIR medical informatics · 2024Article
- Using artificial intelligence to promote equitable care for inpatients with language barriers and complex medical needs: clinical stakeholder perspectives.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Closed-Loop Medication Management with an Electronic Health Record System in U.S. and Finnish Hospitals.International journal of environmental research and public health · 2023Review
- Prediction of Prednisolone Dose Correction Using Machine Learning.Journal of healthcare informatics research · 2023Article
- Appropriateness of Alerts and Physicians' Responses With a Medication-Related Clinical Decision Support System: Retrospective Observational Study.JMIR medical informatics · 2022Article
- Intelligent Telehealth in Pharmacovigilance: A Future Perspective.Drug safety · 2022Review
- Advancing Research on Medical Image Perception by Strengthening Multidisciplinary Collaboration.JNCI cancer spectrum · 2022Article
- Data Dentistry: How Data Are Changing Clinical Care and Research.Journal of dental research · 2022Article
- Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature.Applied clinical informatics · 2022Review
- Hybrid Method Incorporating a Rule-Based Approach and Deep Learning for Prescription Error Prediction.Drug safety · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundClinical decision support (CDS) alerting tools can identify and reduce medication errors. However, they are typically rule-based and can identify only the errors previously programmed into their alerting logic. Machine learning holds promise for improving medication error detection and reducing costs associated with adverse events. This study evaluates the ability of a machine learning system (MedAware) to generate clinically valid alerts and estimates the cost savings associated with potentially prevented adverse events.
methodsAlerts were generated retrospectively by the MedAware system on outpatient data from two academic medical centers between 2009 and 2013. MedAware alerts were compared to alerts in an existing CDS system. A random sample of 300 alerts was selected for medical record review. Frequency and severity of potential outcomes of alerted medication errors of medium and high clinical value were estimated, along with associated health care costs of these potentially prevented adverse events.
resultsA total of 10,668 alerts were generated. Overall, 68.2% of MedAware alerts would not have been generated by the existing CDS system. Ninety-two percent of a random sample of the chart-reviewed alerts were accurate based on structured data available in the record, and 79.7% were clinically valid. Estimated cost of adverse events potentially prevented in an outpatient setting was more than $60 per drug alert and $1.3 million when extrapolating study findings to the full patient population.
conclusionA machine learning system identified clinically valid medication error alerts that might otherwise be missed with existing CDS systems. Estimates show potential for cost savings associated with potentially prevented adverse events.
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Registered trials
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.