AIMC Topic: Waiting Lists

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Predicting Outpatient Appointment Demand Using Machine Learning and Traditional Methods.

Journal of medical systems
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
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
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
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.

Machine Learning for Predicting Waitlist Mortality in Pediatric Heart Transplantation.

Pediatric transplantation
BACKGROUND: Waitlist mortality remains a critical issue for pediatric heart transplant (HTx) candidates, particularly for candidates with congenital heart disease. Listing center organ offer acceptance practices have been identified as a factor influ...

Analysis of the most influential factors affecting outcomes of lung transplant recipients: a multivariate prediction model based on UNOS Data.

BMJ open
OBJECTIVES: In lung transplantation (LTx), a priority is assigned to each candidate on the waiting list. Our primary objective was to identify the key factors that influence the allocation of priorities in LTx using machine learning (ML) techniques t...

Applying Artificial Intelligence to Quantify Body Composition on Abdominal CTs and Better Predict Kidney Transplantation Wait-List Mortality.

Journal of the American College of Radiology : JACR
BACKGROUND: Prekidney transplant evaluation routinely includes abdominal CT for presurgical vascular assessment. A wealth of body composition data are available from these CT examinations, but they remain an underused source of data, often missing fr...

Kidney Allocation Policy: Past, Present, and Future.

Advances in chronic kidney disease
Despite an increase in the number of kidney transplants performed annually, there remain more than 90,000 individuals awaiting transplantation in the United States. As kidney transplantation has evolved, so has kidney allocation policies. The Kidney ...

Effect of a Predictive Model on Planned Surgical Duration Accuracy, Patient Wait Time, and Use of Presurgical Resources: A Randomized Clinical Trial.

JAMA surgery
IMPORTANCE: Accurate surgical scheduling affects patients, clinical staff, and use of physical resources. Although numerous retrospective analyses have suggested a potential for improvement, the real-world outcome of implementing a machine learning m...

The rise and fall of the model for end-stage liver disease score and the need for an optimized machine learning approach for liver allocation.

Current opinion in organ transplantation
PURPOSE OF REVIEW: The Model for End-Stage Liver Disease (MELD) has been used to rank liver transplant candidates since 2002, and at the time bringing much needed objectivity to the liver allocation process. However, and despite numerous revisions to...