ArticleHealth care management science2019
Classification of hospital admissions into emergency and elective care: a machine learning approach.
Article in Health care management science, 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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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine Learning in Medical Emergencies: a Systematic Review and Analysis.Journal of medical systems · 2021Pooled it
- The Use of Machine Learning in Emergency Care Units: A Systematic Review.Journal of primary care & community healthPooled it
- Data-driven identification of outpatient-suitable procedures: a machine learning approach.Health care management science · 2026Article
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- Utilization of acute medical services in general practice: a retrospective routine data analysis.International journal of emergency medicine · 2025Article
- A retrospective analysis of the incidence and risk factors of perioperative urinary tract infections after total hysterectomy.BMC women's health · 2024Article
- Forecasting ward-level bed requirements to aid pandemic resource planning: Lessons learned and future directions.Health care management science · 2023Article
- A Machine Learning Approach for Monitoring and Classifying Healthcare Data-A Case of Emergency Department of KSA Hospitals.International journal of environmental research and public health · 2023Article
- Big Data Analytics to Reduce Preventable Hospitalizations-Using Real-World Data to Predict Ambulatory Care-Sensitive Conditions.International journal of environmental research and public health · 2023Article
- Finding good alternatives to hospitalisation: a data register study in five municipal acute wards in Norway.BMC health services research · 2022Article
- The path from big data analytics capabilities to value in hospitals: a scoping review.BMC health services research · 2022Article
- Leveraging electronic health record data to inform hospital resource management : A systematic data mining approach.Health care management science · 2021Article
- Multilevel Zero-One Inflated Beta Regression Model for the Analysis of the Relationship between Exogenous Health Variables and Technical Efficiency in the Spanish National Health System Hospitals.International journal of environmental research and public health · 2021Article
- Machine learning in patient flow: a review.Progress in biomedical engineering (Bristol, England) · 2021Review
- Investigating the link between medical urgency and hospital efficiency - Insights from the German hospital market.Health care management science · 2020Article
- Severity of illness and the weekend mortality effect: a retrospective cohort study.BMC health services research · 2020Article
- [Routine Data from Emergency Departments: Varying Documentation Standards, Billing Modalities and Data Custodians at an Identical Unit of Care].Gesundheitswesen (Bundesverband der Arzte des Offentlichen Gesundheitsdienstes (Germany)) · 2020Article
- Risk Factors for Pulmonary Embolism in ICU Patients: A Retrospective Cohort Study from the MIMIC-III Database.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisArticle
- Does What Happens in the ED Stay in the ED? The Effects of Emergency Department Physician Workload on Post-ED Care Use.Manufacturing & service operations management : M & SOMArticle
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
No grant is acknowledged in the PubMed record.
Abstract
Rising admissions from emergency departments (EDs) to hospitals are a primary concern for many healthcare systems. The issue of how to differentiate urgent admissions from non-urgent or even elective admissions is crucial. We aim to develop a model for classifying inpatient admissions based on a patient's primary diagnosis as either emergency care or elective care and predicting urgency as a numerical value. We use supervised machine learning techniques and train the model with physician-expert judgments. Our model is accurate (96%) and has a high area under the ROC curve (>.99). We provide the first comprehensive classification and urgency categorization for inpatient emergency and elective care. This model assigns urgency values to every relevant diagnosis in the ICD catalog, and these values are easily applicable to existing hospital data. Our findings may provide a basis for policy makers to create incentives for hospitals to reduce the number of inappropriate ED admissions.
Indexed as
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29177993What 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.