Drug Safety

, Volume 36, Issue 6, pp 427–434 | Cite as

Use of an On-demand Drug–Drug Interaction Checker by Prescribers and Consultants: A Retrospective Analysis in a Swiss Teaching Hospital

  • Patrick Emanuel Beeler
  • Emmanuel Eschmann
  • Christoph Rosen
  • Jürg BlaserEmail author
Short Communication



Offering a drug–drug interaction (DDI) checker on-demand instead of computer-triggered alerts is a strategy to avoid alert fatigue.


The purpose was to determine the use of such an on-demand tool, implemented in the clinical information system for inpatients.


The study was conducted at the University Hospital Zurich, an 850-bed teaching hospital. The hospital-wide use of the on-demand DDI checker was measured for prescribers and consulting pharmacologists. The number of DDIs identified on-demand was compared to the number that would have resulted by computer-triggering and this was compared to patient-specific recommendations by a consulting pharmacist.


The on-demand use was analyzed during treatment of 64,259 inpatients with 1,316,884 prescriptions. The DDI checker was popular with nine consulting pharmacologists (648 checks/consultant). A total of 644 prescribing physicians used it infrequently (eight checks/prescriber). Among prescribers, internists used the tool most frequently and obtained higher numbers of DDIs per check (1.7) compared to surgeons (0.4). A total of 16,553 DDIs were identified on-demand, i.e., <10 % of the number the computer would have triggered (169,192). A pharmacist visiting 922 patients on a medical ward recommended 128 adjustments to prevent DDIs (0.14 recommendations/patient), and 76 % of them were applied by prescribers. In contrast, computer-triggering the DDI checker would have resulted in 45 times more alerts on this ward (6.3 alerts/patient).


The on-demand DDI checker was popular with the consultants only. However, prescribers accepted 76 % of patient-specific recommendations by a pharmacist. The prescribers’ limited on-demand use indicates the necessity for developing improved safety concepts, tailored to suit these consumers. Thus, different approaches have to satisfy different target groups.


Knowledge Base Clinical Decision Support Clinical Decision Support System Computerize Physician Order Entry Clinical Information System 
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.



No funding was received for the conduct of this study. The authors declared no conflict of interest.

Author Contributions

All authors contributed to the conception and planning of the work, analyzed and interpreted data, contributed to the drafting and critical revision of the manuscript, and all authors approved the final submitted version.


