ArticleBMC medical informatics and decision making2022
Machine learning based forecast for the prediction of inpatient bed demand.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 50 citations in OpenAlex.
- Emergency department crowding in the modern era: a systematic review (2018-2025).Clinical and experimental emergency medicine · 2026Pooled it
- A systematic literature review of predicting patient discharges using statistical methods and machine learning.Health care management science · 2024Pooled it
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- Forecasting hospital bed occupancy: a time series approach with prophet.BMC medical informatics and decision making · 2026Article
- Beyond Root Mean Squared Error: Expanding Operational Validation for Surgical Bed Forecasting Models.Journal of medical systems · 2026Article
- Dynamic demand forecasting and capacity assessment for emergency care system: a hybrid time series and machine learning approach with evidence from Beijing.BMC health services research · 2026Article
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- Predicting emergency department disposition using machine learning and large language models to support proactive capacity management: a multicenter retrospective study.BMC emergency medicine · 2026Article
- A Machine Learning Strategy to Predict the Number of High-Acuity Children Who Leave Without Being Seen From the Emergency Department.Journal of the American College of Emergency Physicians open · 2026Article
- Efficiency assessment and demand forecasting in China's primary healthcare system: a comprehensive SBM-DDF-GML analysis.Frontiers in public health · 2026Article
- Machine learning-based predictions of healthcare contacts following emergency hospitalisation using electronic health records.NPJ digital medicine · 2025Article
- Optimizing Emergency Response in Hospitals: A Systematic Review of Surge Capacity Planning and Crisis Resource Management.Healthcare (Basel, Switzerland) · 2025Review
- Developing a Quick Isolation Bed Inquiry System During the COVID-19 Outbreak: User-Centered Design Approach Based on the Toyota Production System.JMIR formative research · 2025Article
- A Simulation-Based Approach for Inpatient Capacity Management at a Hospital Dedicated for Cancer Treatment.Journal of medical systems · 2025Article
- The Impact of a Novel Transfer Process on Patient Bed Days and Length of Stay: A Five-Year Comparative Study at the Mayo Clinic in Rochester and Mankato Quaternary and Tertiary Care Centers.International journal of environmental research and public health · 2025Article
- Forecasting Surgical Bed Utilization: Architectural Design of a Machine Learning Pipeline Incorporating Predicted Length of Stay and Surgical Volume.Journal of medical systems · 2025Article
- Time series forecasting of bed occupancy in mental health facilities in India using machine learning.Scientific reports · 2025Article
- Data-driven identification of metabolic and cardiovascular biomarkers in high-altitude workers: a machine learning approach.Frontiers in public health · 2025Observational
- Hospitalization prediction from the emergency department using computer vision AI with short patient video clips.NPJ digital medicine · 2024Article
- Enhanced forecasting of emergency department patient arrivals using feature engineering approach and machine learning.BMC medical informatics and decision making · 2024Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOvercrowding is a serious problem that impacts the ability to provide optimal level of care in a timely manner. High patient volume is known to increase the boarding time at the emergency department (ED), as well as at post-anesthesia care unit (PACU). Furthermore, the same high volume increases inpatient bed transfer times, which causes delays in elective surgeries, increases the probability of near misses, patient safety incidents, and adverse events.
objectiveThe purpose of this study is to develop a Machine Learning (ML) based strategy to predict weekly forecasts of the inpatient bed demand in order to assist the resource planning for the ED and PACU, resulting in a more efficient utilization.
methodsThe data utilized included all adult inpatient encounters at Geisinger Medical Center (GMC) for the last 5 years. The variables considered were class of inpatient encounter, observation, or surgical overnight recovery (SORU) at the time of their discharge. The ML based strategy is built using the K-means clustering method and the Support Vector Machine Regression technique (K-SVR).
resultsThe performance obtained by the K-SVR strategy in the retrospective cohort amounts to a mean absolute percentage error (MAPE) that ranges between 0.49 and 4.10% based on the test period. Additionally, results present a reduced variability, which translates into more stable forecasting results.
conclusionsThe results from this study demonstrate the capacity of ML techniques to forecast inpatient bed demand, particularly using K-SVR. It is expected that the implementation of this model in the workflow of bed capacity management will create efficiencies, which will translate in a more reliable, inexpensive and timely care for patients.
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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.