Objective The Phenotype Risk Score (PheRS) is a method to detect Mendelian disease patterns using phenotypes from the electronic health record (EHR). We compared the performance of different approaches mapping EHR phenotypes to Mendelian disease features. Materials and Methods PheRS utilizes Mendelian diseases descriptions annotated with Human Phenotype Ontology (HPO) terms. In previous work, we presented a map linking phecodes (based on International Classification of Diseases [ICD]-Ninth Revision) to HPO terms. For this study, we integrated ICD-Tenth Revision codes and lab data. We also created a new map between HPO terms using customized groupings of ICD codes. We compared the performance with cases and controls for 16 Mendelian diseases using 2.5 million de-identified medical records. Results PheRS effectively distinguished cases from controls for all 15 positive controls and all approaches tested (P < 4 × 1016). Adding lab data led to a statistically significant improvement for 4 of 14 diseases. The custom ICD groupings improved specificity, leading to an average 8% increase for precision at 100 (-2% to 22%). Eight of 10 adults with cystic fibrosis tested had PheRS in the 95th percentile prio to diagnosis. Discussion Both phecodes and custom ICD groupings were able to detect differences between affected cases and controls at the population level. The ICD map showed better precision for the highest scoring individuals. Adding lab data improved performance at detecting population-level differences. Conclusions PheRS is a scalable method to study Mendelian disease at the population level using electronic health record data and can potentially be used to find patients with undiagnosed Mendelian disease.

This cross-sectional study assesses potential opportunities for genotype-guided prescribing in pediatric populations among multiple health systems by examining the prevalence of prescriptions for each drug with the highest level of evidence and estimating the prevalence of potentially actionable prescribing decisions.

The value of utilizing a multigene pharmacogenetic panel to tailor pharmacotherapy is contingent on the prevalence of prescribed medications with an actionable pharmacogenetic association. The Clinical Pharmacogenetics Implementation Consortium (CPIC) has categorized over 35 gene‐drug pairs as “level A,” for which there is sufficiently strong evidence to recommend that genetic information be used to guide drug prescribing. The opportunity to use genetic information to tailor pharmacotherapy among adult patients was determined by elucidating the exposure to CPIC level A drugs among 11 Implementing Genomics In Practice Network (IGNITE)‐affiliated health systems across the US. Inpatient and/or outpatient electronic‐prescribing data were collected between January 1, 2011 and December 31, 2016 for patients ≥ 18 years of age who had at least one medical encounter that was eligible for drug prescribing in a calendar year. A median of ~ 7.2 million adult patients was available for assessment of drug prescribing per year. From 2011 to 2016, the annual estimated prevalence of exposure to at least one CPIC level A drug prescribed to unique patients ranged between 15,719 (95% confidence interval (CI): 15,658–15,781) in 2011 to 17,335 (CI: 17,283–17,386) in 2016 per 100,000 patients. The estimated annual exposure to at least 2 drugs was above 7,200 per 100,000 patients in most years of the study, reaching an apex of 7,660 (CI: 7,632–7,687) per 100,000 patients in 2014. An estimated 4,748 per 100,000 prescribing events were potentially eligible for a genotype‐guided intervention. Results from this study show that a significant portion of adults treated at medical institutions across the United States is exposed to medications for which genetic information, if available, should be used to guide prescribing.

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Identifying acute events as they occur is challenging in large hospital systems. Here, we describe an automated method to detect 2 rare adverse drug events (ADEs), drug-induced torsades de pointes and Stevens-Johnson syndrome and toxic epidermal necrolysis, in near real time for participant recruitment into prospective clinical studies. A text processing system searched clinical notes from the electronic health record (EHR) for relevant keywords and alerted study personnel via email of potential patients for chart review or in-person evaluation. Between 2016 and 2018, the automated recruitment system resulted in capture of 138 true cases of drug-induced rare events, improving recall from 43% to 93%. Our focused electronic alert system maintained 2-year enrollment, including across an EHR migration from a bespoke system to Epic. Real-time monitoring of EHR notes may accelerate research for certain conditions less amenable to conventional study recruitment paradigms.