Research

Health information exchanges (HIEs) are expected to improve information coordination in emergency departments (EDs), but their impact on ED operations remains poorly understood. We study the effect of HIE on ED length of stay (LOS) based on about 7.4 million ED visits in Massachusetts. We find that HIE a) reduces ED LOS overall by 10.2%, b) is even more effective in teaching hospitals, c) is less effective in crowded EDs, and d) its effectiveness depends on severity and complexity of the patients. Our findings have implications for the nationwide HIE adoption.

What is the optimal design of a classification algorithm that accounts for human bias in input data and what is its value? In the context of breast cancer diagnosis, clinical-risk information of a patient could bias a radiologist's assessment of the mammogram for that patient. A bias-aware classification algorithm has the potential to be of significant value if integrated into a clinical decision support system (CDSS) used by the referring physician, who decide on the final course of action based on clinical-risk information and the radiologist's assessment.

Predictive algorithms have an increasingly important role in supporting the day-to-day operations of businesses. This paper studies how and when algorithm-enabled process innovation (AEPI) creates value, particularly in the context of changing operational environments and the behavioral responses to algorithmic predictions. We contextualize the broad research problem into the implementation of an alert system in a hospital for early identification and treatment of sepsis. We leverage a rich set of clinical and nonclinical data and econometric approaches to examine the relationship between sepsis AEPI and patient mortality.

Diffusion of health information outside the boundaries of an organization through health-information exchanges (HIEs) is a critical step in achieving socially desirable care outcomes. In this paper, we study the relationships among a payment model, providers' incentives to adopt HIEs, and the value of HIEs using a game-theoretical model. We demonstrate that assessing the value of HIEs in isolation from the underlying payment mechanism and patient switching behavior may result in under- or overestimation of the HIE value. Therefore, as payment models evolve over time, there is a real need to reevaluate the value of HIE adoption and the government policies that induce providers to adopt HIE.

New compensation models for transplant professionals must (1) justify payments for nonbillable work related to transplant activity/procedures; (2) capture the entire academic, clinical, and relationship‐building work effort as part of RVU determination; and (3) move toward a value‐based compensation scheme that aligns the incentives for physicians, surgeons, transplant center, payers, and patients. In this review, we provide an example of redesigning RVUs to address these challenges in compensating transplant physicians and surgeons.

Decision models representing the clinical situations where treatment options entail a significant risk of morbidity or mortality should consider the variations in risk preferences of individuals. In this study, we develop a stochastic modeling framework that optimizes risk-sensitive diagnostic decisions after a mammography exam. Using a risk-sensitive finite-horizon MDP model, we provide medical/policy insights. We empirically validate our model and demonstrate that the current medical practice of breast biopsies resemble risk-seeking behavior.

There is a growing interest in better understanding the new vulnerabilities due to the adoption and the use of information technology (IT). We examine one such vulnerability, viz., the heightened litigation risk due to information sharing enabled by IT, in a typical health care context. Using a game-theoretic model, we find that socially optimal policies may require underprovisioning of health care in the presence of health IT. Limits on malpractice damage can alleviate the underprovisioning of health care.

We develop a finite-horizon discrete-time constrained Markov decision process (MDP) to model diagnostic decisions after mammography where we maximize the total expected quality-adjusted life years (QALYs) of a patient under resource constraints. We use clinical data to estimate the parameters of the MDP model and solve it as a mixed-integer program. By repeating optimization for a sequence of budget levels, we calculate incremental cost-effectiveness ratios attributable to consecutive levels of funding and compare actual clinical practice with optimal decisions.