Decision analysis framework for predicting no-shows to appointments using machine learning algorithms.

Background: No-show to medical appointments has significant adverse effects on healthcare systems and their clients. Using machine learning to predict no-shows allows managers to implement strategies such as overbooking and reminders targeting patients most likely to miss appointments, optimizing the use of resources.

Methods: In this study, we proposed a detailed analytical framework for predicting no-shows while addressing imbalanced datasets.

Layout Planning in Healthcare Facilities: A Systematic Review.

This study presents a systematic review of the literature on layout planning in healthcare facilities. The review includes 81 articles from journals, conferences, books, and other documents. Articles were classified in two groups according to their main contents including (i) concepts and guidelines and (ii) techniques and tools to assist in layout planning in healthcare facilities.

Text mining approach to predict hospital admissions using early medical records from the emergency department.

Objective: Emergency department (ED) overcrowding is a serious issue for hospitals. Early information on short-term inward bed demand from patients receiving care at the ED may reduce the overcrowding problem, and optimize the use of hospital resources. In this study, we use text mining methods to process data from early ED patient records using the SOAP framework, and predict future hospitalizations and discharges.