Pain and Opioid use Following Total Knee Arthroplasty: Psycho-Social Factors are More Predictive Than Pharmacogenomics.

BACKGROUND: This prospective, observational study was designed to assess the phenotype variation of the genes associated with pain and opioid use following total knee arthroplasty (TKA) in comparison to psycho-social elements. METHODS: Preoperative demographic data and Patient-Reported Outcomes Measurement Information System-43 scores were obtained on 305 elective TKA patients. Patient visual analog scale pain scores and opioid use were extracted from the hospital record. Following discharge, participants completed a daily log of visual analog scale pain score, and medications used over 30 days. Pharmacogenomic testing was performed for three genes, CYP2D6, COMT, and OPRM1, which are involved in the opioid pathway and pain modulation. RESULTS: Other than increased pain seen in the COMT high activity group while in the hospital, none of the phenotype variations of the three genes were significantly associated with the participants' pain or opioid use. The Patient-Reported Outcomes Measurement Information System-43 domains of pain interference and anxiety were significantly associated with pain and opioid use using multiple logistic regression. CONCLUSIONS: Pharmacogenomic testing in this study was not predictive of pain and opioid use following TKA compared with psycho-social variables.

Personalized medicine in a community health system: the NorthShore experience.

Genomic and personalized medicine implementation efforts have largely centered on specialty care in tertiary health systems. There are few examples of fully integrated care systems that span the healthcare continuum. In 2014, NorthShore University HealthSystem launched the Center for Personalized Medicine to catalyze the delivery of personalized medicine. Successful implementation required the development of a scalable family history collection tool, the Genetic and Wellness Assessment (GWA) and Breast Health Assessment (BHA) tools; integrated pharmacogenomics programming; educational programming; electronic medical record integration; and robust clinical decision support tools. To date, more than 225,000 patients have been screened for increased hereditary conditions, such as cancer risk, through these tools in primary care. More than 35,000 patients completed clinical genetic testing following GWA or BHA completion. An innovative program trained more than 100 primary care providers in genomic medicine, activated with clinical decision support and access to patient genetic counseling services and digital healthcare tools. The development of a novel bioinformatics platform (FLYPE) enabled the incorporation of genomics data into electronic medical records. To date, over 4,000 patients have been identified to have a pathogenic or likely pathogenic variant in a gene with medical management implications. Over 33,000 patients have clinical pharmacogenomics data incorporated into the electronic health record supported by clinical decision support tools. This manuscript describes the evolution, strategy, and successful multispecialty partnerships aligned with health system leadership that enabled the implementation of a comprehensive personalized medicine program with measurable patient outcomes through a genomics-enabled learning health system model that utilizes implementation science frameworks.

PharmVar Tutorial on CYP2D6 Structural Variation Testing and Recommendations on Reporting.

The Pharmacogene Variation Consortium (PharmVar) provides nomenclature for the highly polymorphic human CYP2D6 gene locus and a comprehensive summary of structural variation. CYP2D6 contributes to the metabolism of numerous drugs and, thus, genetic variation in its gene impacts drug efficacy and safety. To accurately predict a patient's CYP2D6 phenotype, testing must include structural variants including gene deletions, duplications, hybrid genes, and combinations thereof. This tutorial offers a comprehensive overview of CYP2D6 structural variation, terms, and definitions, a review of methods suitable for their detection and characterization, and practical examples to address the lack of standards to describe CYP2D6 structural variants or any other pharmacogene. This PharmVar tutorial offers practical guidance on how to detect the many, often complex, structural variants, as well as recommends terms and definitions for clinical and research reporting. Uniform reporting is not only essential for electronic health record-keeping but also for accurate translation of a patient's genotype into phenotype which is typically utilized to guide drug therapy.

Utility of Polygenic Scores for Differentiating Diabetes Diagnosis Among Patients With Atypical Phenotypes of Diabetes.

