One of the critical issues in the U.S. healthcare sector is attributed to medications management. Mismanagement of medications can not only bring more unfavorable medical outcomes for patients, but also imposes avoidable medical expenditures, which can be partially accounted for the enormous $750 billion that the American healthcare system wastes annually. The lack of efficiency in medical outcomes can be due to several reasons. One of them is the problem of drug intensification: a problem associated with more aggressive management of medications and its negative consequences for patients.

To address this and many other challenges in regard to medications mismanagement, I take advantage of data-driven methodologies where a decision-making framework for identifying optimal medications management strategies will be established based on real-world data. This data-driven approach has the advantage of supporting decision-making processes by data analytics, and hence, the decision made can be validated by verifiable data. Thus, compared to merely theoretical methods, my methodology will be more applicable to patients as the ultimate beneficiaries of the healthcare system.

Based on this premise, in this dissertation I attempt to analyze and advance three streams of research that are influenced by issues involving the management of medications/treatments for different medical contexts. In particular, I will discuss (1) management of medications/treatment modalities for new-onset of diabetes after solid organ transplantations and (2) epidemic of opioid prescription and abuse.

Healthcare operations have enjoyed reduced costs, improved patient safety, and

innovation in healthcare policy over a huge variety of applications by tackling prob-

lems via the creation and optimization of descriptive mathematical models to guide

decision-making. Despite these accomplishments, models are stylized representations

of real-world applications, reliant on accurate estimations from historical data to jus-

tify their underlying assumptions. To protect against unreliable estimations which

can adversely affect the decisions generated from applications dependent on fully-

realized models, techniques that are robust against misspecications are utilized while

still making use of incoming data for learning. Hence, new robust techniques are ap-

plied that (1) allow for the decision-maker to express a spectrum of pessimism against

model uncertainties while (2) still utilizing incoming data for learning. Two main ap-

plications are investigated with respect to these goals, the first being a percentile

optimization technique with respect to a multi-class queueing system for application

in hospital Emergency Departments. The second studies the use of robust forecasting

techniques in improving developing countries’ vaccine supply chains via (1) an inno-

vative outside of cold chain policy and (2) a district-managed approach to inventory

control. Both of these research application areas utilize data-driven approaches that

feature learning and pessimism-controlled robustness.

Hypertensive disorders of pregnancy (HDP) affect up to 5%-15% of pregnancies around the globe, and form a leading cause of maternal and neonatal morbidity and mortality. HDP are progressive disorders for which the only cure is to deliver the baby. An increasing trend in the prevalence of HDP has been observed in the recent years. This trend is anticipated to continue due to the rise in the prevalence of diseases that strongly influence hypertension such as obesity and metabolic syndrome. In order to lessen the adverse outcomes due to HDP, we need to study (1) the natural progression of HDP, (2) the risks of adverse outcomes associated with these disorders, and (3) the optimal timing of delivery for women with HDP.

In the first study, the natural progression of HDP in the third trimester of pregnancy is modeled with a discrete-time Markov chain (DTMC). The transition probabilities of the DTMC are estimated using clinical data with an order restricted inference model that maximizes the likelihood function subject to a set of order restrictions between the transition probabilities. The results provide useful insights on the progression of HDP, and the estimated transition probabilities are used to parametrize the decision models in the third study.

In the second study, the risks of maternal and neonatal adverse outcomes for women with HDP are quantified with a composite measure of childbirth morbidity, and the estimated risks are compared with respect to type of HDP at delivery, gestational age at delivery, and type of delivery in a retrospective cohort study. Furthermore, the safety of child delivery with respect to the same variables is assessed with a provider survey and technique for order performance by similarity to ideal solution (TOPSIS). The methods and results of this study are used to parametrize the decision models in the third study.

