Integrating pharmacogenomics into the electronic health record by implementing genomic indicators.

Caraballo PJ1,2, Sutton JA3, Giri J4, Wright JA5, Nicholson WT6, Kullo IJ7, Parkulo MA2,8, Bielinski SJ9, Moyer AM10.

Author information

1: Division of General Internal Medicine, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA.
2: Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, USA.
3: Department of Information Technology, Mayo Clinic, Rochester, Minnesota.
4: Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, USA.
5: Department of Pharmacy Services, Mayo Clinic, Rochester, Minnesota, USA.
6: Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, Minnesota, USA.
7: Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
8: Division of Community Internal Medicine, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA.
9: Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA.
10: Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA.

Abstract

Pharmacogenomics (PGx) clinical decision support integrated into the electronic health record (EHR) has the potential to provide relevant knowledge to clinicians to enable individualized care. However, past experience implementing PGx clinical decision support into multiple EHR platforms has identified important clinical, procedural, and technical challenges. Commercial EHRs have been widely criticized for the lack of readiness to implement precision medicine. Herein, we share our experiences and lessons learned implementing new EHR functionality charting PGx phenotypes in a unique repository, genomic indicators, instead of using the problem or allergy list. The Gen-Ind has additional features including a brief description of the clinical impact, a hyperlink to the original laboratory report, and links to additional educational resources. The automatic generation of genomic indicators from interfaced PGx test results facilitates implementation and long-term maintenance of PGx data in the EHR and can be used as criteria for synchronous and asynchronous CDS.

KEYWORDS:

clinical decision support systems; delivery of health care; electronic health record; medical informatics; medication therapy management; pharmacogenetics; precision medicine