Changes in behavior are necessary to apply genomic discoveries to practice. We prospectively studied medication changes made by providers representing eight different medicine specialty clinics whose patients had submitted to preemptive pharmacogenomic genotyping. An institutional clinical decision support (CDS) system provided pharmacogenomic results using traffic light alerts: green/genomically favorable, yellow/genomic caution, red/high risk. The influence of pharmacogenomic alerts on prescribing behaviors was the primary endpoint. 2279 outpatient encounters were analyzed. Independent of other potential prescribing mediators, medications with high pharmacogenomic risk were changed significantly more often than prescription drugs lacking pharmacogenomic information (odds ratio [OR]=26.2 [9.0–75.3], p<0.0001). Medications with cautionary pharmacogenomic information were also changed more frequently (OR=2.4 [1.7–3.5], p<0.0001). No pharmacogenomically high-risk medications were prescribed during the entire study when physicians consulted the CDS tool. Pharmacogenomic information improved prescribing in patterns aimed at reducing patient risk, demonstrating that enhanced prescription decision-making is achievable through clinical integration of genomic medicine.

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.

Despite growing clinical use of genomic information, patient perceptions of genomic-based care are poorly understood. We prospectively studied patient-physician pairs who participated in an institutional pharmacogenomic implementation program. Trust/Privacy/Empathy/Medical Decision-Making (MDM)/Personalized Care (PC) dimensions were assessed through patient surveys after clinic visits at which physicians had access to preemptive pharmacogenomic results (Likert scale, 1-minimum/5-maximum; mean [SD]). From 2012–2015, 1,261 surveys were issued to 507 patients, with 792 (62.8%) returned. Privacy, Empathy, MDM and PC scores were significantly higher following visits when physicians considered pharmacogenomic results. Importantly, PC scores were significantly higher after physicians used pharmacogenomic information to guide medication changes (4.0[1.4] vs. 3.0[1.6], P<0.001) compared to prescribing visits without genomic guidance. Multivariable modeling controlling for clinical factors confirmed PC scores were more favorable following visits with genomic-influenced prescribing (OR=3.26 [1.31–8.14], P<0.05). Physicians appear to individualize care when utilizing pharmacogenomic results and this decision-making augmentation is perceived positively by patients.

Providers have expressed a strong desire to have additional clinical decision-support tools to help with interpretation of pharmacogenomic results. We developed and tested a novel disease–drug association tool that enables pharmacogenomic-based prescribing to treat common diseases. First, 324 drugs were mapped to 484 distinct diseases (mean number of drugs treating each disease was 4.9; range 1–37).Then the disease–drug association tool was pharmacogenomically annotated, with an average of 1.8 pharmacogenomically annotated drugs associated/disease. Applying this tool to a prospectively enrolled >1,000 patient cohort from a tertiary medical center showed that 90% of the top ∼20 diseases in this population and ≥93% of patients could appropriately be treated with ≥1 medication with actionable pharmacogenomic information. When combined with clinical patient genotypes, this tool permits delivery of patient-specific pharmacogenomically informed disease treatment recommendations to inform the treatment of many medical conditions of the US population, a key initial step towards implementation of precision medicine.

BackgroundSudden 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.MethodsFemale 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.ResultsThe 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.ConclusionsIn 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.