CONTEXT: Misclassification of diabetes type occurs in people with atypical presentations of type 1 diabetes (T1D) or type 2 diabetes (T2D). Although current clinical guidelines suggest clinical variables and treatment response as ways to help differentiate diabetes type, they remain insufficient for people with atypical presentations. OBJECTIVE: This work aimed to assess the clinical utility of 2 polygenic scores (PGSs) in differentiating between T1D and T2D. METHODS: Patients diagnosed with diabetes in the UK Biobank were studied (N = 41 787), including 464 (1%) and 15 923 (38%) who met the criteria for classic T1D and T2D, respectively, and 25 400 (61%) atypical diabetes. The validity of 2 published PGSs for T1D (PGST1D) and T2D (PGST2D) in differentiating classic T1D or T2D was assessed using C statistic. The utility of genetic probability for T1D based on PGSs (GenProb-T1D) was evaluated in atypical diabetes patients. RESULTS: The joint performance of PGST1D and PGST2D for differentiating classic T1D or T2D was outstanding (C statistic = 0.91), significantly higher than that of PGST1D alone (0.88) and PGST2D alone (0.70), both P less than .001. Using an optimal cutoff of GenProb-T1D, 23% of patients with atypical diabetes had a higher probability of T1D and its validity was independently supported by clinical presentations that are characteristic of T1D. CONCLUSION: PGST1D and PGST2D can be used to discriminate classic T1D and T2D and have potential clinical utility for differentiating these 2 types of diseases among patients with atypical diabetes.

Cancer-associated thrombosis by cancer sites and inherited factors in a prospective population-based cohort.

Cancer-associated thrombosis (CAT) is common and associated with mortality. We estimated CAT rate by cancer sites and inherited factors among cancer patients from the UK Biobank (N =70,406). The 12-month CAT rate after cancer diagnosis was 2.37% overall but varied considerably among cancer sites. Among the 10 cancer sites classified as 'high-risk' of CAT by the National Comprehensive Cancer Network guidelines, 6 had CAT rate <5%. In contrast, 5 cancer sites classified as 'average-risk' by the guidelines had CAT rate >5%. For inherited risk factors, both known mutation carriers in two genes (F5/F2) and polygenic score for venous thromboembolism (VTE) (PGSVTE) were independently associated with increased CAT risk. While F5/F2 identified 6% patients with high genetic-risk for CAT, adding PGSVTE identified 13 % patients at equivalent/higher genetic-risk to CAT than that of F5/F2 mutations. Findings from this large prospective study, if confirmed, provide critical data to update guidelines for CAT risk assessment.

Pharmacogenomic Clinical Decision Support: A Scoping Review.

Clinical decision support (CDS) is often cited as an essential part of pharmacogenomics (PGx) implementations. A multitude of strategies are available; however, it is unclear which strategies are effective and which metrics are used to quantify clinical utility. The objective of this scoping review was to aggregate previous studies into a cohesive depiction of the current state of PGx CDS implementations and identify areas for future research on PGx CDS. Articles were included if they (i) described electronic CDS tools for PGx and (ii) reported metrics related to PGx CDS. Twenty of 3,449 articles were included and provided data on PGx CDS metrics from 15 institutions, with 93% of programs located at academic medical centers. The most common tools in CDS implementations were interruptive post-test alerts. Metrics for clinical response and alert response ranged from 12-73% and 21-98%, respectively. Few data were found on changes in metrics over time and measures that drove the evolution of CDS systems. Relatively few data were available regarding support of optimal approaches for PGx CDS. Post-test alerts were the most widely studied approach, and their effectiveness varied greatly. Further research on the usability, effectiveness, and optimization of CDS tools is needed.

Pharmacogenomics of medications given via nonconventional administration routes: a scoping review.

Pharmacogenomics (PGx) implementation has become increasingly widespread. One of the most important aspects of this implementation process is the development of appropriate clinical decision support (CDS). Major PGx resources, such as the Clinical Pharmacogenetics Implementation Consortium, provide valuable recommendations for the development of CDS for specific gene-drug pairs but do not specify whether the administration route of a drug is clinically relevant. It is also unknown if PGx alerts for nonorally and non-intravenously administered PGx-relevant medications should be suppressed to reduce alert fatigue. The purpose of this scoping review was to identify studies and their clinical, pharmacokinetic and pharmacodynamic outcomes to better determine if CDS alerts are relevant for nonorally and non-intravenously administered PGx-relevant medications. Although this scoping review identified multiple PGx studies, the results of these studies were inconsistent, and more evidence is needed regarding different routes of medication administration and PGx.

