AIMC Topic: Patient Discharge

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Evaluating Spanish Translations of Emergency Department Discharge Instructions by a Large Language Model: Tool Validation and Reliability Study.

JMIR formative research
When given a sample of 100 emergency department discharge instructions, Claude Sonnet, a large language model, produced accurate Spanish translations as evaluated by Spanish-speaking physicians and medical interpreters.

Development of a machine learning-based model for predicting the functional outcome of patients with proximal femur fractures.

Scientific reports
Early-stage rehabilitation is crucial for the functional recovery of patients with proximal femur fractures. Predicting functional prognosis at such an early stage can simplify the process of planning for transfers and discharge destinations, as well...

Predicting 30-day hospital readmissions using ClinicalT5 with structured and unstructured electronic health records.

PloS one
Hospital readmission prediction is a crucial area of research due to its impact on healthcare expenditure, patient care quality, and policy formulation. Accurate prediction of patient readmissions within 30 days post-discharge remains a considerable ...

Natural Language Processing and Coding for Detecting Bleeding Events in Discharge Summaries: Comparative Cross-Sectional Study.

JMIR medical informatics
BACKGROUND: Bleeding adverse drug events (ADEs), particularly among older inpatients receiving antithrombotic therapy, represent a major safety concern in hospitals. These events are often underdetected by conventional rule-based systems relying on s...

Improving Large Language Models' Summarization Accuracy by Adding Highlights to Discharge Notes: Comparative Evaluation.

JMIR medical informatics
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
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
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
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)
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...