Lisa Prosser, Ph.D., M.S.
Dr. Prosser’s research focuses on measuring the comparative effectiveness and cost-effectiveness of childhood health interventions using methods of decision sciences and economics. Research topics include evaluating long-term health and economic outcomes for newborn screening programs using simulation modeling, measuring public values for screening programs, and developing new methods for valuing family spillover effects of childhood illness. Her research on the economic impact of influenza vaccination has been used in setting national vaccine policy for children and for prioritizing subgroups in vaccine shortage years. Her studies using decision science modeling to project long-term health outcomes for proposed newborn screening programs have been used to inform national newborn screening policy decisions. Dr. Prosser directs the Child Health Evaluation And Research (CHEAR) Unit of the U-M Department of Pediatrics.
- Ph.D., Health Policy, Harvard University
- M.S., Technology & Policy, Massachusetts Institute of Technology
- M.S., Management, Massachusetts Institute of Technology
- B.A., Mathematics, Cornell University
Health Services Research & Policy Focus
Collaborating Centers & Programs
What are you thinking about?
As the demand for comparative effectiveness evidence grows, I have been thinking about how decision science methods can be used to provide information on the benefits and harms of clinical interventions and public health programs when data are scarce. My research focuses on child health, where it can be challenging to find data on both short-term and long-term outcomes. Through the use of modeling, we can predict outcomes for child health interventions into adulthood and throughout the lifespan. In collaboration with others, I also have been thinking about how to define patient-centered outcomes in the context of child health. In particular, we have been trying to innovatively capture the family spillover effects of having an ill child, which is key to fully valuing the benefits of child health interventions.
Why is this interesting to you?
The use of modeling provides a structured approach to decision making, often requiring explicit discussion of assumptions that may otherwise remain unexamined. This increased transparency in the decision-making process can lead to more informed decisions and increased consensus for clinical guidelines and public health programs. Understanding the range of possible outcomes and where the most uncertainty lies through the use of simulation modeling can lead to better decisions.
What are the practical implications for healthcare?
We have used decision models to support national policy recommendations for universal newborn screening. Decision analytic modeling can be used in many situations in which it is unethical or infeasible to conduct randomized trials. Through modeling, we can conduct “simulated clinical trials” to provide relevant information for national policy decisions and clinical guidelines. For evaluating child health interventions, I anticipate that we will see increased use of modeling to inform health care decision making, as well as new definitions of family-centered outcomes. With respect to outcomes, it’s exciting to consider new measures for valuing child health that go beyond the individual patient perspective.