ArticleNPJ digital medicine2021
Prediction across healthcare settings: a case study in predicting emergency department disposition.
Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06434220 (Effect of Predictive Model on ED Physician Assessments of Patient Disposition), which is not on this map. Cited by 18 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Effect of Predictive Model on ED Physician Assessments of Patient Disposition
Who cites it
18 citing papers in PubMed.
- Multicenter validation and updating of the ELDER-ICU model for severity assessment in elderly critical illness.NPJ digital medicine · 2026Article
- 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
- The future of algorithmic nondiscrimination compliance in the affordable care act.NPJ digital medicine · 2025Review
- High-Performance Open-Source AI for Breast Cancer Detection and Localization in MRI.Radiology. Artificial intelligence · 2025Article
- Developing interpretable machine learning models to predict length of stay and disposition decision for adult patients in emergency departments.BMJ health & care informatics · 2025Article
- Multicenter Validation of a Machine Learning Model for Surgical Transfusion Risk at 45 US Hospitals.JAMA network open · 2025Article
- Intersectional and Marginal Debiasing in Prediction Models for Emergency Admissions.JAMA network open · 2025Article
- Predicting emergency department admissions using a machine-learning algorithm: a proof of concept with retrospective study.BMC emergency medicine · 2025Article
- Prognostic prediction of dengue hemorrhagic fever in pediatric patients with suspected dengue infection: A multi-site study.PloS one · 2025Article
- Associations Between Anthropogenic Factors, Meteorological Factors, and Cause-Specific Emergency Department Admissions.GeoHealth · 2024Article
- The value of parental medical records for the prediction of diabetes and cardiovascular disease: a novel method for generating and incorporating family histories.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Article
- Fair admission risk prediction with proportional multicalibration.Proceedings of machine learning research · 2023Article
- Assessing optimal methods for transferring machine learning models to low-volume and imbalanced clinical datasets: experiences from predicting outcomes of Danish trauma patients.Frontiers in digital health · 2023Article
- Article
- Machine learning for real-time aggregated prediction of hospital admission for emergency patients.NPJ digital medicine · 2022Article
- A machine learning approach to identifying delirium from electronic health records.JAMIA open · 2022Article
- Machine learning generalizability across healthcare settings: insights from multi-site COVID-19 screening.NPJ digital medicine · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Several approaches exist today for developing predictive models across multiple clinical sites, yet there is a lack of comparative data on their performance, especially within the context of EHR-based prediction models. We set out to provide a framework for prediction across healthcare settings. As a case study, we examined an ED disposition prediction model across three geographically and demographically diverse sites. We conducted a 1-year retrospective study, including all visits in which the outcome was either discharge-to-home or hospitalization. Four modeling approaches were compared: a ready-made model trained at one site and validated at other sites, a centralized uniform model incorporating data from all sites, multiple site-specific models, and a hybrid approach of a ready-made model re-calibrated using site-specific data. Predictions were performed using XGBoost. The study included 288,962 visits with an overall admission rate of 16.8% (7.9-26.9%). Some risk factors for admission were prominent across all sites (e.g., high-acuity triage emergency severity index score, high prior admissions rate), while others were prominent at only some sites (multiple lab tests ordered at the pediatric sites, early use of ECG at the adult site). The XGBoost model achieved its best performance using the uniform and site-specific approaches (AUC = 0.9-0.93), followed by the calibrated-model approach (AUC = 0.87-0.92), and the ready-made approach (AUC = 0.62-0.85). Our results show that site-specific customization is a key driver of predictive model performance.
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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.