Dataset Information

Development and use of active clinical decision support for preemptive pharmacogenomics.

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.

SUBMITTER: Bell GC

PROVIDER: S-EPMC3957400 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

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Publications

Development and use of active clinical decision support for preemptive pharmacogenomics.

Bell Gillian C GC Crews Kristine R KR Wilkinson Mark R MR Haidar Cyrine E CE Hicks J Kevin JK Baker Donald K DK Kornegay Nancy M NM Yang Wenjian W Cross Shane J SJ Howard Scott C SC Freimuth Robert R RR Evans William E WE Broeckel Ulrich U Relling Mary V MV Hoffman James M JM

Journal of the American Medical Informatics Association : JAMIA 20130826 e1

<h4>Background</h4>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.<h4>Objective</h4>We describe the development, implementation, and evaluation of active CDS for multiple pharmacogenetic test results reported preemptively.<h4>Materials and methods</h4>Clinical pharmacogenetic test results accompanied by clinical interpretations are placed into the patient's EHR, ...[more]

PMID: 23978487

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Project description:PURPOSE:Precision oncology is hindered by the lack of decision support for determining the functional and therapeutic significance of genomic alterations in tumors and relevant clinically available options. To bridge this knowledge gap, we established a Precision Oncology Decision Support (PODS) team that provides annotations at the alteration-level and subsequently determined if clinical decision-making was influenced. METHODS:Genomic alterations were annotated to determine actionability based on a variant's known or potential functional and/or therapeutic significance. The medical records of a subset of patients annotated in 2015 were manually reviewed to assess trial enrollment. A web-based survey was implemented to capture the reasons why genotype-matched therapies were not pursued. RESULTS:PODS processed 1,669 requests for annotation of 4,084 alterations (2,254 unique) across 49 tumor types for 1,197 patients. 2,444 annotations for 669 patients included an actionable variant call: 32.5% actionable, 9.4% potentially, 29.7% unknown, 28.4% non-actionable. 66% of patients had at least one actionable/potentially actionable alteration. 20.6% (110/535) patients annotated enrolled on a genotype-matched trial. Trial enrolment was significantly higher for patients with actionable/potentially actionable alterations (92/333, 27.6%) than those with unknown (16/136, 11.8%) and non-actionable (2/66, 3%) alterations (p=0.00004). Actionable alterations in PTEN, PIK3CA, and ERBB2 most frequently led to enrollment on genotype-matched trials. Clinicians cited a variety of reasons why patients with actionable alterations did not enroll on trials. CONCLUSION:Over half of alterations annotated were of unknown significance or non-actionable. Physicians were more likely to enroll a patient on a genotype-matched trial when an annotation supported actionability. Future studies are needed to demonstrate the impact of decision support on trial enrollment and oncologic outcomes.

Dataset Information

Development and use of active clinical decision support for preemptive pharmacogenomics.

Background

Objective

Materials and methods

Results

Conclusions

Publications

Development and use of active clinical decision support for preemptive pharmacogenomics.

Similar Datasets

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