Medical College of Wisconsin
CTSICores SearchResearch InformaticsREDCap

Development and use of active clinical decision support for preemptive pharmacogenomics. J Am Med Inform Assoc 2014 Feb;21(e1):e93-9

Date

08/28/2013

Pubmed ID

23978487

Pubmed Central ID

PMC3957400

DOI

10.1136/amiajnl-2013-001993

Scopus ID

2-s2.0-84893549013 (requires institutional sign-in at Scopus site)   166 Citations

Abstract

BACKGROUND: Active clinical decision support (CDS) delivered through an electronic health record (EHR) facilitates gene-based drug prescribing and other applications of genomics to patient care.

OBJECTIVE: We describe the development, implementation, and evaluation of active CDS for multiple pharmacogenetic test results reported preemptively.

MATERIALS AND METHODS: Clinical pharmacogenetic test results accompanied by clinical interpretations are placed into the patient's EHR, typically before a relevant drug is prescribed. Problem list entries created for high-risk phenotypes provide an unambiguous trigger for delivery of post-test alerts to clinicians when high-risk drugs are prescribed. In addition, pre-test alerts are issued if a very-high risk medication is prescribed (eg, a thiopurine), prior to the appropriate pharmacogenetic test result being entered into the EHR. Our CDS can be readily modified to incorporate new genes or high-risk drugs as they emerge.

RESULTS: Through November 2012, 35 customized pharmacogenetic rules have been implemented, including rules for TPMT with azathioprine, thioguanine, and mercaptopurine, and for CYP2D6 with codeine, tramadol, amitriptyline, fluoxetine, and paroxetine. Between May 2011 and November 2012, the pre-test alerts were electronically issued 1106 times (76 for thiopurines and 1030 for drugs metabolized by CYP2D6), and the post-test alerts were issued 1552 times (1521 for TPMT and 31 for CYP2D6). Analysis of alert outcomes revealed that the interruptive CDS appropriately guided prescribing in 95% of patients for whom they were issued.

CONCLUSIONS: Our experience illustrates the feasibility of developing computational systems that provide clinicians with actionable alerts for gene-based drug prescribing at the point of care.

Author List

Bell GC, Crews KR, Wilkinson MR, Haidar CE, Hicks JK, Baker DK, Kornegay NM, Yang W, Cross SJ, Howard SC, Freimuth RR, Evans WE, Broeckel U, Relling MV, Hoffman JM

Author

Ulrich Broeckel MD Chief, Center Associate Director, Professor in the Pediatrics department at Medical College of Wisconsin




MESH terms used to index this publication - Major topics in bold

Decision Support Systems, Clinical
Electronic Health Records
Humans
Pharmacogenetics