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Hartzler, Andrea L.

https://orcid.org/0000-0003-4932-7314

Mayo Clinic

Kaiser Permanente Washington Health Research Institute

Citations 3

Empowering genomic medicine by establishing critical sequencing result data flows: the eMERGE example
Aronson, Samuel , Babb, Lawrence , Ames, Darren C., Gibbs, Richard A. , Connelly, John, Marsolo, Keith , Hartzler, Andrea L. , Liang, Wayne H. , Ralston, James D. , Devine, Emily Beth , Murphy, Shawn N. , Freimuth, Robert R. , Rasmussen, Luke V. , Peterson, Josh F. , Robinson, Jamie R. , Wiley, Ken, Taylor, Casey Overby , Group, eMERGE Network EHRI Working, Caraballo, Pedro J. , Babb, Lawrence , Rasmussen, Luke V , Freimuth, Robert R , Venner, Eric , Weng, Chunhua , Williams, Marc S , Chute, Christopher G. , Kullo, Iftikhar J. , Wehbe, Firas , Group, eMERGE Network EHRI Working, Peterson, Josh F

Journal of the American Medical Informatics Association | April 19, 2018 | Volume: 25 | Issue: 10 | Pages: 1375-1381

10.1093/jamia/ocy051 Get It

AbstractThe eMERGE Network is establishing methods for electronic transmittal of patient genetic test results from laboratories to healthcare providers across organizational boundaries. We surveyed the capabilities and needs of different network participants, established a common transfer format, and implemented transfer mechanisms based on this format. The interfaces we created are examples of the connectivity that must be instantiated before electronic genetic and genomic clinical decision support can be effectively built at the point of care. This work serves as a case example for both standards bodies and other organizations working to build the infrastructure required to provide better electronic clinical decision support for clinicians.
Healthcare Provider Education to Support Integration of Pharmacogenomics in Practice: The Emerge Network Experience
Vitek, Carolyn R. Rohrer , Abul‐Husn, Noura S. , Connolly, John J. , Hartzler, Andrea L. , Kitchner, Terrie, Peterson, Josh F. , Rasmussen, Luke V. , Smith, Maureen E. , Stallings, Sarah , Wolf, Wendy A. , Prows, Cynthia A. , Rohrer Vitek, Carolyn R, Williams, Marc S , Rasmussen, Luke V , Connolly, John J , Peterson, Josh F

Pharmacogenomics | June 22, 2017 | Volume: 18 | Issue: 10 | Pages: 1013-1025

10.2217/pgs-2017-0038 Get It

CSER and eMERGE: current and potential state of the display of genetic information in the electronic health record
Shirts, Brian H. , Salama, Joseph S , Aronson, Samuel , Gray, Stacy W. , Hindorff, Lucia A. , Jarvik, Gail P. , Tarczy‐Hornoch, Peter , Freimuth, Robert R. , Grundmeier, Robert W. , Hartzler, Andrea L. , Li, Rongling, Peissig, Peggy , Peterson, Josh F. , Rasmussen, Luke V. , Starren, Justin , Overby, Casey Lynnette, Taylor, Casey Overby , Chute, Christopher G. , Chung, Wendy K. , Plon, Sharon E. , Stoffel, Elena M. , Van Allen, Eliezer M , Weck, Karen E. , Williams, Marc S , Jarvik, Gail P , Rasmussen, Luke V , Freimuth, Robert R , Peterson, Josh F , Gray, Stacy W

Journal of the American Medical Informatics Association | July 4, 2015 | Volume: 22 | Issue: 6 | Pages: 1231-1242

10.1093/jamia/ocv065 Get It

Abstract Objective Clinicians’ ability to use and interpret genetic information depends upon how those data are displayed in electronic health records (EHRs). There is a critical need to develop systems to effectively display genetic information in EHRs and augment clinical decision support (CDS). Materials and Methods The National Institutes of Health (NIH)-sponsored Clinical Sequencing Exploratory Research and Electronic Medical Records & Genomics EHR Working Groups conducted a multiphase, iterative process involving working group discussions and 2 surveys in order to determine how genetic and genomic information are currently displayed in EHRs, envision optimal uses for different types of genetic or genomic information, and prioritize areas for EHR improvement. Results There is substantial heterogeneity in how genetic information enters and is documented in EHR systems. Most institutions indicated that genetic information was displayed in multiple locations in their EHRs. Among surveyed institutions, genetic information enters the EHR through multiple laboratory sources and through clinician notes. For laboratory-based data, the source laboratory was the main determinant of the location of genetic information in the EHR. The highest priority recommendation was to address the need to implement CDS mechanisms and content for decision support for medically actionable genetic information. Conclusion Heterogeneity of genetic information flow and importance of source laboratory, rather than clinical content, as a determinant of information representation are major barriers to using genetic information optimally in patient care. Greater effort to develop interoperable systems to receive and consistently display genetic and/or genomic information and alert clinicians to genomic-dependent improvements to clinical care is recommended.