PGx CDS Process Outcomes – Tim Herr • Dr. Herr discussed how PGx CDS alerts effect clinical action by inventorying alert scenarios across sites, gathering data on firing rate, and determining how physicians are interacting with the data. This report also expounded on previous discussions of when to initiate clinical decision support (at time of prescription or downstream), and how to initiate it (actively or passively). • Preliminary results of the study indicate that just because a physician ignored an alert doesn’t mean the alert wasn’t considered in the treatment plan. Passive alerts are potentially a useful way of disseminating information. Clopidogrel is the most common alert firing, and while on the whole physicians took no action, there was some change (23 followed the alert). One explanation is that the alert can be viewed and acted on at any time after firing, and they may see the alert at a time when they aren’t making a prescribing decision yet. Also, there are differences in recommendation acceptance of different DGIs (Simvastatin recommendations were accepted more often than those for Warfarin). • Follow up interviews to gather qualitative information are in process. The group discussed legal precedence and implications of CDS, including where liability lies and standard of care. Phenotyping Workgroup Update – Josh Denny & Peggy Peissig • The workgroup completed a total of 56 phenotyping algorithms throughout eMERGE I & II. The phenotypes had ~98% PPV overall. • Working with Brad Malin, the group developed meta-analysis tools (SecureMA), as was able to do a systematic review of re-identification attacks (findings showed only 1 paper using modern de-identification methods on healthcare data, and had a success rate of .013%). • During Phase II, PheKB.org was expanded to include validation tools. • Portable NLP was explored during Phase II. • The KNIME workflow engine was utilized for phenotyping at a minimum of 2 sites for 4 phenotypes. This engine was shown to reduce implementation burden at other sites, although the group noted that a common data model would help more sites be able to utilize. “A Prototype for Executable and Portable Electronic Clinical Quality Measures Using the KNIME Analytic o Platform” - Winner of ‘Best Paper Award’ at 2015 AMIA CRI • The workgroup published 91 papers, with an additional 19 in progress. Next Generation Phenotyping Using Interoperable Big Data – George Hripcsak & Chunhua Weng • An overview of OHDSI was provided. Observational Health Data Sciences and Informatics (OHDSI) is a network of researchers and health databases with a central coordinating center at Columbia. Their mission is to provide a large-scale analysis of observational health databases for population-level estimation and patient-level predictions. The vision for OHDSI is for patients and clinicians to use the tool to access evidence based on a 1 billion patient cohort. OHDSI is currently comprised of >120 collaborators from 11 different countries. Takes 3 months for collaborators to convert data to the common data model. o eMERGE Coordinating Center Report – Paul Harris , Marylyn Ritchie, Josh Denny, & Brad Malin • The CC assisted the network in: Accelerating phenotype development/sharing: o § PheKB, Data Dictionary/Data Validation Tool, PheWAS Catalog, and the eMERGE Record Counter were all given as examples of tools developed by the CC. Expanding genomic data integration: o § 55,028 total samples were genotyped during Phases I & II. The merged imputed dataset was released to dbGaP. § The CC developed a variant calling pipeline for eMERGE-PGx w/ the Genomics workgroup. CC also performed variant calling for all eMERGE-PGx samples.