AIMC Topic: Waiting Lists
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Predicting patient risk of leaving without being seen using machine learning: a retrospective study in a single overcrowded emergency department.
BMC emergency medicine
Jul 15, 2025
Emergency department (ED) overcrowding has become a critical issue in hospital management, leading to increased patient wait times and higher rates of individuals leaving without being seen (LWBS). This study aims to identify key factors influencing ...
Artificial Intelligence Solutions to Improve Emergency Department Wait Times: Living Systematic Review.
The Journal of emergency medicine
Jun 10, 2025
BACKGROUND: Overcrowding and long wait times in emergency departments (EDs) remain global challenges that negatively affect patient outcomes and staff satisfaction. As an emerging technology, artificial intelligence (AI) offers the potential to optim...
The Effectiveness of a Chatbot Single-Session Intervention for People on Waitlists for Eating Disorder Treatment: Randomized Controlled Trial.
Journal of medical Internet research
May 21, 2025
BACKGROUND: Early treatment is critical for improving eating disorder prognosis. Single-session interventions (SSIs) can provide short-term support to people on waitlists for eating disorder treatment; however, it is not always possible to access SSI...
Interpretable machine learning models for prolonged Emergency Department wait time prediction.
BMC health services research
Mar 18, 2025
OBJECTIVE: Prolonged Emergency Department (ED) wait times lead to diminished healthcare quality. Utilizing machine learning (ML) to predict patient wait times could aid in ED operational management. Our aim is to perform a comprehensive analysis of M...
Development of a Machine Learning-Powered Optimized Lung Allocation System for Maximum Benefits in Lung Transplantation: A Korean National Data.
Journal of Korean medical science
Feb 24, 2025
BACKGROUND: An ideal lung allocation system should reduce waiting list deaths, improve transplant survival, and ensure equitable organ allocation. This study aimed to develop a novel lung allocation score (LAS) system, the MaxBenefit LAS, to maximize...
Real-Time Analytics and AI for Managing No-Show Appointments in Primary Health Care in the United Arab Emirates: Before-and-After Study.
JMIR formative research
Jan 6, 2025
BACKGROUND: Primary health care (PHC) services face operational challenges due to high patient volumes, leading to complex management needs. Patients access services through booked appointments and walk-in visits, with walk-in visits often facing lon...
Machine learning for post-liver transplant survival: Bridging the gap for long-term outcomes through temporal variation features.
Computer methods and programs in biomedicine
Sep 29, 2024
BACKGROUND: The long-term survival of liver transplant (LT) recipients is essential for optimizing organ allocation and estimating mortality outcomes. While models like the Model-for-End-Stage-Liver-Disease (MELD) predict 90-day mortality on the wait...
Investigation of emergency department abandonment rates using machine learning algorithms in a single centre study.
Scientific reports
Aug 22, 2024
A critical problem that Emergency Departments (EDs) must address is overcrowding, as it causes extended waiting times and increased patient dissatisfaction, both of which are immediately linked to a greater number of patients who leave the ED early, ...
[Prioritized appointment allocation in new patients, what is really decisive? : Comparative analysis of manual appointment allocation with automated and AI-assisted approaches].
Zeitschrift fur Rheumatologie
Aug 16, 2024
BACKGROUND: The timely allocation of appointments for new patients is a daily challenge in rheumatological practice, which can be supported by digital solutions. The question is to find the simplest and most effective possible method for prioritizati...
Managing low-acuity patients in an Emergency Department through simulation-based multiobjective optimization using a neural network metamodel.
Health care management science
Jun 10, 2024
This paper deals with Emergency Department (ED) fast-tracks for low-acuity patients, a strategy often adopted to reduce ED overcrowding. We focus on optimizing resource allocation in minor injuries units, which are the ED units that can treat low-acu...
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