AIMC Topic: No-Show Patients
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Predicting Missed Appointments in Primary Care: A Personalized Machine Learning Approach.
Annals of family medicine
Jul 28, 2025
PURPOSE: Factors influencing missed appointments are complex and difficult to anticipate and intervene against. To optimize appointment adherence, we aimed to use personalized machine learning and big data analytics to predict the risk of and contrib...
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 ...
Predictive Optimization of Patient No-Show Management in Primary Healthcare Using Machine Learning.
Journal of medical systems
Jan 14, 2025
The "no-show" problem in healthcare refers to the prevalent phenomenon where patients schedule appointments with healthcare providers but fail to attend them without prior cancellation or rescheduling. In addressing this issue, our study delves into ...
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...
Public health nurse perspectives on predicting nonattendance for cervical cancer screening through classification, ensemble, and deep learning models.
Public health nursing (Boston, Mass.)
May 17, 2024
OBJECTIVES: Women's attendance to cervical cancer screening (CCS) is a major concern for healthcare providers in community. This study aims to use the various algorithms that can accurately predict the most barriers of women for nonattendance to CS.
Efficient Prediction of Missed Clinical Appointment Using Machine Learning.
Computational and mathematical methods in medicine
Oct 22, 2021
Public health and its related facilities are crucial for thriving cities and societies. The optimum utilization of health resources saves money and time, but above all, it saves precious lives. It has become even more evident in the present as the pa...
Application of Machine Learning to Predict Patient No-Shows in an Academic Pediatric Ophthalmology Clinic.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 25, 2021
Patient "no-shows" are missed appointments resulting in clinical inefficiencies, revenue loss, and discontinuity of care. Using secondary electronic health record (EHR) data, we used machine learning to predict patient no-shows in follow-up and new p...
Artificial Intelligence Predictive Analytics in the Management of Outpatient MRI Appointment No-Shows.
AJR. American journal of roentgenology
Sep 9, 2020
Outpatient appointment no-shows are a common problem. Artificial intelligence predictive analytics can potentially facilitate targeted interventions to improve efficiency. We describe a quality improvement project that uses machine learning techniqu...
Prediction of hospital no-show appointments through artificial intelligence algorithms.
Annals of Saudi medicine
Dec 5, 2019
BACKGROUND: No-shows, a major issue for healthcare centers, can be quite costly and disruptive. Capacity is wasted and expensive resources are underutilized. Numerous studies have shown that reducing uncancelled missed appointments can have a tremend...
Machine-Learning-Based Prediction of a Missed Scheduled Clinical Appointment by Patients With Diabetes.
Journal of diabetes science and technology
May 3, 2016
BACKGROUND: About 10% of patients with diabetes discontinue treatment, resulting in the progression of diabetes-related complications and reduced quality of life.
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