Health Economics
Title: Building User-friendly Generic Decision Analytic Modeling Tools to Inform and Guide Decision-making of Lynch Syndrome Screening at Local Healthcare Systems (IMPULSS Supplement)
PI: Jing Hao, PhD, MD, MS, MPH (Parent R01 PI: Alanna Rahm, Geisinger)
Funder: NIH/NCI
Grant number: Supplement to R01 CA 211723
Dates: 8/1/2020 – 7/31/2022
The overarching goal of the parent grant – Implementing Universal Lynch Syndrome Screening (IMPULSS) across Multiple Healthcare Systems – is to create an organization-level toolkit for implementing, maintaining and improving Lynch syndrome (LS) screening using tools from implementation science to describe, explain, and compare decision making and other variations in LS screening implementation across multiple healthcare systems. Aim 3 of the IMPULSS project is to determine the relative effectiveness, efficiency, and costs of different implementation strategies/protocols by healthcare systems. Under Aim 3, the health economics team at Geisinger has developed decision analytic models that represent multiple LS screening protocols in colorectal cancer consistent with current evidence and guidelines. There is consensus among the IMPULSS sites that developing a user-friendly generic modeling tool allowing non-economist end-users from individual sites to interact with and answer specific clinical questions relevant to their sites would be much valuable to support local decision-making of LS screening implementation. Thus, the purpose of this proposed supplement is to develop two user-friendly generic modeling tools for colorectal cancer and endometrial cancer populations, respectively, based on already developed conventional decision analytic models to inform and guide decision-making of Lynch syndrome screening at local healthcare systems.
Title: Surveillance for Outcomes of Genomic Medicine Policies
Site-PI: Jing Hao, PhD, MD, MS, MPH (PI: Chris Lu, Harvard Pilgrim)
Funder: NIH/NHGRI
Grant number: 1R35HG011285-01
Dates: 09/01/2020-06/30/2025
There are currently no surveillance structures in place to examine outcomes associated with policies for genomic tests. Insurance coverage policies and levels of patient cost-sharing are major factors that influence patient access to new genomic tests by determining who gets tested, screened, and ultimately treated based on genomic information. These coverage determinations can have significant impact on the health of our patients and their access to potentially lifesaving medical care. Currently, we know little about the effects of policies for genomic tests despite the increasing implementation of such policies. Monitoring the consequences of policies is an important public health issue. This project seeks to advance the field of genomic medicine and society by developing and validating analytical methods for efficient, rigorous evaluation of policies that impact access to genomic tests and associated outcomes to inform healthcare and policy decisions.