Pharmacogenomics (PGx) is the study of how a person's genes and DNA may impact their response to certain medications. There are many hospitals and large academic medical centers that have begun using pharmacogenomic testing to better help guide treatment decisions for patients. Currently, there are few standards in place to guide these institutions on how to put a patient's pharmacogenomic information into their personal medical chart. To help create these standards, a consensus is needed on what types of medication orders will alert physicians to patients who may have pharmacogenomic-related concerns. One area that must be addressed to help with these standardizations involves the route of administration of a medication. Do pharmacogenomic considerations depend on how a medication is given to the patient (e.g., by mouth or through a vein or muscle)? The purpose of this scoping review was to assess the evidence surrounding this question in the hopes of clarifying what types of medication orders should trigger alerts to physicians. If the administration route results in no concerns regarding a patient's pharmacogenomic data, then the physician should not be shown an alert for that medication order. Too many of these alerts may cause 'alert fatigue' and lead to more errors in medication ordering, which may result in patient harm. It is important to address this area of pharmacogenomics to ensure this information is being used appropriately for patient care.

Effect Modification by Social Determinants of Pharmacogenetic Medication Interactions on 90-Day Hospital Readmissions within an Integrated U.S. Healthcare System.

The present study builds on our prior work that demonstrated an association between pharmacogenetic interactions and 90-day readmission. In a substantially larger, more diverse study population of 19,999 adults tracked from 2010 through 2020 who underwent testing with a 13-gene pharmacogenetic panel, we included additional covariates to evaluate aggregate contribution of social determinants and medical comorbidity with the presence of identified gene-x-drug interactions to moderate 90-day hospital readmission (primary outcome). Univariate logistic regression analyses demonstrated that strongest associations with 90 day hospital readmissions were the number of medications prescribed within 30 days of a first hospital admission that had Clinical Pharmacogenomics Implementation Consortium (CPIC) guidance (CPIC medications) (5+ CPIC medications, odds ratio (OR) = 7.66, 95% confidence interval 5.45−10.77) (p < 0.0001), major comorbidities (5+ comorbidities, OR 3.36, 2.61−4.32) (p < 0.0001), age (65 + years, OR = 2.35, 1.77−3.12) (p < 0.0001), unemployment (OR = 2.19, 1.88−2.64) (p < 0.0001), Black/African-American race (OR 2.12, 1.47−3.07) (p < 0.0001), median household income (OR = 1.63, 1.03−2.58) (p = 0.035), male gender (OR = 1.47, 1.21−1.80) (p = 0.0001), and one or more gene-x-drug interaction (defined as a prescribed CPIC medication for a patient with a corresponding actionable pharmacogenetic variant) (OR = 1.41, 1.18−1.70). Health insurance was not associated with risk of 90-day readmission. Race, income, employment status, and gene-x-drug interactions were robust in a multivariable logistic regression model. The odds of 90-day readmission for patients with one or more identified gene-x-drug interactions after adjustment for these covariates was attenuated by 10% (OR = 1.31, 1.08−1.59) (p = 0.006). Although the interaction between race and gene-x-drug interactions was not statistically significant, White patients were more likely to have a gene-x-drug interaction (35.2%) than Black/African-American patients (25.9%) who were not readmitted (p < 0.0001). These results highlight the major contribution of social determinants and medical complexity to risk for hospital readmission, and that these determinants may modify the effect of gene-x-drug interactions on rehospitalization risk.

A clinician's guide for counseling patients on results of a multigene pharmacogenomic panel.

PURPOSE: This article explores approaches to pharmacogenomic counseling for patients who have undergone multigene panel testing by describing the collective experience of 5 institutions. SUMMARY: Multigene panel pharmacogenomic testing has the potential to unlock a myriad of information about a patient's past, present, and future drug response. The multifaceted nature of drug response coupled with the complexity of genetic results necessitates some form of patient education through pharmacogenomic counseling. Published literature regarding disclosure of pharmacogenomic test results is limited. This article compares the counseling practices of pharmacists from 5 different institutions with pharmacogenomics clinics whose experience represents perspectives ranging from academia to community clinical environments. Overarching counseling themes discussed during result disclosure center around (1) pharmacogenomic results, (2) gene-drug interactions, (3) gene-drug-drug interactions, (4) drug changes (5) future, familial, or disease-risk implications, (6) updates in the interpretation and application of pharmacogenomic results, (7) gauging patient comprehension, and (8) sharing results and supplemental information. CONCLUSION: Dedicating time to counseling patients on the results of a multigene pharmacogenomic panel is important given the lifelong applications of a test that is generally performed only once. The content and methods of disclosing test results shared by the experiences of pharmacists at 5 different institutions serve as guide to be further refined as research addresses effective communication strategies that enhance patient comprehension of pharmacogenomic results.

