Machine Learning in Population Health: Frequent Emergency Department Utilization Pattern Identification and Prediction

Emergency Department (ED) overcrowding is an emerging risk to patient safety and may significantly affect chronically ill people. For instance, overcrowding in an ED may cause delays in patient transportation or revenue loss for hospitals due to hospital diversion. Frequent users with avoidable visits play a significant role in imposing such challenges to ED settings. Non-urgent or "avoidable" ED use induces overcrowding and cost increases due to unnecessary tests and treatment. It is, therefore, valuable to understand the pattern of the ED visits among a population and prospectively identify ED frequent users, to provide stratified care management and resource allocation. Although most current models use classical methods like descriptive analysis or regression modelling, more sophisticated techniques may be needed to increase the accuracy of outcomes where big data is in use. This study focuses on the Machine Learning (ML) techniques to identify the ED usage pattern among frequent users and to evaluate the predicting ability of the models. I performed an extensive literature review to generate a list of potential predictors of ED frequent use. For this thesis, I used Korean Health Panel data from 2008 to 2015. Individuals with at least one ED visit were included, among whom those with four or more visits per year were considered frequent ED users. Demographic and clinical data was collected. The relationship between predictors and ED frequent use was examined through multivariable analysis. A K-modes clustering algorithm was applied to identify ED utilization patterns among frequent users. Finally, the performance of four machine learning classification algorithms was assessed and compared to logistic regression. The classification algorithms used in my thesis were Random Forest, Support Vector Machine (SVM), Bagging, and Voting. The models' performance was evaluated based on Positive Predictive Value (PPV), sensitivity, Area Under Curve (AUC), and classification error. A total of 9,348 individuals with 15,627 ED visits were eligible for this study. Frequent ED users accounted for 2.4% of all ED visits. Frequent ED users tended to be older, male, and more likely to be using ambulance as a mode of transport than non‐frequent ED users. In the cluster analysis, we identified three subgroups among frequent ED users: (i) older patients with respiratory system complaints, the highest discharged rates who were more likely to visit in Spring and Winter, (ii) older patients with the highest rate of hospitalization, who are also more likely to have used ambulance, and visited ED due to circulatory system complaints, (iii) younger patients, mostly female, with the highest rate of ED visits in summer, and lowest rate of using an ambulance, who visited ED mostly due to damages such as injuries, poisoning, etc. The ML classification algorithms predicted frequent ED users with high precision (90% - 98%) and sensitivity (87% - 91%), while showed high AUC scores from 89% for SVM to 96% for Random Forest, as well. The classification error varied among algorithms; logistic regression had the highest classification error (34.9%) while Random Forest had the least (3.8%). According to the Random Forest Importance Score, the top 5 factors predicting frequent users were disease category, age, day of the week, season, and sex. In this thesis, I showed how ML methods applies to ED users in population health. The study results show that ML classification algorithms are robust techniques with predictive power for future ED visit identification and prediction. As more data are collected and the amount of data availability increases, machine learning approaches is a promising tool for advancing the understanding of such ‘Big’ data

Operationalizing Frequent Emergency Department Use: A Systemic Perspective

Frequent emergency department (ED) use has been the topic of much conversation, research, and debate in recent years as the healthcare sector in the U.S. makes the transition from volume- to value-based care. Although there are systemic factors associated with frequent ED use, this phenomenon is operationalized in research and media solely by the number of visits a patient makes to the ED. This linear, unidimensional way of framing the problem leads to interventions and policies that focus on reducing the number of ED visits, while ignoring value-based measures of care such as health outcomes or whether patients are receiving appropriate kinds of care. This dissertation includes six chapters, comprising (a) an introduction to the dissertation, (b) a literature review examining the way in which frequent ED use is defined, and informs research, interventions, media, and policy, (c) a systematic review of research that defines frequent ED use, (d) a chapter outlining the methodology for the empirical research study, (e) an empirical research study using machine learning algorithms to develop ED patient cohorts or clusters based on systemic data, and finally (f) a policy brief in which recommendations are made based on the empirical findings of the original research from this dissertation

Predictive Modeling of FMOL Health System Utilization Using Machine Learning Algorithms and Retrospective Study of COVID Tested Patients

Overutilization of Emergency Departments (ED) is a major problem among the health care providers in the United States. In this research, a machine learning-based predictive model for predicting ED high utilizers will be designed based on a set of existing and proposed facilities and the population and social determinant of health (SDOH) factors influencing utilization. The purpose of the model will be to alert the healthcare systems and government organizations by identifying the reasons for overutilization of the medical services among the people in a particular community. Also, the novel coronavirus disease 2019 (COVID-19) developed in Whunan city, China has spread quickly to the other parts of the world. It has become a serious health threat to the United States. Moreover, in this research study, the clinical and social characteristics that are responsible for the quick spread of COVID-19 disease across the Louisiana state will be identified. The purpose of this study is to identify what kind of population gets COVID 19 and providing real time care decisions to minimize the risk of an individual acquiring COVID-19. The patient data from Electronic Health Records (EHR) of Francis Missionaries of our Lady Health System (FMOLHS) is geocoded and mapped into ArcGIS software. The socioeconomic factors and social vulnerability Index (SVI) variables available from various online sources are joined to the geocoded patient data with the help of spatial joining techniques available in the ArcGIS software. Correlation analysis between the dependent variables and factors will be conducted