The U.S. healthcare system is facing many public health challenges that affect population health, societal well-being, and quality of healthcare. Infant mortality, opioid overdose death, and hospital readmission after stroke are some of these important public health concerns that can impact the effectiveness and outcomes of the healthcare system. We analyze these problems through the industrial engineering and data analytics lens. The major goal of this dissertation is to enhance understanding of these three challenges and related interventions using different levels of analysis to improve the health outcomes. To attain this objective, I introduced three stand-alone papers to answer the related research questions.
In essay 1, we focused on the performance of the state's healthcare systems in reducing unfavorable birth outcomes such as infant mortality, preterm birth, and low birthweight using Data Envelopment Approach. We constructed a unique state-level dataset to answer this main research question: what does make a healthcare system more successful in improving the birth outcomes? Our results indicated that socioeconomic and demographic factors may facilitate or obstruct health systems in improving their outcomes. We realized that states with a lower rate of poverty and African-American women were more successful in effectively reduce unfavorable birth outcomes. In the second essay, we looked into the trends of the opioid overdose mortalities in each state from 2008 to 2017. We investigated the effect of four state laws and programs that have been established to curb the epidemic (i.e., dose and duration limitations on the initial prescription, pain management clinic laws, mandated use of prescription drug monitoring programs, and medical cannabis laws) in short and long-term, while we controlled for several protentional risk factors. The results of fixed-effect regression and significant tests indicated that state policies and laws were unlikely to result in an immediate reduction in overdose mortalities and comprehensive interventions were needed to restrain the epidemic. The third essay investigated the risk factors of 30-day readmission in patients with ischemic stroke at an individual level. We aimed to identify the main risk factors of stroke readmissions and prioritized them using machine learning techniques and logistic regression. We also introduced the most effective predictive model based on different performance metrics. We used the electronic health records of stroke patients extracted from two stroke centers within the Geisinger Health System from 2015 to 2018. This data set included a comprehensive list of clinical features, patients' comorbidities, demographical characteristics, discharge status, and type of health insurance. One of the major findings of this study was that stroke severity, insert an indwelling urinary catheter, and hypercoagulable state were more important than generally known diagnoses such as diabetes and hypertension in the prediction of stroke 30-day readmission. Furthermore, machine learning-based models can be designed to provide a better predictive model. Overall, this dissertation provided new insights to better understand the three major challenges of the U.S. healthcare system and improve its outcomes. / Doctor of Philosophy / The major goal of a healthcare system can be summarized in three main objectives: preventing preterm birth and premature mortality, advancing the quality of life, and preparing for a good death. Despite all the national efforts to achieve these goals, the U.S. healthcare system still faces many obstacles and crises and suffers from inefficiencies. The U.S. infant mortality rate is still higher than any other comparable advanced country. The opioid overdose death rate has been steadily increasing since 1999 and has risen exponentially in recent years. Hospital readmissions especially in stroke patients impose a substantial cost burden on the healthcare system in the U.S. Also, readmitted stroke patients are at higher risk of mortality compared to the first admission. I believe that industrial engineering and data analytics approaches can help in advancing the understanding of these health challenges, their important risk factors, and effective interventions. In this dissertation, the main focus was on the performance, trends, variations, and processes of the healthcare systems. We applied innovative methods to provide answers to the following questions in three essays: What does make a healthcare system more successful in improving the birth outcomes? What factors do explain mortality from opioid painkillers? What are the determinants of state variations in mortalities from an opioid overdose? What is the impact of states' laws and programs and opioid prescription rates and overdose mortality rates? What are the most important contributors to stroke readmissions? The results of the first essay showed that not all the state's healthcare systems perform the same in terms of reducing unfavorable birth outcomes. States with lower people in poverty and lower African American women were more successful in improving their birth outcomes. The second study revealed that states with a higher share of uninsured people and binge drinkers were suffering from higher opioid overdose deaths. Also, our results implied that in addition to upstream prevention policies, states need to implement downstream programs to curb the epidemic. Finally, the third study showed that the top predictors of stroke readmissions within 30 days consist of the severity of the stroke, insert an indwelling urinary catheter, being overweight, and malnourished. The results of this dissertation can help to educate policymakers and practitioners at state and organizational level in a way to better serve the society and ultimately enhance the population health, quality of healthcare, and societal well-being.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/99910 |
| Date | 04 September 2020 |
| Creators | Darabi, Negar |
| Contributors | Industrial and Systems Engineering, Hosseinichimeh, Niyousha, Abedi, Vida, Triantis, Konstantinos P., Kong, Zhenyu |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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