ArticleJournal of the American Medical Informatics Association : JAMIA2018
Using statistical anomaly detection models to find clinical decision support malfunctions.
Article in Journal of the American Medical Informatics Association : JAMIA, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 44 citations in OpenAlex.
- A Clustering Ensemble Method for Drug Safety Signal Detection in Post-Marketing Surveillance.Therapeutic innovation & regulatory science · 2025Article
- A scoping review of rule-based clinical decision support malfunctions.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Use of Epic Electronic Health Record System for Health Care Research: Scoping Review.Journal of medical Internet research · 2023Article
- Identifying Anomalies while Preserving Privacy.IEEE transactions on knowledge and data engineering · 2023Article
- Unsupervised anomaly detection of implausible electronic health records: a real-world evaluation in cancer registries.BMC medical research methodology · 2023Article
- Using automated methods to detect safety problems with health information technology: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Genomic map of candidate human imprint control regions: the imprintome.Epigenetics · 2022Article
- Clinical decision support malfunctions related to medication routes: a case series.Journal of the American Medical Informatics Association : JAMIA · 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
- Applying requisite imagination to safeguard electronic health record transitions.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Assessing the quality of clinical and administrative data extracted from hospitals: the General Medicine Inpatient Initiative (GEMINI) experience.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Artificial intelligence-based clinical decision support in modern medical physics: Selection, acceptance, commissioning, and quality assurance.Medical physics · 2020Review
- Current challenges in health information technology-related patient safety.Health informatics journal · 2020Article
- How to Accurately and Privately Identify Anomalies.Conference on Computer and Communications Security : proceedings of the ... conference on computer and communications security. ACM Conference on Computer and Communications Security · 2019Article
- User stories as lightweight requirements for agile clinical decision support development.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Contributions on Clinical Decision Support from the 2018 Literature.Yearbook of medical informatics · 2019Review
- A clustering approach for detecting implausible observation values in electronic health records data.BMC medical informatics and decision making · 2019Article
- Real-Time Automated Hazard Detection Framework for Health Information Technology Systems.Health systems (Basingstoke, England) · 2019Article
- Smashing the strict hierarchy: three cases of clinical decision support malfunctions involving carvedilol.Journal of the American Medical Informatics Association : JAMIA · 2018Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Objective: Malfunctions in Clinical Decision Support (CDS) systems occur due to a multitude of reasons, and often go unnoticed, leading to potentially poor outcomes. Our goal was to identify malfunctions within CDS systems. Methods: We evaluated 6 anomaly detection models: (1) Poisson Changepoint Model, (2) Autoregressive Integrated Moving Average (ARIMA) Model, (3) Hierarchical Divisive Changepoint (HDC) Model, (4) Bayesian Changepoint Model, (5) Seasonal Hybrid Extreme Studentized Deviate (SHESD) Model, and (6) E-Divisive with Median (EDM) Model and characterized their ability to find known anomalies. We analyzed 4 CDS alerts with known malfunctions from the Longitudinal Medical Record (LMR) and Epic® (Epic Systems Corporation, Madison, WI, USA) at Brigham and Women's Hospital, Boston, MA. The 4 rules recommend lead testing in children, aspirin therapy in patients with coronary artery disease, pneumococcal vaccination in immunocompromised adults and thyroid testing in patients taking amiodarone. Results: Poisson changepoint, ARIMA, HDC, Bayesian changepoint and the SHESD model were able to detect anomalies in an alert for lead screening in children and in an alert for pneumococcal conjugate vaccine in immunocompromised adults. EDM was able to detect anomalies in an alert for monitoring thyroid function in patients on amiodarone. Conclusions: Malfunctions/anomalies occur frequently in CDS alert systems. It is important to be able to detect such anomalies promptly. Anomaly detection models are useful tools to aid such detections.
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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.