Precision Medicine Treatment (PreEMT) Model
Study PI: Ann Wu, MD MPH
Our research goal is to develop and apply a detailed simulation we are calling the Precision Medicine Treatment (PreEMT) Model, a sophisticated computer model capable of simulating short- and long-term clinical benefits and estimating the cost-effectiveness of integrating different genome screening strategies into clinical care for healthy or high-risk newborns for a wide variety of heritable conditions. With this model, we will synthesize the best available clinical, epidemiologic, and economic data on genetic variants present at birth for a wide variety of genetically-driven childhood conditions to project health outcomes, and provide a dynamic tool to evaluate evolving knowledge in the area of genomics and precision medicine in the United States over the next decade.
Implementing Universal Lynch Syndrome Screening Across Multiple Healthcare Systems: Identifying Strategies To Facilitate And Maintain Programs In Different Organizational Contexts
HPHCI Site PI: Christine Lu, PhD
Study PI: Alanna Rahm, PhD (Geisinger Clinic)
The overarching goal of this project is to create an organization-level toolkit for implementing, maintaining and improving Lynch syndrome (LS) screening by using tools from implementation science to describe, explain, and compare decision making and other variations in LS screening implementation across multiple healthcare systems. We will accomplish this through analyzing variation in LS screening implementation across diverse healthcare systems, estimating costs of different protocols by healthcare system, synthesizing this information into an organizational implementation toolkit, and testing the toolkit within the healthcare systems. This model will enable more effective and efficient implementation of LS screening; ultimately preventing needless suffering of patients and their family members from preventable cancers, decreasing waste in healthcare system costs, and informing strategies to facilitate the promise of precision medicine.
Age-Dependent Pharmacogenomics of Asthma Treatment (ADAPT)
PI: Ann Wu
Applications of the genetic knowledge resulting from the Human Genome Project are not yet available for asthma despite the immense potential that pharmacogenomics demonstrates for improving asthma care. This research will link genetic variants to therapeutic responses with additional information from genomics and metabolomics to provide insight into the biologic pathways that may be activated in an age-dependent method. This study will elucidate response to the two most commonly used medications for asthma, inhaled steroids and β2-agonists. This research employs existing genetic, genomic, and metabolomics data from clinical trial and real-life populations. The ability to link genetic variants to the therapeutic responses with additional information from genomics and metabolomics will provide insight into the biologic pathways that may be activated. By integrating and accounting for the interplay between genetics, genomics, and metabolomics, we will develop a comprehensive signature that predicts response to inhaled steroids and β2-agonists. Knowledge gained from this research will advance the field of personalized medicine for pediatric asthma.
The Age-Dependent Pharmacogenomics of Asthma Treatment (ADAPT) Team:
Kelan Tantisira, Joanne Sordillo, Rachel Kelly, Ann Wu, Jessica Lasky-Su, Mike McGeachie, Amber Dahlin
Genomics-based Technologies: Access and Reimbursement Issues
PI's: Christine Lu, Ann Wu
Genomics-based health care is complex, rapidly evolving, and highly relevant to public health because of its potential use in assessing risk, diagnosing disease, and developing treatment plans. Access to genomic tests often depends on cost and coverage of services by the health plan. Drs. Lu and Wu are leading the current investigation to systematically examine access and reimbursement issues relating to guideline-recommended pharmacogenomic tests and implications of barriers to access and/or differential access for patients, providers, and society. Understanding access barriers in current practice and decision-making processes will allow policy makers to develop coverage policies to optimize affordable and equitable access to guideline-recommended genomics-based technologies in order to improve population health.