Drug Safety

, Volume 38, Issue 2, pp 197–206 | Cite as

Consensus Recommendations for Systematic Evaluation of Drug–Drug Interaction Evidence for Clinical Decision Support

  • Richard T. Scheife
  • Lisa E. Hines
  • Richard D. Boyce
  • Sophie P. Chung
  • Jeremiah D. Momper
  • Christine D. Sommer
  • Darrell R. Abernethy
  • John R. Horn
  • Stephen J. Sklar
  • Samantha K. Wong
  • Gretchen Jones
  • Mary L. Brown
  • Amy J. Grizzle
  • Susan Comes
  • Tricia Lee Wilkins
  • Clarissa Borst
  • Michael A. Wittie
  • Daniel C. Malone
Original Research Article

Abstract

Background

Healthcare organizations, compendia, and drug knowledgebase vendors use varying methods to evaluate and synthesize evidence on drug–drug interactions (DDIs). This situation has a negative effect on electronic prescribing and medication information systems that warn clinicians of potentially harmful medication combinations.

Objective

The aim of this study was to provide recommendations for systematic evaluation of evidence for DDIs from the scientific literature, drug product labeling, and regulatory documents.

Methods

A conference series was conducted to develop a structured process to improve the quality of DDI alerting systems. Three expert workgroups were assembled to address the goals of the conference. The Evidence Workgroup consisted of 18 individuals with expertise in pharmacology, drug information, biomedical informatics, and clinical decision support. Workgroup members met via webinar 12 times from January 2013 to February 2014. Two in-person meetings were conducted in May and September 2013 to reach consensus on recommendations.

Results

We developed expert consensus answers to the following three key questions. (i) What is the best approach to evaluate DDI evidence? (ii) What evidence is required for a DDI to be applicable to an entire class of drugs? (iii) How should a structured evaluation process be vetted and validated?

Conclusion

Evidence-based decision support for DDIs requires consistent application of transparent and systematic methods to evaluate the evidence. Drug compendia and clinical decision support systems in which these recommendations are implemented should be able to provide higher-quality information about DDIs.

Supplementary material

40264_2014_262_MOESM1_ESM.pdf (109 kb)
Supplementary material 1 (PDF 109 kb)
40264_2014_262_MOESM2_ESM.pdf (100 kb)
Supplementary material 2 (PDF 100 kb)
40264_2014_262_MOESM3_ESM.pdf (89 kb)
Supplementary material 3 (PDF 89 kb)
40264_2014_262_MOESM4_ESM.pdf (96 kb)
Supplementary material 4 (PDF 97 kb)

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Richard T. Scheife
    • 1
  • Lisa E. Hines
    • 2
  • Richard D. Boyce
    • 3
  • Sophie P. Chung
    • 4
  • Jeremiah D. Momper
    • 5
  • Christine D. Sommer
    • 6
  • Darrell R. Abernethy
    • 7
  • John R. Horn
    • 8
  • Stephen J. Sklar
    • 9
  • Samantha K. Wong
    • 10
  • Gretchen Jones
    • 4
  • Mary L. Brown
    • 2
  • Amy J. Grizzle
    • 2
  • Susan Comes
    • 4
  • Tricia Lee Wilkins
    • 11
  • Clarissa Borst
    • 12
  • Michael A. Wittie
    • 11
  • Daniel C. Malone
    • 2
  1. 1.Tufts University School of MedicineBostonUSA
  2. 2.University of Arizona College of PharmacyTucsonUSA
  3. 3.Department of Biomedical InformaticsUniversity of PittsburghPittsburghUSA
  4. 4.Epocrates, athenahealth, Inc.San FranciscoUSA
  5. 5.University of California, San Diego, Skaggs School of Pharmacy and Pharmaceutical SciencesLa JollaUSA
  6. 6.FDB (First Databank, Inc.)South San FranciscoUSA
  7. 7.Office of Clinical Pharmacology, U.S. Food and Drug AdministrationSilver SpringsUSA
  8. 8.University of Washington School of PharmacySeattleUSA
  9. 9.Wolters Kluwer HealthIndianapolisUSA
  10. 10.Cerner MultumDenverUSA
  11. 11.U.S. Office of the National Coordinator for Health Information TechnologyWashington, DCUSA
  12. 12.Elsevier Clinical SolutionsTampaUSA

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