Design and evaluation of analytical tools for emergency department management based on machine learning techniques

The Spanish National Healthcare System (NHS) is mostly publicly funded and provided. It is considered highly cost-efficient according to international studies based on World Health Organization (WHO) data. However, the contention of healthcare costs increases while maintaining adequate levels of quality of care, is still a largely unsolved problem. In recent years, Emergency Departments (EDs) of specialized care hospitals have been subjected to budget restrictions, increased visits and increased clinical complexity of these visits. These circumstances require new approaches to ED management, which could benefit from decision support tools. In this Ph.D. thesis, we propose machine learning solutions for two problems common to most EDs of specialized care hospitals: ED census forecasting and real-time prediction of probabilities of inpatient admission for all triaged patients in the ED. These solutions could be used as decision support systems. Data for the development of these solutions were provided by the Ramon y Cajal University Hospital of Madrid, a large specialized care referral center with all medical specialties excepting Obstetrics. In years 2011 and 2012 it had approximately 1,100 beds and approximately 553,000 patients assigned to its clinical area. Another topic of this Ph.D. thesis are software tools for the generation of logistic regression and Cox regression nomograms, since nomograms can be used as clinical decision aids and as contingency procedures in case of failure of computer-based decision support systems. The first topic of this Ph.D. thesis is the development of models for ED census forecasting (i.e. prediction of the number of patients present at the ED at a given time). One of the uses of ED census forecasting is nursing personnel allocation, based on national and international recommendations. We chose an 8-hour granularity for our forecasts since many resources (such as nursing personnel) in the ED are organized in 8-hour shifts. Our aim was to generate forecasts for two dependent ...