Improving Effectiveness and Equity of Healthcare Delivery through Systems Optimization

Abstract

Effective and equitable healthcare delivery is crucial for advancing health outcomes, reducing resource waste, alleviating healthcare disparities, and improving overall individual well-being and community welfare. With increasing costs, limited resources, growing demand for patient-centered services, and advancements in remote technology, resource allocation has gained significant attention as a key strategy to optimize care delivery. The objective of this dissertation is to improve the effectiveness and equity of healthcare services through developing decision-analytic, machine learning, and optimization models using patient-level data, with a focus on both remote and in-person healthcare settings. In remote care settings, we explored how technologies could enhance healthcare resource utilization for chronic disease management. Remote monitoring has emerged as a promising option with high personalization and adaptability. However, the cost-effectiveness of these technologies remained uncertain. We used chronic depression as a case study and evaluated the cost-effectiveness of remote monitoring strategies compared to rule-based follow-up and fixed-frequency follow-up strategies. We developed a decision-analytic Markov-cohort model to simulate disease progression for patients with different risks, incorporating optimal treatment switching. Results showed that remote monitoring technology can be cost-effective and identified requirements for it to work more effectively. It provided a novel assessment framework that can guide the development of emerging technologies and highlighted the bright future of improving care delivery through remote monitoring. In in-person care settings, we aimed to optimize trauma care delivery, given its critical role in emergency healthcare. We began by investigating the variability in care delivery within statewide trauma systems. Hospitals are designated as trauma centers (TCs) with level I-V, or non-trauma centers (non-TCs), based on their medical and research resources. To explore trauma care delivery patterns and their association with trauma designation levels, we performed three sets of unsupervised clustering analyses on statewide TCs and non-TCs based on hospital features with a focus on surgical care. We found that the resulting clusters only partially aligned with the TC designations, implying not all hospitals with the same TC level provide equivalent care. The results highlight the performance variability and help us better understand trauma system functioning, guiding the subsequent study to optimize the trauma system at the hospital level. To optimize statewide trauma systems, we developed a systematic framework for improving care quality while addressing population equity. This objective is achieved by establishing and assigning hospital profiles representing performance targets which can be used to guide resource allocation and operational adjustment decisions. While many studies have focused on optimizing emergency transport services, care quality and equity have often been overlooked. Using state data, we established a set of comprehensive trauma care quality metrics for distinct population groups formed by sociodemographic factors and Injury Severity Score (ISS). We then created a quality index to represent trauma care quality accounting for hospital variations using a Principal Component Analysis (PCA) analysis of the quality metrics. Next, we created hospital profiles using a quality index of each population group, which were estimated from data and imputed using a linear mixed-effects model. We formulated a mixed-integer linear program (MILP) to maximize the quality index of targeted population groups under various equity objectives. The model identified optimal hospital profile assignments as proxies for performance targets for the hospitals. These results help identify necessary resources for performance enhancement, guiding hospitals in making targeted improvements to better serve diverse patient populations. Overall, this dissertation advances healthcare effectiveness and equity by evaluating remote care technologies, uncovering variability in trauma systems, and establishing optimal performance targets for hospital trauma care delivery. Our findings offer actionable guidelines to enhance chronic disease management in remote settings and improve the quality and equity of statewide acute trauma care systems.