Pharmacogenomic Clinical Decision Support: A Review, How-to Guide, and Future Vision.

Clinical decision support (CDS) is an essential part of any pharmacogenomics (PGx) implementation. Increasingly, institutions have implemented CDS tools in the clinical setting to bring PGx data into patient care, and several have published their experiences with these implementations. However, barriers remain that limit the ability of some programs to create CDS tools to fit their PGx needs. Therefore, the purpose of this review is to summarize the types, functions, and limitations of PGx CDS currently in practice. Then, we provide an approachable step-by-step how-to guide with a case example to help implementers bring PGx to the front lines of care regardless of their setting. Particular focus is paid to the five "rights" of CDS as a core around designing PGx CDS tools. Finally, we conclude with a discussion of opportunities and areas of growth for PGx CDS.

The Contribution of Pharmacogenetic Drug Interactions to 90-Day Hospital Readmissions: Preliminary Results from a Real-World Healthcare System.

Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines exist for many medications commonly prescribed prior to hospital discharge, yet there are limited data regarding the contribution of gene-x-drug interactions to hospital readmissions. The present study evaluated the relationship between prescription of CPIC medications prescribed within 30 days of hospital admission and 90-day hospital readmission from 2010 to 2020 in a study population (N = 10,104) who underwent sequencing with a 14-gene pharmacogenetic panel. The presence of at least one pharmacogenetic indicator for a medication prescribed within 30 days of hospital admission was considered a gene-x-drug interaction. Multivariable logistic regression analyzed the association between one or more gene-x-drug interactions with 90-day readmission. There were 2211/2354 (93.9%) admitted patients who were prescribed at least one CPIC medication. Univariate analyses indicated that the presence of at least one identified gene-x-drug interaction increased the risk of 90-day readmission by more than 40% (OR = 1.42, 95% confidence interval (CI) 1.09-1.84) (p = 0.01). A multivariable model adjusting for age, race, sex, employment status, body mass index, and medical conditions slightly attenuated the effect (OR = 1.32, 95% CI 1.02-1.73) (p = 0.04). Our results suggest that the presence of one or more CPIC gene-x-drug interactions increases the risk of 90-day hospital readmission, even after adjustment for demographic and clinical risk factors.

Development and Implementation of In-House Pharmacogenomic Testing Program at a Major Academic Health System.

Pharmacogenomics (PGx) studies how a person's genes affect the response to medications and is quickly becoming a significant part of precision medicine. The clinical application of PGx principles has consistently been cited as a major opportunity for improving therapeutic outcomes. Several recent studies have demonstrated that most individuals (> 90%) harbor PGx variants that would be clinically actionable if prescribed a medication relevant to that gene. In multiple well-conducted studies, the results of PGx testing have been shown to guide therapy choice and dosing modifications which improve treatment efficacy and reduce the incidence of adverse drug reactions (ADRs). Although the value of PGx testing is evident, its successful implementation in a clinical setting presents a number of challenges to molecular diagnostic laboratories, healthcare systems, providers and patients. Different molecular methods can be applied to identify PGx variants and the design of the assay is therefore extremely important. Once the genotyping results are available the biggest technical challenge lies in turning this complex genetic information into phenotypes and actionable recommendations that a busy clinician can effectively utilize to provide better medical care, in a cost-effective, efficient and reliable manner. In this paper we describe a successful and highly collaborative implementation of the PGx testing program at the University of Minnesota and MHealth Fairview Molecular Diagnostic Laboratory and selected Pharmacies and Clinics. We offer detailed descriptions of the necessary components of the pharmacogenomic testing implementation, the development and technical validation of the in-house SNP based multiplex PCR based assay targeting 20 genes and 48 SNPs as well as a separate CYP2D6 copy number assay along with the process of PGx report design, results of the provider and pharmacists usability studies, and the development of the software tool for genotype-phenotype translation and gene-phenotype-drug CPIC-based recommendations. Finally, we outline the process of developing the clinical workflow that connects the providers with the PGx experts within the Molecular Diagnostic Laboratory and the Pharmacy.

Implementing Primary Care Mediated Population Genetic Screening Within an Integrated Health System.

