What am I interested in?


Algorithmic Decision Making

The pervasiveness of data promises significant opportunities to improve decision-making by using algorithms, analytical tools that use available data and produce an output (such as a prediction or a recommendation). In healthcare, transforming care into a reliable and evidence-based undertaking with a focus on data-driven decision-making has been a significant motivation. My research efforts in algorithmic decision-making are based on this motivation and focus on critical medical decisions that physicians need to make in their day-to-day encounters with patients. I focus on two inter-related streams of research. The first is medical decision-making, in which I develop optimization models and calibrate them using real data to improve critical decisions made by medical professionals. The prescriptions of the models inform macro-level policy choices for such decisions. The second substream is value creation and pitfalls, in which I address (i) how and when predictive algorithms create value and (ii) the issues associated with the algorithmic approach to decision-making.

Economics of Information/IT

My work on the economics of information and health IT focuses on the value and impact of various technologies such as health information exchanges and designing incentive mechanisms to induce meaningful use of those technologies. The motivation is that the use of information technologies can help solve some problems while also creating others–often referred to as digital vulnerabilities. For example, while offering several benefits such as increased transparency and better decision making, increased information visibility can also enable or exacerbate concerns such as privacy issues, intellectual property theft, and behavioral biases. The systematic resistance to information sharing across healthcare organizations (i.e., through HIEs) is another example. The coopetitive nature of HIEs and the agency problems inherent to healthcare present a great opportunity for further research into understanding the relationships amongst stakeholders including patients, providers, payers, and policymakers

Medical Informatics

I primarily use various data mining techniques and information theory in my medical informatics research. In particular, predictive modeling for estimating disease risk, the value of information for diagnosing diseases, and natural language processing for processing textual data are areas that are very important for the current health environment. Some of these efforts also constitute the preliminary step of parameter estimation in numerically validating the optimization models. I have published this line of research in clinical and informatics journals. Some of these research pieces require substantial domain knowledge. In this line of research, I am often the methods expert and also contribute to other aspects while doctors provide the contextual expertise as well as guidance in conducting the study.

Health Economics

Medical decision-making is the applied and theoretical analysis of clinical decisions using primarily the methods studied by the Decision Analysis field. The objective is to maximize the utility (e.g., quality-adjusted expected life or expected life) from an individual’s or society’s perspective. When individuals optimally prefer a strategy because of high effectiveness while societal perspective indicates another strategy from resources standpoint, which strategy is cost-effective becomes an important research question for health policy and medical decision-making. In my health economics work, I focus on cost-effectiveness or comparative effectiveness of medical decisions, to contribute to the understanding of this inefficiency.

Selected Work (My SSRN Page) (My ResearchGate Page)

Select Institutions of Research Collaborators