Our expert answers 3 Questions
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.
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.
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.