Simplifying the use of pharmacogenomics in clinical practice: Building the genomic prescribing system.

BACKGROUND: A barrier to the use of genomic information during prescribing is the limited number of software solutions that combine a user-friendly interface with complex medical data. We built and designed an online, secure, electronic custom interface termed the Genomic Prescribing System (GPS). METHODS: Actionable pharmacogenomic (PGx) information was reviewed, collected, and stored in the back-end of GPS to enable creation of customized drug- and variant-specific clinical decision support (CDS) summaries. The database architecture utilized the star schema to store information. Patient raw genomic data underwent transformation via custom-designed algorithms to enable gene and phenotype-level associations. Multiple external data sets (PubMed, The Systematized Nomenclature of Medicine (SNOMED), National Drug File - Reference Terminology (ND-FRT), and a publically-available PGx knowledgebase) were integrated to facilitate the delivery of patient, drug, disease, and genomic information. Institutional security infrastructure was leveraged to securely store patient genomic and clinical data on a HIPAA-compliant server farm. RESULTS: As of May 17, 2016, the GPS back-end housed 257 CDS encompassing 112 genetic variants, 42 genes, and 46 PGx-actionable drugs. The GPS user interface presented patient-specific CDS alongside a recognizable traffic light symbol (green/yellow/red), denoting PGx risk for each genomic result. The number of traffic lights per visit increased with the corresponding increase in the number of available PGx-annotated drugs over time. An integrated drug and disease search functionality, links to primary literature sources, and potential alternative PGx drugs were indicated. The system, which was initially used as stand-alone CDS software within our clinical environment, was then integrated with the institutional electronic medical record for enhanced usability. There have been nearly 2000 logins in 43months since inception, with usage exceeding 56 logins per month and system up-times of 99.99%. For all patient-provider visits encompassing >3years of implementation, unique alert click-through rates corresponded to genomic risk: red lights clicked 100%, yellow lights 79%, green lights 43%. CONCLUSIONS: Successful deployment of GPS by combining complex data and recognizable iconography led to a tool that enabled point-of-care genomic delivery with high usability. Continued scalability and incorporation of additional clinical elements to be considered alongside PGx information could expand future impact.

You can have your breastmilk and safe sleep too: a preliminary analysis of infant safe sleep data in a Midwestern home visiting program.

BACKGROUND: Sudden unexpected infant death (SUID) accounted for approximately 3700 infant deaths in the US in 2015. SUID risk factors include prone sleeping, bed-sharing, soft bedding use, and maternal smoking. Infant safe sleep data in at-risk communities are difficult to obtain and home visiting programs can add to what we know. This study's purpose is to determine how often caregivers enrolled in home visiting programs provide safe sleep environments for their infants in relation to breastfeeding status and tobacco use. METHODS: Female caregivers in at-risk communities were prospectively enrolled in Midwestern home visiting programs. Those that had infants < 365 days old and completed a safe sleep survey between October 1, 2016 and May 18, 2017 were included. Caregivers' responses (always, sometimes, or never) to three safe sleep questions were compared by breastfeeding status, caregiver tobacco use, and household tobacco use using Pearson's chi-squared or Fisher's exact test. RESULTS: The characteristics of the 289 eligible female caregivers included 120 (42%) ≤ 21 years old, 137 (47%) black, 77 (27%) breastfeeding, and 60 (22%) with household tobacco use. Two hundred forty-six (85%) caregivers always placed infants in the supine position, 148 (51%) never bed-shared, and 186 (64%) never used soft bedding. Ongoing breastfeeding caregivers never bed-shared more often than those who never breastfed or weaned (66% vs. 53% vs. 39%, p = 0.003). Households with tobacco use placed infants in the supine position less (75% vs. 88%, p = 0.03), bed-shared more (62% vs. 44%, p = 0.04), and used soft bedding more (50% vs. 32%, p = 0.004) relative to those without tobacco use. CONCLUSIONS: In this group of at-risk young mothers, those who breastfed bed-shared less than mothers who were not breastfeeding; this finding has implications toward reducing the SUID risk in similar populations. This study also demonstrated that infants living with a tobacco user are less likely to be sleeping safely. This suggests that a multifaceted approach to safe sleep counseling may be needed.