PURPOSE:Many healthcare professionals received little to no practical training on pharmacogenomics (PGx) during their degree programs. Due to a rapid influx of PGx into clinical practice, healthcare professionals face a need for education and support. SUMMARY:We established a PGx Extension for Community Healthcare Outcome (ECHO) program, a telementoring and education model whereby healthcare professionals learn and acquire clinical skills through real-world case presentations. The goal was to provide clinical education, foster a community of practice, and promote health equity in PGx utilization by providing convenient virtual access to PGx expertise. Here we report on the first 2 years of the program and the delivery of 29 case sessions. Most learners (42%) are pharmacists, and participation has grown dramatically. At the time of enrollment, 57.6% of learners had previously received some form of PGx education prior to attending a session. Of these, only 17.6% felt very confident in their knowledge, 43.4% felt somewhat confident, and 24.7% were not confident in their knowledge. Participants were surveyed after each session through email. Of the attendee respondents, 94% agreed or strongly agreed that the sessions increased their knowledge, 88% reported increased skill in managing medications with PGx results, and 89% felt the education would improve their performance as health professionals. Notably, 54.9% of respondents indicated they would make changes to their practice primarily by using PGx to select a new medication and change a medication. CONCLUSION:The PGx ECHO model is an effective tool to bring together experts and learners for education and mentoring.
Data science has promise to be a useful tool in the prevention of genocide and related atrocity crimes. The generally agreed upon definition from the United Nations’ 1948 Genocide Convention has significant weight behind it, but attempts to operationalize it has proven to have mixed success. In this paper, we introduce our program toward genocide prevention which, in part, requires quantitative approaches in addition to more traditional methods in genocide studies. We apply data-scientific approaches to operationalize this definition of genocide, in particular, to predict the onset of genocide, and identify preventive policy options. Toward forecasting, we reaffirm some of the risk metrics and methodologies of the Early Warning Project, attaining accuracy scores in retrospective analysis of the forecasting problem, applying a bootstrapping methodology to achieve an aggregate median test-set accuracy of 80
