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. MaloneEmail author
Original Research Article



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.


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.


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.


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?


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.


Clinical Decision Support Clinical Decision Support System Narrow Therapeutic Index Drug Computerize Clinical Decision Support System Drug Interaction Probability Scale 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This project was funded by the Agency for Healthcare Research and Quality (AHRQ) Grant No. 1R13HS021826-01 (Malone DC-PI). RDB received support to contribute to this project from AHRQ Grant No. K12 HS019461-01, National Library of Medicine Grant No. R01 LM011838-01, and National Institute of Aging Grant No. K01 AG044433-01. Additional support was provided by Cerner, Elsevier Clinical Solutions, Epocrates, athenahealth, Inc., FDB (First Databank, Inc.), Truven Health Analytics, and Wolters Kluwer. The authors would like to thank Alissa Rich for her participation in workgroup discussions and Loretta Peters for her editorial assistance in preparing this manuscript. We would like to acknowledge that John Horn developed the Drug Interaction Probability Scale that is recommended for use in evaluating drug interaction case reports.

Conflicts of interest

Lisa E. Hines, Mary Brown, and Amy J. Grizzle have no conflicts of interest to report except for grant support and travel support from the University of Arizona. Richard T. Scheife, Richard D. Boyce, John Horn and Jeremiah D. Momper have no conflicts of interest to report except for travel support from the University of Arizona to attend workgroup meetings. Christine D. Sommer is an employee of FDB, which supported the travel to workgroup meetings and reviewed the manuscript during business hours. Stephen J. Sklar is an employee of Wolters Kluwer, which provided a portion of financial support for this project and provided support for his travel to meetings. Sophie P. Chung and Susan Comes are employees of Epocrates, athenahealth, Inc., which paid for their travel to workgroup meetings. Gretchen Jones was employed by Epocrates, athenahealth, Inc., which paid for the travel to meetings, and she holds stock in that company. Clarissa Borst is an employee of Elsevier, which provided support to the conference. Daniel C. Malone received support from the University of Arizona to conduct this project, which was partially funded by a conference grant from the Agency for Healthcare Research and Quality. Michael A. Wittie, Darrell R. Abernethy, Samantha K. Wong and Tricia Lee Wilkins have no conflicts of interest that are directly relevant to the content of this study.

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
    Email author
  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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