Projects

Our ongoing projects focus on the impact of alternative payment models on the costs and quality of health care; financial and non-financial incentive design, including behavioral economics, to drive clinician practice change; and predictive analytics and informatics to improve health care delivery. Our current areas of focus include bundled payments, large scale re-design of physician and hospital payment models, optimal use of performance feedback in health care (e.g., to decrease opioid prescribing), and design of predictive models to drive behavior change. Click the various categories below to discover current projects in The Parity Center’s portfolio.

payment Payment Physicians and hospitals face a variety of economic forces in highly regulated markets, though prices generally cannot respond rapidly to market forces. How physicians and hospitals respond to the complex incentives that potentially uncouple the link between quality and price could provide important lessons for designing policies to improve value-based care at the system and federal levels. Our technical expertise is focused on the use of claims and clinical data to measure costs, quality and appropriateness of health care; this research provides us an up-close view of the complexities of health care financing and the resulting hardships.
policy Policy We design and implement projects that are data driven, policy-focused and policy-impactful. Our team brings together active research groups, both within Penn and external to Penn, with extensive experience on alternative payment model (APM) evaluations, surgical outcomes, health disparities, policy impacts on disparities in access to and outcomes of surgery, and organizational and provider impacts of APMs. Our goal is to make inferences on clinical care delivery that are highly relevant to analysis of health system performance and policy design.
practice Practice and Behavioral Economics The field of behavioral economics (BE) has established that individuals make decisions in predictably irrational ways. While BE principles have been tested extensively to improve patient behaviors, our team extends applications to clinician and hospital financial and non-financial incentive design. Our portfolio of work includes a mix of pragmatic policy trials and quasi-experimental analyses with the goal of informing clinician practice change.
prediction Prediction Informatics techniques using clinical data and advanced analytic methods demonstrate significant promise in answering health services questions. There is also great promise in applying machine learning methods to improve clinical practice. However, there are significant challenges in harmonizing the different types of data and harnessing the information throughout the electronic medical record. Part of our diverse portfolio is dedicated to combining statistical and machine learning techniques to analyze claims data and data from electronic hospital systems to improve clinical practices and patient outcomes.