ABSTRACT
The quality of service in healthcare is constantly challenged by outlier events such as pandemics (i.e., Covid-19) and natural disasters (such as hurricanes and earthquakes). In most cases, such events lead to critical uncertainties in decision-making, as well as in multiple medical and economic aspects at a hospital. External (geographic) or internal factors (medical and managerial) lead to shifts in planning and budgeting, but most importantly, reduce confidence in conventional processes. In some cases, support from other hospitals proves necessary, which exacerbates the planning aspect. This paper presents three data-driven methods that provide data-driven indicators to help healthcare managers organize their economics and identify the most optimum plan for resources allocation and sharing. Conventional decision-making methods fall short in recommending validated policies for managers. Using reinforcement learning, genetic algorithms, traveling salesman, and clustering, we experimented with different healthcare variables and presented tools and outcomes that could be applied at health institutes. Experiments are performed; the results are recorded, evaluated, and presented.
ABSTRACT
Data-driven healthcare policy discussions are gaining traction after the Covid-19 outbreak and ahead of the 2020 US presidential elections. The US has a hybrid healthcare structure; it is a system that does not provide universal coverage, albeit few years ago enacted a mandate (Affordable Care Act-ACA) that provides coverage for the majority of Americans. The US has the highest health expenditure per capita of all western and developed countries; however, most Americans don't tap into the benefits of preventive healthcare. It is estimated that only 8% of Americans undergo routine preventive screenings. On a national level, very few states (15 out of the 50) have above-average preventive healthcare metrics. In literature, many studies focus on the cure of diseases (research areas such as drug discovery and disease prediction); whilst a minority have examined data-driven preventive measures-a matter that Americans and policy makers ought to place at the forefront of national issues. In this work, we present solutions for preventive practices and policies through Machine Learning (ML) methods. ML is morally neutral, it depends on the data that train the models; in this work, we make the case that Big Data is an imperative paradigm for healthcare. We examine disparities in clinical data for US patients by developing correlation and imputation methods for data completeness. Non-conventional patterns are identified. The data lifecycle followed is methodical and deliberate; 1000+ clinical, demographical, and laboratory variables are collected from the Centers for Disease Control and Prevention (CDC). Multiple statistical models are deployed (Pearson correlations, Cramer's V, MICE, and ANOVA). Other unsupervised ML models are also examined (K-modes and K-prototypes for clustering). Through the results presented in the paper, pointers to preventive chronic disease tests are presented, and the models are tested and evaluated.