ArticleJournal of the American Medical Informatics Association : JAMIA2019
Cranky comments: detecting clinical decision support malfunctions through free-text override reasons.
Article in Journal of the American Medical Informatics Association : JAMIA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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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
26 citing papers in PubMed.
- User Actions within a Clinical Decision Support Alert for the Management of Hypertension in Chronic Kidney Disease.Applied clinical informatics · 2025Trial
- Provider comments reveal barriers to EHR nudge effectiveness: process evaluation of a null deprescribing trial.Implementation science communications · 2026Article
- Predicting Electronic Health Record Usability: Scoping Review of Adoption Models, Metrics, and Future Directions.JMIR human factors · 2026Article
- Optimizing Loop Diuretic Treatment for Mortality Reduction in Patients With Acute Dyspnea Using a Practical Offline Reinforcement Learning Pipeline for Health Care: Retrospective Single-Center Simulation Study.JMIR medical informatics · 2025Article
- When Nudges Don't Budge: A Mixed Methods Study of Why EHR-Based Deprescribing Nudges Failed to Change Provider Behavior.Research square · 2025Article
- Actionability of Clinician Feedback in a Multi-Specialty Group Practice for Clinical Decision Support Improvement: An Observational Study.Journal of general internal medicine · 2025Article
- The Burden of a Highly Targeted Alert.Applied clinical informatics · 2025Article
- Clinical trials informed framework for real world clinical implementation and deployment of artificial intelligence applications.NPJ digital medicine · 2025Review
- A scoping review of rule-based clinical decision support malfunctions.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Why do users override alerts? Utilizing large language model to summarize comments and optimize clinical decision support.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Leveraging explainable artificial intelligence to optimize clinical decision support.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Reducing Therapeutic Duplication in Inpatient Medication Orders.Applied clinical informatics · 2023Article
- Combining text mining with clinical decision support in clinical practice: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Using automated methods to detect safety problems with health information technology: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Clinical decision support malfunctions related to medication routes: a case series.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Real-Time User Feedback to Support Clinical Decision Support System Improvement.Applied clinical informatics · 2022Article
- Clinician collaboration to improve clinical decision support: the Clickbusters initiative.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Clinical Decision Support Stewardship: Best Practices and Techniques to Monitor and Improve Interruptive Alerts.Applied clinical informatics · 2022Review
- Giving Your Electronic Health Record a Checkup After COVID-19: A Practical Framework for Reviewing Clinical Decision Support in Light of the Telemedicine Expansion.JMIR medical informatics · 2021Article
- Development of a Taxonomy for Medication-Related Patient Safety Events Related to Health Information Technology in Pediatrics.Applied clinical informatics · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Background: Rule-base clinical decision support alerts are known to malfunction, but tools for discovering malfunctions are limited. Objective: Investigate whether user override comments can be used to discover malfunctions. Methods: We manually classified all rules in our database with at least 10 override comments into 3 categories based on a sample of override comments: "broken," "not broken, but could be improved," and "not broken." We used 3 methods (frequency of comments, cranky word list heuristic, and a Naïve Bayes classifier trained on a sample of comments) to automatically rank rules based on features of their override comments. We evaluated each ranking using the manual classification as truth. Results: Of the rules investigated, 62 were broken, 13 could be improved, and the remaining 45 were not broken. Frequency of comments performed worse than a random ranking, with precision at 20 of 8 and AUC = 0.487. The cranky comments heuristic performed better with precision at 20 of 16 and AUC = 0.723. The Naïve Bayes classifier had precision at 20 of 17 and AUC = 0.738. Discussion: Override comments uncovered malfunctions in 26% of all rules active in our system. This is a lower bound on total malfunctions and much higher than expected. Even for low-resource organizations, reviewing comments identified by the cranky word list heuristic may be an effective and feasible way of finding broken alerts. Conclusion: Override comments are a rich data source for finding alerts that are broken or could be improved. If possible, we recommend monitoring all override comments on a regular basis.
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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.