ArticleJournal of the American Medical Informatics Association : JAMIA2016
Real-time prediction of inpatient length of stay for discharge prioritization.
Article in Journal of the American Medical Informatics Association : JAMIA, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic literature review of predicting patient discharges using statistical methods and machine learning.Health care management science · 2024Pooled it
- Earlier medically ready for discharge date entry associated with lower unnecessary bed days.BMC health services research · 2026Article
- Machine learning models for early mortality prediction in trauma patients using public data: a nationwide retrospective study.World journal of emergency surgery : WJES · 2026Article
- Development and validation of a model predicting preterm infant discharge in level 2 care.Pediatric research · 2026Article
- Toward Real-Time Discharge Volume Predictions in Multisite Health Care Systems: Longitudinal Observational Study.Journal of medical Internet research · 2025Observational
- Development of Multiservice Machine Learning Models to Predict Postsurgical Length of Stay and Discharge Disposition at the Time of Case Posting.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2025Article
- Developing a decision support tool to predict delayed discharge from hospitals using machine learning.BMC health services research · 2025Article
- Concurrent prediction of in-hospital mortality and length of stay using single-task, multiclass and multitask machine learning.BMJ digital health & AI · 2025Article
- Explainable predictions of a machine learning model to forecast the postoperative length of stay for severe patients: machine learning model development and evaluation.BMC medical informatics and decision making · 2024Article
- Predicting individual patient and hospital-level discharge using machine learning.Communications medicine · 2024Article
- Feasibility of forecasting future critical care bed availability using bed management data.BMJ health & care informatics · 2024Article
- Artificial Intelligence in Perioperative Care: Opportunities and Challenges.Anesthesiology · 2024Article
- Beyond the Bedside: Machine Learning-Guided Length of Stay (LOS) Prediction for Cardiac Patients in Tertiary Care.Healthcare (Basel, Switzerland) · 2024Article
- Development and validation of a machine learning model integrated with the clinical workflow for inpatient discharge date prediction.Frontiers in digital health · 2024Article
- Early Expected Discharge Date Accuracy During Hospitalization: A Multivariable Analysis.Journal of medical systems · 2023Article
- Time-to-event modeling for hospital length of stay prediction for COVID-19 patients.Machine learning with applications · 2022Article
- Exploratory analysis of novel electronic health record variables for quantification of healthcare delivery strain, prediction of mortality, and prediction of imminent discharge.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- The prediction of hospital length of stay using unstructured data.BMC medical informatics and decision making · 2021Observational
- Predicting next-day discharge via electronic health record access logs.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Machine Learning-Based Hospital Discharge Prediction for Patients With Cardiovascular Diseases: Development and Usability Study.JMIR medical informatics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveHospitals are challenged to provide timely patient care while maintaining high resource utilization. This has prompted hospital initiatives to increase patient flow and minimize nonvalue added care time. Real-time demand capacity management (RTDC) is one such initiative whereby clinicians convene each morning to predict patients able to leave the same day and prioritize their remaining tasks for early discharge. Our objective is to automate and improve these discharge predictions by applying supervised machine learning methods to readily available health information. MATERIALS AND
methodsThe authors use supervised machine learning methods to predict patients' likelihood of discharge by 2 p.m. and by midnight each day for an inpatient medical unit. Using data collected over 8000 patient stays and 20 000 patient days, the predictive performance of the model is compared to clinicians using sensitivity, specificity, Youden's Index (i.e., sensitivity + specificity - 1), and aggregate accuracy measures.
resultsThe model compared to clinician predictions demonstrated significantly higher sensitivity (P < .01), lower specificity (P < .01), and a comparable Youden Index (P > .10). Early discharges were less predictable than midnight discharges. The model was more accurate than clinicians in predicting the total number of daily discharges and capable of ranking patients closest to future discharge.
conclusionsThere is potential to use readily available health information to predict daily patient discharges with accuracies comparable to clinician predictions. This approach may be used to automate and support daily RTDC predictions aimed at improving patient flow.
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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.