SynthesisHealth care management science2024
A systematic literature review of predicting patient discharges using statistical methods and machine learning.
Synthesis in Health care management science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Looking for the crystal ball in unscheduled care: a systematic literature review of the forecasting process.Health care management science · 2025Pooled it
- Standard-of-care vs. machine learning-recommended discharge destinations for geriatric surgical inpatients: algorithm development and internal validation.European geriatric medicine · 2026Article
- Observational
- Toward generalizable and interpretable machine learning models in healthcare: Insights from ICU outcome predictions.Health care management science · 2026Article
- Artificial Intelligence in Healthcare Administration and Clinical Informatics: A Critical Review and Governance Roadmap.Healthcare (Basel, Switzerland) · 2026Review
- Artificial Intelligence for Medicines Information: Scoping Review of Clinical Applications and Digital Health Inequalities.Journal of medical Internet research · 2026Article
- Predicting emergency department disposition using machine learning and large language models to support proactive capacity management: a multicenter retrospective study.BMC emergency medicine · 2026Article
- Machine learning-enhanced prediction of operating room occupation time and length of stay: a retrospective cohort study on emergency surgery care pathways.Journal of clinical monitoring and computing · 2025Article
- The crucial role of explainable artificial intelligence (XAI) in improving health care management.Health care management science · 2025Article
- Deep Learning Predicts Postoperative Mobility, Activities of Daily Living, and Discharge Destination in Older Adults from Sensor Data.Sensors (Basel, Switzerland) · 2025Article
- Review
- Systematic Comparison of Different Compartmental Models for Predicting COVID-19 Progression.Epidemiologia (Basel, Switzerland) · 2025Article
- Developing a decision support tool to predict delayed discharge from hospitals using machine learning.BMC health services research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Discharge planning is integral to patient flow as delays can lead to hospital-wide congestion. Because a structured discharge plan can reduce hospital length of stay while enhancing patient satisfaction, this topic has caught the interest of many healthcare professionals and researchers. Predicting discharge outcomes, such as destination and time, is crucial in discharge planning by helping healthcare providers anticipate patient needs and resource requirements. This article examines the literature on the prediction of various discharge outcomes. Our review discovered papers that explore the use of prediction models to forecast the time, volume, and destination of discharged patients. Of the 101 reviewed papers, 49.5% looked at the prediction with machine learning tools, and 50.5% focused on prediction with statistical methods. The fact that knowing discharge outcomes in advance affects operational, tactical, medical, and administrative aspects is a frequent theme in the papers studied. Furthermore, conducting system-wide optimization, predicting the time and destination of patients after discharge, and addressing the primary causes of discharge delay in the process are among the recommendations for further research in this field.
Indexed as
Identifiers
What OpenQuestion holds
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.