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With the ongoing payment and delivery system reforms, the healthcare industry faces a major challenge to deliver value through improved patient outcomes and efficiency. My work addresses the challenge by studying operational excellence and value creation in the healthcare industry. I research operations practices and technologies that facilitate organizational learning and performance improvements and examine how organizations make performance tradeoffs in competitive markets over time. My publications apply econometric and analytical models to examine how resource management, market competition, and health IT contribute to value creation and performance improvements. Through working with healthcare providers such as the Mayo Clinic and the Cancer Institute of New Jersey (CINJ), my scholarship advances our understanding of the mechanism behind operational excellence and factors leading to the Triple Aim of Health Care.

Resource allocation and management
This stream of research examines how allocating resources to highlight specific clinical department(s) (i.e., focus) affects efficiency and clinical quality (Ding, 2014, 2015; Ding et al., 2020; Peng et al., 2023; Ting & Ding et al., working paper).

Market competition and performance tradeoffs
The second stream of research answers the question by examining how hospitals adapt to external market competition through adjusting internal operations and prioritizing key performance metrics (Ding, 2014; Ding et al., 2020; Ding, 2023).

Health IT and technology capabilities
The third stream of research examines how health IT capability, either captured by the maturity of electronic medical records (EMR) or reflected by aggregated clinical and administrative functionalities, addresses the complexity in clinical operations and hence improves clinical quality and process of care (Peng et al., 2020; Ding & Peng, 2022), These studies extend my longstanding research interests in technology adoption and experience management (Ding et al. 2010). They also generate ongoing research focusing on how technology readiness drives usage (Ding, 2022)  and how digital service technology interacts with AI and service robots to drive cost-effective service excellence (Wirtz et al. 2023, SIJ).

Methodological contributions and industry collaboration
My editorial work as an Associate Editor for JOM and DSJ inspired a few projects examining various issues with research design, including potential endogeneity due to omitting variables and reverse causality (Lu & Ding et al., 2018). The paper has been cited in at least 130 publications and was featured in JOM editorials. In another work with Goswami, Baveja, Roberts, and Melamed, we propose a new framework to study the pharmaceutical supply chain by combining qualitative inputs from case studies and quantitative outputs from discrete-event simulations. My work with physicians and executives in the healthcare industry also led to a funded project with the Cancer Institute of New Jersey and a manuscript accepted for publication with Business and Management Review (Francis, Baveja, Ding, Bagchi, Melamed, 2022).

Future research

My ongoing research builds on the above work to examine factors driving performance improvements, including cost-quality tradeoffs and government regulations in the healthcare industry. In addition, I am interested in exploring the connection between health inequities and operational characteristics such as resource utilization. This line of research examines whether crowding in hospitals negatively affects patient outcomes and exacerbates health inequities. With inpatient data from both national and state health agencies, I plan to study whether access to care is more likely to deteriorate for black patients with decreasing resource availability. I am also interested in exploring the use of artificial intelligence in healthcare and workforce settings. My long-term goal is to study what factors incentivize care providers to adopt AI in both clinical decision-making and patient scheduling, as well as how to address ethical challenges and the transparency paradox of AI.