AIMC Topic: Patient Discharge
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Improving Large Language Models' Summarization Accuracy by Adding Highlights to Discharge Notes: Comparative Evaluation.
JMIR medical informatics
Jul 24, 2025
BACKGROUND: The American Medical Association recommends that electronic health record (EHR) notes, often dense and written in nuanced language, be made readable for patients and laypeople, a practice we refer to as the simplification of discharge not...
Developing interpretable machine learning models to predict length of stay and disposition decision for adult patients in emergency departments.
BMJ health & care informatics
Jun 26, 2025
OBJECTIVE: Machine learning (ML) models have emerged as tools to predict length of stay (LOS) and disposition decision (DD) in emergency departments (EDs) to combat overcrowding. However, site-specific ML models are not transferable to different site...
A comparative study of recent large language models on generating hospital discharge summaries for lung cancer patients.
Journal of biomedical informatics
Jun 20, 2025
OBJECTIVE: Generating discharge summaries is a crucial yet time-consuming task in clinical practice, essential for conveying pertinent patient information and facilitating continuity of care. Recent advancements in large language models (LLMs) have s...
Using interpretable survival analysis to assess hospital length of stay.
BMC health services research
May 22, 2025
Accurate in-hospital length of stay prediction is a vital quality metric for hospital leaders and health policy decision-makers. It assists with decision-making and informs hospital operations involving factors such as patient flow, elective cases, a...
Strategies to Reduce Hospital Length of Stay: Evidence and Challenges.
Medicina (Kaunas, Lithuania)
May 20, 2025
Hospital length of stay (HLOS) is a critical healthcare metric influencing patient outcomes, resource utilization, and healthcare costs. While reducing HLOS can improve hospital efficiency and patient throughput, it also poses risks such as premature...
Automated generation of discharge summaries: leveraging large language models with clinical data.
Scientific reports
May 12, 2025
This study explores the use of open-source large language models (LLMs) to automate generation of German discharge summaries from structured clinical data. The structured data used to produce AI-generated summaries were manually extracted from electr...
Post-Anesthesia Care Unit (PACU) readiness predictions using machine learning: a comparative study of algorithms.
BMC medical informatics and decision making
Mar 25, 2025
INTRODUCTION: Accurate and timely discharge from the Post-Anesthesia Care Unit (PACU) is essential to prevent postoperative complications and optimize hospital resource utilization. Premature discharge can lead to severe issues such as respiratory or...
Evaluation and comparison of machine learning algorithms for predicting discharge against medical advice in injured inpatients.
Surgery
Mar 23, 2025
BACKGROUND: Whether the application of machine learning algorithms offers an advantage over logistic regression in forecasting discharge against medical advice occurrences needs to be evaluated.
Using Machine Learning to Identify Social Determinants of Health that Impact Discharge Disposition for Hospitalized Patients.
Journal of the American Medical Directors Association
Mar 20, 2025
OBJECTIVE: To identify self-reported social determinants of health (SDOH) among hospitalized patients that predict discharge to a skilled nursing facility (SNF).
Predicting Readmission Among High-Risk Discharged Patients Using a Machine Learning Model With Nursing Data: Retrospective Study.
JMIR medical informatics
Mar 5, 2025
BACKGROUND: Unplanned readmissions increase unnecessary health care costs and reduce the quality of care. It is essential to plan the discharge care from the beginning of hospitalization to reduce the risk of readmission. Machine learning-based readm...
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