Customizing Value-Based Methods to Prioritize Implementation of Pharmacogenomic Clinical Decision Support for Learning Health Systems (Washington)

Project Details - Ongoing

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Summary:

Precision medicine is an emerging area in which disease treatment and prevention are tailored to the individual level, taking into consideration an individual’s genetics, environment, and lifestyle. Pharmacogenomics (PGx) uses information about a person’s genetics to guide drug therapy. The adoption of PGx by healthcare systems, including learning health systems (LHSs), has been slow due to many existing barriers, including the need for more evidence for clinical utility around drug-PGx pairs, the specific PGx biomarkers to test for, availability of test results across organizations, incorporation of PGx information into current clinical workflow, and a lack of training for many clinicians to be able to interpret genomic test results. The use of clinical decision support (CDS) alerts may help to overcome some of these barriers.

This research will estimate the cost-effectiveness of PGx CDS alerts and create a tool that provides estimates of the value of developing and implementing them. This will assist those in LHSs to make informed decisions about the implementation of PGx-CDS alerts specific to their populations that consider trade-offs between the cost of implementation and the potential clinical benefits to patients.

The specific aims of this research are as follows:

  • Estimate the cost-effectiveness of PGx-CDS alerts, versus no alerts, on adverse drug events (ADEs) outcomes. 
  • Create a web-based, interactive, publicly available tool that provides estimates of the value of developing and implementing PGx-CDS alerts, customized to each LHS. 

A genotyping panel including 12 of the most commonly tested, clinically actionable pharmacogenes will be modeled. Each of the 12 is associated with one or more drugs across nine disease areas—including depression, cardiovascular disease, pain, gastroesophageal reflux disease, and schizophrenia—representing 18 drug classes and 26 drugs, constituting 30 gene-drug pairs. Each of these pairs can cause one or more ADEs and thus would be targeted for a PGx-CDS alert. Investigators will use decision modeling to create a framework for estimating the value of these PGx-CDS alerts. They will then adapt this framework to an online platform, creating a publicly available, web-based tool that will enable customized estimates of the value of PGx-CDS alerts based on the population within a given LHS. Finally, investigators will pilot and improve the tool by collaborating with stakeholder-colleagues in LHSs.

Ultimately, the investigators hope that this study will provide an innovative, evidence-based information technology solution to manage population health and improve quality and outcomes within LHSs in a way that makes the solution configurable across disparate LHSs. In addition, the study results should provide a platform to share and analyze practice data in a way that makes knowledge learned actionable and shareable, including tailoring messages to decision makers.

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