AIMC Topic: Waiting Lists
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Dissatisfaction-considered waiting time prediction for outpatients with interpretable machine learning.
Health care management science
Jun 1, 2024
Long waiting time in outpatient departments is a crucial factor in patient dissatisfaction. We aim to analytically interpret the waiting times predicted by machine learning models and provide patients with an explanation of the expected waiting time....
A NLP-based semi-automatic identification system for delays in follow-up examinations: an Italian case study on clinical referrals.
BMC medical informatics and decision making
Apr 23, 2024
BACKGROUND: This study aims to propose a semi-automatic method for monitoring the waiting times of follow-up examinations within the National Health System (NHS) in Italy, which is currently not possible to due the absence of the necessary structured...
Prediction of hospitalization and waiting time within 24 hours of emergency department patients with unstructured text data.
Health care management science
Nov 3, 2023
Overcrowding of emergency departments is a global concern, leading to numerous negative consequences. This study aimed to develop a useful and inexpensive tool derived from electronic medical records that supports clinical decision-making and can be ...
Deep Learning-Based Survival Analysis for Receiving a Steatotic Donor Liver Versus Waiting for a Standard Liver.
Transplantation proceedings
Oct 22, 2023
BACKGROUND: An emerging strategy to expand the donor pool is the use of a steatotic donor liver (SDLs; ≥ 30% macrosteatosis on biopsy). With the obesity epidemic and prevalence of nonalcoholic fatty liver disease, SDLs have been reported in 59% of al...
Ranking patients on the kidney transplant waiting list based on fuzzy inference system.
BMC nephrology
Jan 15, 2022
BACKGROUND: Kidney transplantation is the best treatment for people with End-Stage Renal Disease (ESRD). Kidney allocation is the most important challenge in kidney transplantation process. In this study, a Fuzzy Inference System (FIS) was developed ...
Identifying scenarios of benefit or harm from kidney transplantation during the COVID-19 pandemic: A stochastic simulation and machine learning study.
American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons
Jul 15, 2020
Clinical decision-making in kidney transplant (KT) during the coronavirus disease 2019 (COVID-19) pandemic is understandably a conundrum: both candidates and recipients may face increased acquisition risks and case fatality rates (CFRs). Given our po...
Predicting Outpatient Appointment Demand Using Machine Learning and Traditional Methods.
Journal of medical systems
Jul 19, 2019
Traditional methods have long been used for clinical demand forecasting. Machine learning methods represent the next evolution in forecasting, but model choice and optimization remain challenging for achieving optimal results. To determine the best m...
Machine Learning for Predicting Patient Wait Times and Appointment Delays.
Journal of the American College of Radiology : JACR
Oct 24, 2017
Being able to accurately predict waiting times and scheduled appointment delays can increase patient satisfaction and enable staff members to more accurately assess and respond to patient flow. In this work, the authors studied the applicability of m...
Ubiquitous Multicriteria Clinic Recommendation System.
Journal of medical systems
Mar 17, 2016
Advancements in information, communication, and sensor technologies have led to new opportunities in medical care and education. Patients in general prefer visiting the nearest clinic, attempt to avoid waiting for treatment, and have unequal preferen...
Patterns in GP appointment systems: a cluster analysis of 3480 English practices.
The British journal of general practice : the journal of the Royal College of General Practitioners
Aug 1, 2025
BACKGROUND: In response to increasing demand for appointments, UK general practices have adopted a range of appointment systems. These systems vary widely in implementation. These changes have not yet been clearly described.
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