INTRODUCTION: Genetic screenings can have a large impact on enabling personalized preventive care. However, this can be limited by the primary use of medical history-based screenings in determining care. The purpose of this study was to understand the impact of DNA10K, a population-based genetic screening program mediated by primary care physicians within an integrated health system to emphasize its contribution to preventive healthcare. METHODS: Construction of the patient experience as part of DNA10K shaped the context for PCP engagement within the program. A cross-sectional analysis of patient consents, orders, tests, and results of nearly 10,000 patients within the primary care specialties of family medicine, internal medicine or obstetrics/gynecology between April 1, 2019 and January 22, 2020 was conducted. RESULTS: Across all specialties, a median number of 7.5 cancer and cardiovascular disease variants per PCP was found. The average age of the study population was 49.6 years. Over 8% of these patients had at least one actionable genetic risk variant and almost 2% of patients had at least one CDC Tier 1 variant. The median numbers of patients per PCP with either hereditary breast and ovarian cancer, Lynch Syndrome, or Familial Hypercholesterolemia was 1 (Interquartile Range 0-2). DISCUSSION: The analysis of test results and the engagement of an integrated healthcare system in the implementation of a genetic screening program suggests that it can have a large impact on population health outcomes and minimal referral burden to PCPs if identified risks can lead to preventive care.

Moving Pharmacogenetics Into Practice: It's All About the Evidence!

The evidence for pharmacogenetics has grown rapidly in recent decades. However, the strength of evidence required for the clinical implementation of pharmacogenetics is highly debated. Therefore, the purpose of this review is to summarize different perspectives on the evidence required for the clinical implementation of pharmacogenetics. First, we present two patient cases that demonstrate how knowledge of pharmacogenetic evidence affected their care. Then we summarize resources that curate pharmacogenetic evidence, types of evidence (with an emphasis on randomized controlled trials [RCT]) and their limitations, and different perspectives from implementers, clinicians, and patients. We compare pharmacogenetics to a historical example (i.e., the evidence required for the clinical implementation of pharmacokinetics/therapeutic drug monitoring), and we provide future perspectives on the evidence for pharmacogenetic panels and the need for more education in addition to evidence. Although there are differences in the interpretation of pharmacogenetic evidence across resources, efforts for standardization are underway. Survey data illustrate the value of pharmacogenetic testing from the patient perspective, with their providers seen as key to ensuring maximum benefit from test results. However, clinicians and practice guidelines from medical societies often rely on RCT data to guide treatment decisions, which are not always feasible or ethical in pharmacogenetics. Thus, recognition of other types of evidence to support pharmacogenetic implementation is needed. Among pharmacogenetic implementers, consistent evidence of pharmacogenetic associations is deemed most critical. Ultimately, moving pharmacogenetics into practice will require consideration of multiple stakeholder perspectives, keeping particularly attuned to the voice of the ultimate stakeholder-the patient.

Patient-Reported Outcomes and Experiences with Population Genetic Testing Offered Through a Primary Care Network.

Aims: To explore patient experiences in a large-scale primary care-based, preemptive genetic testing program. Methods: Patients who received genetic results from the initiative were invited to participate in an online survey 3 weeks postresult disclosure. A 6-month follow-up survey was sent to assess changes over time. Results: The initial survey was completed by 1646 patients, with 544 completing the 6-month follow-up survey. The following outcomes were high overall: patient-reported understanding of results (cancer: 87%; cardiac: 86%); perceived utility (75%); positive emotions (relieved: 66.8%; happy: 62.0%); family result sharing (67.6%); and satisfaction (87%), although analysis by demographic factors identified groups who may benefit from additional education and emotional support. Results-related health behaviors and discussions with providers increased over time (screening procedures 6.1% to 14.2% p < 0.001; provider discussion 10.3% to 25.3%, p < 0.001), and were more likely to take place for patients with positive cancer and/or cardiac results (39.8% vs. 7.6%, p < 0.001). Forty-seven percent of patients reported insurance discrimination concerns, and most (79.4%) were not familiar with privacy and nondiscrimination laws. Concerns regarding discrimination and negative emotions decreased between the two survey time points (privacy issues 44.6% to 35.1% p < 0.001; life insurance discrimination concerns 35.5% to 29.6%, p = 0.001; anxiety 8.1% to 3.3%, p < 0.001; and uncertainty 19.8% to 12.8%, p < 0.001). These findings led to the development and integration of additional patient resources to improve program implementation. Conclusion: Our findings highlight patient experiences with and areas of need in a community-based genomic screening pilot initiative using a mixed primary care/genetics provider model to deliver precision medicine.

Synthesis of major pharmacogenomics pretest counseling themes: a multisite comparison.

