Using Behavioral Economics and Implementation Science to Advance the Use of Genomic Medicine Utilizing an EHR Infrastructure Across a Diverse Health System

Overview: Using key stakeholder engagement, this study will refine clinician- and patient-directed nudges designed to change the status quo bias that too often is relied upon within the complexity of medical care and decision-making, which reduces the likelihood that genetic testing will be used in situations where it will change medical management. The investigatorswill define algorithms to identify patients eligible for genetic testing (Aim 1); conduct a hybrid type 3 cluster-randomized implementation trial to evaluate optimized patient- and/or clinician-directed nudges for increasing the use of genetic testing to inform medical management (Aim 2); and engage in dissemination activities to increase the capacity of other medical settings to adopt both our EHR-based infrastructure and the implementation strategies designed and evaluated in this trial (Aim 3).

For Aim 1, our Stakeholder Advisory Council will design the nudges to "de-risk" and optimize implementation strategies by ensuring that the perspectives of end-users are included initially. In Aim 2, the investigators will test our optimized nudges in a six-arm hybrid type 3 pragmatic cluster randomized controlled trial (RCT) to evaluate the effectiveness of nudges to clinicians (referral vs. ordering), nudges to patients, or nudges to both for increasing genetic testing, vs. generic clinician Best Practice Alert and no nudge. The trial will include 230 clinicians, who will be the unit of randomization, with randomization performed on "clusters" of clinicians to control for the potential of contamination that can arise when clinicians who work closely together are randomized to different arms of the trial. Once the clusters are randomized by specialty, the trial will follow at least 16,500 patients over 3 years, monitoring fidelity of nudge delivery, use of genetic testing, and secondary implementation outcomes. Patient, clinician, and system factors will be assessed as moderators and an effectiveness outcome will be examined.

In Aim 3, Both EHR-based algorithms already established through the PennChart Genomics Initiative, for which the investigators have received multiple requests, and those developed through this application, will be shared through PheKB, ANVIL, and Epic Community Library.

Aim 1: To develop clinician- and patient-directed nudges, informed by behavioral economic theory, within the EHR that will address the barriers to specialist clinician genetic testing of patients in whom it will change medical management, develop clinician- and patient-directed informational websites, and refine EHR algorithms to identify patients who are good candidates for genetic testing. Procedures for refining systems to identify patients for genetic testing: Although there is a growing number of conditions for which genetic testing is indicated where the results will change medical management for patients, between 0-90% of patients have genetic testing. Changing this first depends on the use of electronic phenotyping algorithms to identify eligible patients. Development of electronic phenotype algorithms involves the integration of ICD-10 diagnosis codes, clinical lab measurements, vital signs, medications, procedures, and clinical notes into rule-based algorithms to identify individuals who can be classified with a diagnosis for a given disease phenotype. The phenotype knowledgebase (PheKB) is a collaborative environment to build, validate, and share these electronic phenotype algorithms. To perform our RCT, the investigators first need to identify patients who would benefit from genetic testing. The investigators will work closely with the clinical experts in neurogenetics, cardiac, and medical genetics to identify the clinical features from the EHR for defining disease diagnosis for each condition, including two prior encounters for the diagnosis with first within a year. The investigators will deploy the algorithm and then perform manual review of 100 charts to estimate the positive predictive value (PPV) of the algorithm; this is the proportion of patients defined as cases by the algorithm who have the condition. The investigators will also perform manual review of 100 charts for individuals who have a diagnosis code in the EHR from two different encounters for one of the study conditions but are not identified as cases based on the electronic phenotyping algorithm. This review will give us an estimate of how much specificity the investigators gain with the electronic phenotype algorithm above and beyond the diagnosis codes alone. As the patients identified by the algorithm will be enrolled in the clinical trial, the investigators will aim for 100% PPV. Each patient identified by the phenotype algorithms will be added to the Diagnosis-specific Epic Registry and the SQL database. The algorithms will be disseminated through Aim 3.

Procedures for nudge design: To develop a sustainable EHR-based infrastructure, supporting the provision of genomic medicine and inclusion of specialty clinicians, equally valuable for other groups nationally, the investigators must consider multiple perspectives on how our nudges - content, design, and mode of delivery - will impact institutions, payors, clinicians, and patients. Thus, the investigators have formed a Stakeholder Advisory Council with diverse representation from genetics and non-genetics clinicians, informaticians, payors, testing companies, legal experts and ethicists, and patient groups and community representation, with expertise regarding health disparities and equity. The group will support the development of and wording in the nudges, genetics and disease-specific website education for patients and clinicians, and ease of use of the EHR-based infrastructure for genetic test ordering, results, and referral.