Sutton, Joseph

426 Background: Testing for DPYD gene variations prior to administration of fluoropyrimidines ([FP] e.g. 5-FU or capecitabine) can determine if patients are at risk of significant toxicity and may benefit from dose reduction or elimination of chemotherapy. Barriers to testing include lack of recommendations in US guidelines for routine DPYD testing, absence of insurance coverage, and limitations in provider support. We designed an electronic health record (EHR)-based intervention to support shared decision-making (SDM) between providers and patients regarding DPYD testing. Methods: We built an EHR alert identifying all patients initiating FP without DPYD testing. The alert fired to remind providers upon signing FP. Clinical decision support (CDS) was included in the alert to help with DPYD testing selection along with an educational note template containing the incidence of DPYD deficiency, rationale for testing, test costs, and turnaround time. Interactions with the EHR were recorded to determine why providers declined ordering pharmacogenomic testing. Testing rates and results for patients initiating FP were collected from the EHR. Finally, providers were surveyed to determine the impact of the alert on conversations with patients as well as the utility of the embedded CDS and educational note template. Results: Within the first 8 months after alert activation, 34 patients completed DPYD testing of 444 starting FP (testing rate of 7.7%) vs the baseline testing rate in the 3 months prior to alert go-live of 6.8% (p = 0.86 via chi-squared analysis, two-tailed). Of the 34 patients, 5 were intermediate DPYD metabolizers. Reasons for alert dismissal per provider selection were: “Patient already tolerated prior fluoropyrimidines” (15.7%), “Patient declines” (12.9%), “Will order separately” (5.7%), “Not discussed as not appropriate” (5.3%), and “Not discussed, deferred” (60.4%). Thirty-nine providers (n = 77 eligible) responded to the survey; 22 prescribed FP in their practice and had encountered the alert. 21 (95.5%) of these reported the alert was “Very Useful” or “Somewhat Useful” and 18 (81.8%) used the DPYD testing options available in the alert. Sixteen (72.7%) discussed DPYD testing more frequently with patients as a result of the alert. Barriers to DPYD testing were “Concerned about the impact of lowering FP dosing” and “Lack of knowledge about DPYD testing”, while “Cost of DPYD testing” was scored lowest. Conclusions: Utilizing an EHR alert with embedded CDS had a positive impact on provider awareness and led to more discussions regarding DPYD pharmacogenomic testing with patients in line with current FDA recommendations. Limitations in provider knowledge regarding DPYD testing and concerns about lowering chemotherapy doses posed barriers to ordering testing. Further interventions including additional education are required.

Pharmacogenomics (PGx) has the potential to improve prescribing practices. In 2012, our institution developed a multidisciplinary program to implement PGx-CDS to support the clinical practice. CDS has been implemented for 45 drug-gene interactions, in 42,000 patients with PGx tests results, and in 2023, approximately 9281 CDS interventions have triggered at the time of prescribing. The program has been successful.

Familial Hypercholesterolemia (FH) is underdiagnosed in the United States. Clinical decision support (CDS) could increase FH detection once implemented in clinical workflows. We deployed CDS for FH at an academic medical center and sought clinician insights using an implementation survey. In November 2020, the FH CDS was deployed in the electronic health record at all Mayo Clinic sites in two formats: a best practice advisory (BPA) and an in-basket alert. Over three months, 104 clinicians participated in the survey (response rate 11.1%). Most clinicians (81%) agreed that CDS implementation was a good option for identifying FH patients; 78% recognized the importance of implementing the tool in practice, and 72% agreed it would improve early diagnosis of FH. In comparing the two alert formats, clinicians found the in-basket alert more acceptable (p = 0.036) and more feasible (p = 0.042) than the BPA. Overall, clinicians favored implementing the FH CDS in clinical practice and provided feedback that led to iterative refinement of the tool. Such a tool can potentially increase FH detection and optimize patient management.

AbstractObjective Familial hypercholesterolemia (FH), a prevalent genomic disorder that increases risk of coronary heart disease, remains significantly underdiagnosed. Clinical decision support (CDS) tools have the potential to increase FH detection. We describe our experience in the development and implementation of a genomic CDS for FH at a large academic medical center.Methods CDS development and implementation were conducted in four phases: (1) development and validation of an algorithm to identify “possible FH”; (2) obtaining approvals from institutional committees to develop the CDS; (3) development of the initial prototype; and (4) use of an implementation science framework to evaluate the CDS.Results The timeline for this work was approximately 4 years; algorithm development and validation occurred from August 2018 to February 2020. During this 4-year period, we engaged with 15 stakeholder groups to build and integrate the CDS, including health care providers who gave feedback at each stage of development. During CDS implementation six main challenges were identified: (1) need for multiple institutional committee approvals; (2) need to align the CDS with institutional knowledge resources; (3) need to adapt the CDS to differing workflows; (4) lack of institutional guidelines for CDS implementation; (5) transition to a new institutional electronic health record (EHR) system; and (6) limitations of the EHR related to genomic medicine.Conclusion We identified multiple challenges in different domains while developing CDS for FH and integrating it with the EHR. The lessons learned herein may be helpful in streamlining the development and deployment of CDS to facilitate genomic medicine implementation.

Electronic health record (EHR)-based clinical decision support (CDS) can address the low awareness and undertreatment of familial hypercholesterolemia (FH), a disorder associated with a markedly increased risk of coronary heart disease. We aimed to incorporate provider perspectives into the development and implementation of a CDS tool for FH. An implementation science framework and a user-centered design process were used to create a CDS tool for FH. Primary care physicians and specialist physicians participated in qualitative interviews, usability testing and an implementation survey. The CDS was configured in two formats—a best practice alert (BPA) and an in-basket message and subsequently deployed in the EHR in silent mode. The key themes that emerged from the analysis of interview transcripts included understanding and awareness of FH, clinical workflow, physician preferences and value of CDS tools, perspectives on patient needs and values and dissemination and implementation. Recommendations related to usability included preferred CDS format and placement, content, timing and frequency, and level of alert urgency/prioritization. In response to the survey, 84.6% of physicians agreed that the CDS would improve early FH diagnosis and 92.3% agreed that it would help them identify and manage FH patients. Physician feedback led to iterative CDS refinement. In summary, we developed a CDS tool for FH using an implementation science framework and physician feedback. Initial deployment revealed a significant burden of FH and the potential for the CDS tool to have a large impact.

Abstract Pharmacogenomics (PGx) clinical decision support integrated into the electronic health record (EHR) has the potential to provide relevant knowledge to clinicians to enable individualized care. However, past experience implementing PGx clinical decision support into multiple EHR platforms has identified important clinical, procedural, and technical challenges. Commercial EHRs have been widely criticized for the lack of readiness to implement precision medicine. Herein, we share our experiences and lessons learned implementing new EHR functionality charting PGx phenotypes in a unique repository, genomic indicators, instead of using the problem or allergy list. The Gen-Ind has additional features including a brief description of the clinical impact, a hyperlink to the original laboratory report, and links to additional educational resources. The automatic generation of genomic indicators from interfaced PGx test results facilitates implementation and long-term maintenance of PGx data in the EHR and can be used as criteria for synchronous and asynchronous CDS.