The accessibility of pharmacogenomic (PGx) testing has grown substantially over the last decade and with it has arisen a demand for patients to be counseled on the use of these tests. While guidelines exist for the use of PGx results; objective determinants for who should receive PGx testing remain incomplete. PGx clinical services have been created to meet these screening and education needs and significant variability exists between these programs. This article describes the practices of four PGx clinics during pretest counseling sessions. A description of the major tenets of the benefits, limitations and risks of testing are compiled. Additional tools are provided to serve as a foundation for those wishing to begin or expand their own counseling service.

Clinical Pharmacogenetics Implementation Consortium Guideline for CYP2D6, OPRM1, and COMT Genotypes and Select Opioid Therapy.

Opioids are mainly used to treat both acute and chronic pain. Several opioids are metabolized to some extent by CYP2D6 (codeine, tramadol, hydrocodone, oxycodone, and methadone). Polymorphisms in CYP2D6 have been studied for an association with the clinical effect and safety of these drugs. Other genes that have been studied for their association with opioid clinical effect or adverse events include OPRM1 (mu receptor) and COMT (catechol-O-methyltransferase). This guideline updates and expands the 2014 Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for CYP2D6 genotype and codeine therapy and includes a summation of the evidence describing the impact of CYP2D6, OPRM1, and COMT on opioid analgesia and adverse events. We provide therapeutic recommendations for the use of CYP2D6 genotype results for prescribing codeine and tramadol and describe the limited and/or weak data for CYP2D6 and hydrocodone, oxycodone, and methadone, and for OPRM1 and COMT for clinical use.

Flype: Software for enabling personalized medicine.

The advent of next generation DNA sequencing (NGS) has revolutionized clinical medicine by enabling wide-spread testing for genomic anomalies and polymorphisms. With that explosion in testing, however, come several informatics challenges including managing large amounts of data, interpreting the results and providing clinical decision support. We present Flype, a web-based bioinformatics platform built by a small group of bioinformaticians working in a community hospital setting, to address these challenges by allowing us to: (a) securely accept data from a variety of sources, (b) send orders to a variety of destinations, (c) perform secondary analysis and annotation of NGS data, (d) provide a central repository for all genomic variants, (e) assist with tertiary analysis and clinical interpretation, (f) send signed out data to our EHR as both PDF and discrete data elements, (g) allow population frequency analysis and (h) update variant annotation when literature knowledge evolves. We discuss the multiple use cases Flype supports such as (a) in-house NGS tests, (b) in-house pharmacogenomics (PGX) tests, (c) dramatic scale-up of genomic testing using an external lab, (d) consumer genomics using two external partners, and (e) a variety of reporting tools. The source code for Flype is available upon request to the authors.

Variant Interpretation in Current Pharmacogenetic Testing.

In the current marketplace, there are now more than a dozen commercial companies providing pharmacogenetic tests. Each company varies in the panel of genes they test and the variants they are able to screen for. The reports generated by these companies provide phenotypic interpretations of pharmacogenes and clinically actionable gene-drug interactions based on internally curated data and proprietary algorithms. The freedom to choose the types of evidence to include versus exclude in interpreting genomics has created reporting discrepancies in the industry. The case report presented here reveals the discordant phenotype analysis provided by two pharmacogenetic testing companies. The uncertainty and unnecessary distress experienced by the patient highlights the need for consensus in phenotype reporting within the industry.

Primary Care Physician Experiences with Integrated Population-Scale Genetic Testing: A Mixed-Methods Assessment.

The scalable delivery of genomic medicine requires collaboration between genetics and non-genetics providers. Thus, it is essential to investigate and address the perceived value of and barriers to incorporating genetic testing into the clinical practice of primary care providers (PCPs). We used a mixed-methods approach of qualitative interviews and surveys to explore the experience of PCPs involved in the pilot DNA-10K population genetic testing program. Similar to previous research, PCPs reported low confidence with tasks related to ordering, interpreting and managing the results of genetic tests, and identified the need for additional education. PCPs endorsed high levels of utility for patients and their families but noted logistical challenges to incorporating genetic testing into their practice. Overall PCPs were not familiar with the United States' Genetic Information Nondiscrimination Act and they expressed high levels of concern for patient data privacy and potential insurance discrimination. This PCP feedback led to the development and implementation of several processes to improve the PCP experience with the DNA-10K program. These results contribute to the knowledge base regarding genomic implementation using a mixed provider model and may be beneficial for institutions developing similar clinical programs.