ArticleJournal of the American Medical Informatics Association : JAMIA2019
Predicting emergency department orders with multilabel machine learning techniques and simulating effects on length of stay.
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 19 papers, 2 of them syntheses that pooled it.
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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
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Applications of Artificial Intelligence and Machine Learning in Emergency Medicine Triage - A Systematic Review.Medical archives (Sarajevo, Bosnia and Herzegovina) · 2024Pooled it
- Influence of artificial intelligence on the work design of emergency department clinicians a systematic literature review.BMC health services research · 2022Pooled it
- OrderRex clinical user testing: a randomized trial of recommender system decision support on simulated cases.Journal of the American Medical Informatics Association : JAMIA · 2020Trial
- Review
- Deciphering the influence of demographic factors on the treatment of pediatric patients in the emergency department.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- Combined Applications of Artificial Intelligence and Simulation for Healthcare Process Optimization: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- In Silico Evaluation of Algorithm-Based Clinical Decision Support Systems: Protocol for a Scoping Review.JMIR research protocols · 2025Article
- PheWJAMIA open · 2024Article
- Article
- Leveraging machine learning and rule extraction for enhanced transparency in emergency department length of stay prediction.Frontiers in digital health · 2024Article
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- Graph-based clinical recommender: Predicting specialists procedure orders using graph representation learning.Journal of biomedical informatics · 2023Article
- Benchmarking emergency department prediction models with machine learning and public electronic health records.Scientific data · 2022Article
- A Data-Driven Algorithm to Recommend Initial Clinical Workup for Outpatient Specialty Referral: Algorithm Development and Validation Using Electronic Health Record Data and Expert Surveys.JMIR medical informatics · 2022Article
- Designing a bed-side system for predicting length of stay in a neonatal intensive care unit.Scientific reports · 2021Article
- Article
- Economic evaluations of big data analytics for clinical decision-making: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Review
- Machine learning models for predicting extended length of stay and hospital charges in nontraumatic subarachnoid hemorrhage.Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveEmergency departments (EDs) continue to pursue optimal patient flow without sacrificing quality of care. The speed with which a healthcare provider receives pertinent information, such as results from clinical orders, can impact flow. We seek to determine if clinical ordering behavior can be predicted at triage during an ED visit. MATERIALS AND
methodsUsing data available during triage, we trained multilabel machine learning classifiers to predict clinical orders placed during an ED visit. We benchmarked 4 classifiers with 2 multilabel learning frameworks that predict orders independently (binary relevance) or simultaneously (random k-labelsets). We evaluated algorithm performance, calculated variable importance, and conducted a simple simulation study to examine the effects of algorithm implementation on length of stay and cost.
resultsAggregate performance across orders was highest when predicting orders independently with a multilayer perceptron (median F1 score = 0.56), but prediction frameworks that simultaneously predict orders for a visit enhanced predictive performance for correlated orders. Visit acuity was the most important predictor for most orders. Simulation results indicated that direct implementation of the model would increase ordering costs (from $21 to $45 per visit) but reduce length of stay (from 158 minutes to 151 minutes) over all visits. DISCUSSION: Simulated implementations of the predictive algorithm decreased length of stay but increased ordering costs. Optimal implementation of these predictions to reduce patient length of stay without incurring additional costs requires more exploration.
conclusionsIt is possible to predict common clinical orders placed during an ED visit with data available at triage.
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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.