Given the expansion of indications for genetic testing and our understanding of conditions for which the results change medical management, it is imperative to consider novel ways to deliver care beyond the traditional genetic counseling visit, which are both amenable to large-scale implementation and sustainable. The investigators propose an entirely new approach for the implementation of genomic medicine, supported by the leadership of Penn Medicine, investigating the use of non-geneticist clinician and patient nudges in the delivery of genomic medicine through a pragmatic randomized clinical trial, addressing NHGRI priorities. Our application is highly conceptually and technically innovative, building upon expertise and infrastructure already in place.
Innovative qualities of our proposal include: 1) Cutting edge EHR infrastructure already built to support genomic medicine (e.g., partnering with multiple commercial genetic testing laboratories for direct test ordering and results reporting in the EHR); 2) Automated EHR-based direct ordering or referring by specialist clinicians (i.e., use of replicable modules that enable specialist clinicians to order genetic testing through Epic Smartsets, including all needed components, such as populated gene lists, smartphrases, genetic testing, informational websites and acknowledgement e-forms for patient signature); 3) EHR algorithms for accurate patient identification (i.e., electronic phenotype algorithms to identify eligible patients, none of which currently have phenotype algorithms present in PheKB; 4) Behavioral economics-informed implementation science methods: This trial will be the first to evaluate implementation strategies informed by behavioral economics, directed at clinicians and/or patients, for increasing the use of genetic testing; further it will be the first study in this area to test two forms of defaults as a potential local adaptation to facilitate implementation (ordering vs. referring); and 5) Dissemination: In addition to standard dissemination modalities,PheKB95, GitHub and Epic Community Library, the investigators propose to disseminate via AnVIL (NHGRI's Genomic Data Science Analysis, Visualization, and Informatics Lab-Space). Our results will represent an entirely new paradigm for the provision of genomic medicine for patients in whom the results of genetic testing change medical management.
18 years of age or older
diagnosed with one of the study conditions
Under 18 years of age
not diagnosed with one of the study conditions
Factorial
Behavioral nudge
BehavioralComparison of genetic rates among individuals for whom it may make a difference in their care across study arms.
6
Treatment groupsSee each treatment group below.
Rate of Genetic Testing
The number of patients seen by the total number of genetic tests conducted
Rate of Genetic Test Orders/Referrals
The number of pended orders or referrals for genetic testing, completed or not
Rate of Patient Engagement
Frequency of patient logins to the genetic testing information websites and the duration of each visit
Rate of Pathogenic variants and VUS
These will be stratified by ancestry; comparison of effect of nudges by baseline genetic testing rates, and, as best assessed, rates of insurance coverage
Rate of Clinician actions
At six months post-results, we will measure whether patients with pathogenic variants have clinician action, specifically orders placed
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