Healthcare Utilization and Costs after Receiving a Positive BRCA1/2 Result from a Genomic Screening Program.

Population genomic screening has been demonstrated to detect at-risk individuals who would not be clinically identified otherwise. However, there are concerns about the increased utilization of unnecessary services and the associated increase in costs. The objectives of this study are twofold: (1) determine whether there is a difference in healthcare utilization and costs following disclosure of a pathogenic/likely pathogenic (P/LP) BRCA1/2 variant via a genomic screening program, and (2) measure the post-disclosure uptake of National Comprehensive Cancer Network (NCCN) guideline-recommended risk management. We retrospectively reviewed electronic health record (EHR) and billing data from a female population of BRCA1/2 P/LP variant carriers without a personal history of breast or ovarian cancer enrolled in Geisinger's MyCode genomic screening program with at least a one-year post-disclosure observation period. We identified 59 women for the study cohort out of 50,726 MyCode participants. We found no statistically significant differences in inpatient and outpatient utilization and average total costs between one-year pre- and one-year post-disclosure periods ($18,821 vs. $19,359, p = 0.76). During the first year post-disclosure, 49.2% of women had a genetic counseling visit, 45.8% had a mammography and 32.2% had an MRI. The uptake of mastectomy and oophorectomy was 3.5% and 11.8%, respectively, and 5% of patients received chemoprevention.

Precision screening for familial hypercholesterolaemia: a machine learning study applied to electronic health encounter data.

BACKGROUND: Cardiovascular outcomes for people with familial hypercholesterolaemia can be improved with diagnosis and medical management. However, 90% of individuals with familial hypercholesterolaemia remain undiagnosed in the USA. We aimed to accelerate early diagnosis and timely intervention for more than 1·3 million undiagnosed individuals with familial hypercholesterolaemia at high risk for early heart attacks and strokes by applying machine learning to large health-care encounter datasets. METHODS: We trained the FIND FH machine learning model using deidentified health-care encounter data, including procedure and diagnostic codes, prescriptions, and laboratory findings, from 939 clinically diagnosed individuals with familial hypercholesterolaemia (395 of whom had a molecular diagnosis) and 83 136 individuals presumed free of familial hypercholesterolaemia, sampled from four US institutions. The model was then applied to a national health-care encounter database (170 million individuals) and an integrated health-care delivery system dataset (174 000 individuals). Individuals used in model training and those evaluated by the model were required to have at least one cardiovascular disease risk factor (eg, hypertension, hypercholesterolaemia, or hyperlipidemia). A Health Insurance Portability and Accountability Act of 1996-compliant programme was developed to allow providers to receive identification of individuals likely to have familial hypercholesterolaemia in their practice. FINDINGS: Using a model with a measured precision (positive predictive value) of 0·85, recall (sensitivity) of 0·45, area under the precision-recall curve of 0·55, and area under the receiver operating characteristic curve of 0·89, we flagged 1 331 759 of 170 416 201 patients in the national database and 866 of 173 733 individuals in the health-care delivery system dataset as likely to have familial hypercholesterolaemia. Familial hypercholesterolaemia experts reviewed a sample of flagged individuals (45 from the national database and 103 from the health-care delivery system dataset) and applied clinical familial hypercholesterolaemia diagnostic criteria. Of those reviewed, 87% (95% Cl 73-100) in the national database and 77% (68-86) in the health-care delivery system dataset were categorised as having a high enough clinical suspicion of familial hypercholesterolaemia to warrant guideline-based clinical evaluation and treatment. INTERPRETATION: The FIND FH model successfully scans large, diverse, and disparate health-care encounter databases to identify individuals with familial hypercholesterolaemia. FUNDING: The FH Foundation funded this study. Support was received from Amgen, Sanofi, and Regeneron.

Patient-Centered Precision Health In A Learning Health Care System: Geisinger's Genomic Medicine Experience.

Health care delivery is increasingly influenced by the emerging concepts of precision health and the learning health care system. Although not synonymous with precision health, genomics is a key enabler of individualized care. Delivering patient-centered, genomics-informed care based on individual-level data in the current national landscape of health care delivery is a daunting challenge. Problems to overcome include data generation, analysis, storage, and transfer; knowledge management and representation for patients and providers at the point of care; process management; and outcomes definition, collection, and analysis. Development, testing, and implementation of a genomics-informed program requires multidisciplinary collaboration and building the concepts of precision health into a multilevel implementation framework. Using the principles of a learning health care system provides a promising solution. This article describes the implementation of population-based genomic medicine in an integrated learning health care system-a working example of a precision health program.

Medication therapy disease management: Geisinger's approach to population health management.