In the third study, the decision problem of timing of delivery for women with HDP is formulated as a discrete-time Markov decision process (MDP) model that minimizes the risks of maternal and neonatal adverse outcomes. We additionally formulate a robust MDP model that gives the worst-case optimal policy when transition probabilities are allowed to vary within their confidence intervals. The results of the decision models are assessed within a probabilistic sensitivity analysis (PSA) that considers the uncertainty in the estimated risk values. In our PSA, the performance of candidate delivery policies is evaluated using a large number of problem instances that are constructed according to the orders between model parameters to incorporate physicians' intuition.

In recent years, Operations Research (OR) has had a signicant impact on improving the performance of hospital Emergency Departments (EDs). This includes improving a wide range of processes involving patient ow from the initial call to the ED through disposition, discharge home, or admission to the hospital. We mainly seek to illustrate the benet of OR in EDs, and provide an overview of research performed in this vein to assist both researchers and practitioners. We also elaborate on possibilities for future researchers by shedding light on some less studied aspects that can have valuable impacts on practice.

Background: Hyperglycemia following solid organ transplant is common among patients without pre-existing diabetes mellitus (DM). Post-transplant hyperglycemia can occur once or multiple times, which if continued, causes new-onset diabetes after transplantation (NODAT).

Objective: To study if the first and recurrent incidence of hyperglycemia are affected differently by immunosuppressive regimens, demographic and medical-related risk factors, and inpatient hyperglycemic conditions (i.e., an emphasis on the time course of post-transplant complications).

Methods: We conducted a retrospective analysis of 407 patients who underwent kidney transplantation at Mayo Clinic Arizona. Among these, there were 292 patients with no signs of DM prior to transplant. For this category of patients, we evaluated the impact of (1) immunosuppressive drugs (e.g., tacrolimus, sirolimus, and steroid), (2) demographic and medical-related risk factors, and (3) inpatient hyperglycemic conditions on the first and recurrent incidence of hyperglycemia in one year post-transplant. We employed two versions of Cox regression analyses: (1) a time-dependent model to analyze the recurrent cases of hyperglycemia and (2) a time-independent model to analyze the first incidence of hyperglycemia.

Results: Age (P = 0.018), HDL cholesterol (P = 0.010), and the average trough level of tacrolimus (P<0.0001) are significant risk factors associated with the first incidence of hyperglycemia, while age (P<0.0001), non-White race (P = 0.002), BMI (P = 0.002), HDL cholesterol (P = 0.003), uric acid (P = 0.012), and using steroid (P = 0.007) are the significant risk factors for the recurrent cases of hyperglycemia.

Technological advances have enabled the generation and collection of various data from complex systems, thus, creating ample opportunity to integrate knowledge in many decision making applications. This dissertation introduces holistic learning as the integration of a comprehensive set of relationships that are used towards the learning objective. The holistic view of the problem allows for richer learning from data and, thereby, improves decision making.

The first topic of this dissertation is the prediction of several target attributes using a common set of predictor attributes. In a holistic learning approach, the relationships between target attributes are embedded into the learning algorithm created in this dissertation. Specifically, a novel tree based ensemble that leverages the relationships between target attributes towards constructing a diverse, yet strong, model is proposed. The method is justified through its connection to existing methods and experimental evaluations on synthetic and real data.

The second topic pertains to monitoring complex systems that are modeled as networks. Such systems present a rich set of attributes and relationships for which holistic learning is important. In social networks, for example, in addition to friendship ties, various attributes concerning the users' gender, age, topic of messages, time of messages, etc. are collected. A restricted form of monitoring fails to take the relationships of multiple attributes into account, whereas the holistic view embeds such relationships in the monitoring methods. The focus is on the difficult task to detect a change that might only impact a small subset of the network and only occur in a sub-region of the high-dimensional space of the network attributes. One contribution is a monitoring algorithm based on a network statistical model. Another contribution is a transactional model that transforms the task into an expedient structure for machine learning, along with a generalizable algorithm to monitor the attributed network. A learning step in this algorithm adapts to changes that may only be local to sub-regions (with a broader potential for other learning tasks). Diagnostic tools to interpret the change are provided. This robust, generalizable, holistic monitoring method is elaborated on synthetic and real networks.