PURPOSE: Pharmacists' involvement in a population health initiative focused on chronic disease management is described. SUMMARY: Geisinger Health System has cultivated a culture of innovation in population health management, as highlighted by its ambulatory care pharmacy program, the Medication Therapy Disease Management (MTDM) program. Initiated in 1996, the MTDM program leverages pharmacists' pharmacotherapy expertise to optimize care and improve outcomes. MTDM program pharmacists are trained and credentialed to manage over 16 conditions, including atrial fibrillation (AF) and multiple sclerosis (MS). Over a 15-year period, Geisinger Health Plan (GHP)-insured patients with AF whose warfarin therapy was managed by the MTDM program had, on average, 18% fewer emergency department (ED) visits and 18% fewer hospitalizations per year than GHP enrollees with AF who did not receive MTDM services, with 23% lower annual total care costs. Over a 2-year period, GHP-insured patients with MS whose pharmacotherapy was managed by pharmacists averaged 28% fewer annual ED visits than non-pharmacist-managed patients; however, the mean annual total care cost was 21% higher among MTDM clinic patients. CONCLUSION: The Geisinger MTDM program has evolved over 20 years from a single pharmacist-run anticoagulation clinic into a large program focused on managing the health of an ever-growing population. Initial challenges in integrating pharmacists into the Geisinger patient care framework as clinical experts were overcome by demonstrating the MTDM program's positive impact on patient outcomes.

Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR study.

The DiscovEHR collaboration between the Regeneron Genetics Center and Geisinger Health System couples high-throughput sequencing to an integrated health care system using longitudinal electronic health records (EHRs). We sequenced the exomes of 50,726 adult participants in the DiscovEHR study to identify ~4.2 million rare single-nucleotide variants and insertion/deletion events, of which ~176,000 are predicted to result in a loss of gene function. Linking these data to EHR-derived clinical phenotypes, we find clinical associations supporting therapeutic targets, including genes encoding drug targets for lipid lowering, and identify previously unidentified rare alleles associated with lipid levels and other blood level traits. About 3.5% of individuals harbor deleterious variants in 76 clinically actionable genes. The DiscovEHR data set provides a blueprint for large-scale precision medicine initiatives and genomics-guided therapeutic discovery.

The Geisinger MyCode community health initiative: an electronic health record-linked biobank for precision medicine research.

PURPOSE: Geisinger Health System (GHS) provides an ideal platform for Precision Medicine. Key elements are the integrated health system, stable patient population, and electronic health record (EHR) infrastructure. In 2007, Geisinger launched MyCode, a system-wide biobanking program to link samples and EHR data for broad research use. METHODS: Patient-centered input into MyCode was obtained using participant focus groups. Participation in MyCode is based on opt-in informed consent and allows recontact, which facilitates collection of data not in the EHR and, since 2013, the return of clinically actionable results to participants. MyCode leverages Geisinger's technology and clinical infrastructure for participant tracking and sample collection. RESULTS: MyCode has a consent rate of >85%, with more than 90,000 participants currently and with ongoing enrollment of ~4,000 per month. MyCode samples have been used to generate molecular data, including high-density genotype and exome sequence data. Genotype and EHR-derived phenotype data replicate previously reported genetic associations. CONCLUSION: The MyCode project has created resources that enable a new model for translational research that is faster, more flexible, and more cost-effective than traditional clinical research approaches. The new model is scalable and will increase in value as these resources grow and are adopted across multiple research platforms.Genet Med 18 9, 906-913.

Tryptophan depletion and HIV infection: a metabolic link to pathogenesis.

HIV-1-infected patients have low circulating tryptophan concentrations despite evidence of adequate dietary intake of this essential amino acid. A chronic increase in inducible tryptophan oxidation is the basis of HIV-1-associated tryptophan depletion. This metabolic process results in the irretrievable loss of tryptophan molecules from the available pool. Such sustained disruption of normal tryptophan metabolism over time disturbs the many metabolic processes involving this amino acid, and has been implicated in some features of AIDS pathogenesis. Normal T-cell function is adversely affected by tryptophan depletion, but the extent of the effect in HIV-1-infected patients is still unclear. Attempting to directly supplement tryptophan is not advised given the potential increase in circulating concentrations of neurotoxic intermediates. Although only preliminary data are available, evidence suggests that antiretroviral and nicotinamide treatments can boost plasma tryptophan concentrations in HIV-1-infected patients and impact the secondary effects of tryptophan depletion. Additional study of this metabolism could lead to improved treatment strategies for patients with HIV infection. In this review I focus on the potential links between disturbed tryptophan metabolism and pathogenesis.