  1. 1.
    Committee on Quality of Health Care in America, Institute of Medicine. To err is human: building a safer health system. Washington: National Academies Press; 2000.Google Scholar
  2. 2.
    Leendertse AJ, Egberts AC, Stoker LJ, van den Bemt PM. Frequency of and risk factors for preventable medication-related hospital admissions in the Netherlands. Arch Intern Med. 2008;168(17):1890–6.PubMedCrossRefGoogle Scholar
  3. 3.
    Hamilton RA, Briceland LL, Andritz MH. Frequency of hospitalization after exposure to known drug–drug interactions in a Medicaid population. Pharmacotherapy. 1998;18(5):1112–20.PubMedGoogle Scholar
  4. 4.
    Bates DW, Leape LL, Cullen DJ, Laird N, Petersen LA, Teich JM, et al. Effect of computerized physician order entry and a team intervention on prevention of serious medication errors. JAMA. 1998;280(15):1311–6.PubMedCrossRefGoogle Scholar
  5. 5.
    van der Sijs H, Aarts J, Vulto A, Berg M. Overriding of drug safety alerts in computerized physician order entry. J Am Med Inform Assoc. 2006;13(2):138–47.PubMedCrossRefGoogle Scholar
  6. 6.
    Payne TH, Nichol WP, Hoey P, Savarino J. Characteristics and override rates of order checks in a practitioner order entry system. In: Proceedings of the AMIA symposium, University of Washington, Seattle, WA, USA; 2002. pp. 602–6.Google Scholar
  7. 7.
    Tamblyn R, Huang A, Taylor L, Kawasumi Y, Bartlett G, Grad R, et al. A randomized trial of the effectiveness of on-demand versus computer-triggered drug decision support in primary care. J Am Med Inform Assoc. 2008;15(4):430–8.PubMedCrossRefGoogle Scholar
  8. 8.
    Oren E, Shaffer ER, Guglielmo BJ. Impact of emerging technologies on medication errors and adverse drug events. Am J Health Syst Pharm. 2003;60(14):1447–58.PubMedGoogle Scholar
  9. 9.
    Seidling HM, Phansalkar S, Seger DL, Paterno MD, Shaykevich S, Haefeli WE, et al. Factors influencing alert acceptance: a novel approach for predicting the success of clinical decision support. J Am Med Inform Assoc. 2011;18(4):479–84.PubMedCrossRefGoogle Scholar
  10. 10.
    Strom BL, Schinnar R, Aberra F, Bilker W, Hennessy S, Leonard CE, et al. Unintended effects of a computerized physician order entry nearly hard-stop alert to prevent a drug interaction a randomized controlled trial. Arch Intern Med. 2010;170(17):1578–83.PubMedCrossRefGoogle Scholar
  11. 11.
    Phansalkar S, Desai AA, Bell D, Yoshida E, Doole J, Czochanski M, et al. High-priority drug–drug interactions for use in electronic health records. J Am Med Inform Assoc. 2012;19(5):735–43.PubMedCrossRefGoogle Scholar
  12. 12.
    Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ. 2005;330(7494):765.PubMedCrossRefGoogle Scholar
  13. 13.
    Mannheimer B, Eliasson E. Drug–drug interactions that reduce the formation of pharmacologically active metabolites: a poorly understood problem in clinical practice. J Intern Med. 2010;268(6):540–8.PubMedCrossRefGoogle Scholar
  14. 14.
    Paterno MD, Maviglia SM, Gorman PN, Seger DL, Yoshida E, Seger AC, et al. Tiering drug–drug interaction alerts by severity increases compliance rates. J Am Med Inform Assoc. 2009;16(1):40–6.PubMedCrossRefGoogle Scholar
  15. 15.
    Hansten PD, Horn JR, Hazlet TK. ORCA: OpeRational ClassificAtion of drug interactions. J Am Pharm Assoc (Wash). 2001;41(2):161–5.Google Scholar
  16. 16.
    Beeler PE, Kucher N, Blaser J. Sustained impact of electronic alerts on rate of prophylaxis against venous thromboembolism. Thromb Haemost. 2011;106(4):734–8.PubMedCrossRefGoogle Scholar
  17. 17.
    Weingart SN, Seger AC, Feola N, Heffernan J, Schiff G, Isaac T. Electronic drug interaction alerts in ambulatory care: the value and acceptance of high-value alerts in US medical practices as assessed by an expert clinical panel. Drug Saf. 2011;34(7):587–93.PubMedCrossRefGoogle Scholar
  18. 18.
    Oertle M. Frequency and nature of drug–drug interactions in a Swiss primary and secondary acute care hospital. Swiss Med Wkly. 2012;142:0.PubMedGoogle Scholar
  19. 19.
    Morrell J, Podlone M, Cohen SN. Receptivity of physicians in a teaching hospital to a computerized drug interaction monitoring and reporting system. Med Care. 1977;15(1):68–78.PubMedCrossRefGoogle Scholar
  20. 20.
    Eppenga WL, Derijks HJ, Conemans JM, Hermens WA, Wensing M, De Smet PA. Comparison of a basic and an advanced pharmacotherapy-related clinical decision support system in a hospital care setting in the Netherlands. J Am Med Inform Assoc. 2012;19(1):66–71.PubMedCrossRefGoogle Scholar
  21. 21.
    Glassman PA, Belperio P, Simon B, Lanto A, Lee M. Exposure to automated drug alerts over time: effects on clinicians’ knowledge and perceptions. Med Care. 2006;44(3):250–6.PubMedCrossRefGoogle Scholar
  22. 22.
    van der Sijs H, Aarts J, van Gelder T, Berg M, Vulto A. Turning off frequently overridden drug alerts: limited opportunities for doing it safely. J Am Med Inform Assoc. 2008;15(4):439–48.PubMedCrossRefGoogle Scholar
  23. 23.
    Seidling HM, Storch CH, Bertsche T, Senger C, Kaltschmidt J, Walter-Sack I, et al. Successful strategy to improve the specificity of electronic statin–drug interaction alerts. Eur J Clin Pharmacol. 2009;65(11):1149–57.PubMedCrossRefGoogle Scholar
  24. 24.
    Terrell KM, Perkins AJ, Hui SL, Callahan CM, Dexter PR, Miller DK. Computerized decision support for medication dosing in renal insufficiency: a randomized, controlled trial. Ann Emerg Med. 2010;56(6):623–9.PubMedCrossRefGoogle Scholar
  25. 25.
    Abarca J, Malone DC, Armstrong EP, Grizzle AJ, Hansten PD, Van Bergen RC, et al. Concordance of severity ratings provided in four drug interaction compendia. J Am Pharm Assoc (2003). 2004;44(2):136–41.CrossRefGoogle Scholar
  26. 26.
    Zorina OI, Haueis P, Semmler A, Marti I, Gonzenbach RR, Guzek M, et al. Comparative evaluation of the drug interaction screening programs MediQ and ID PHARMA CHECK in neurological inpatients. Pharmacoepidemiol Drug Saf. 2012;21(8):872–80.PubMedCrossRefGoogle Scholar
  27. 27.
    Smith WD, Hatton RC, Fann AL, Baz MA, Kaplan B. Evaluation of drug interaction software to identify alerts for transplant medications. Ann Pharmacother. 2005;39(1):45–50.PubMedCrossRefGoogle Scholar
  28. 28.
    Smithburger PL, Kane-Gill SL, Benedict NJ, Falcione BA, Seybert AL. Grading the severity of drug–drug interactions in the intensive care unit: a comparison between clinician assessment and proprietary database severity rankings. Ann Pharmacother. 2010;44(11):1718–24.PubMedCrossRefGoogle Scholar
  29. 29.
    Bottiger Y, Laine K, Andersson ML, Korhonen T, Molin B, Ovesjo ML, et al. SFINX—a drug–drug interaction database designed for clinical decision support systems. Eur J Clin Pharmacol. 2009;65(6):627–33.PubMedCrossRefGoogle Scholar
  30. 30.
    Grizzle AJ, Mahmood MH, Ko Y, Murphy JE, Armstrong EP, Skrepnek GH, et al. Reasons provided by prescribers when overriding drug–drug interaction alerts. Am J Manag Care. 2007;13(10):573–8.PubMedGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Patrick Emanuel Beeler
    • 1
  • Emmanuel Eschmann
    • 1
  • Christoph Rosen
    • 2
  • Jürg Blaser
    • 1
    Email author
  1. 1.Research Center for Medical Informatics, Directorate of Research and TeachingUniversity Hospital ZurichZurichSwitzerland
  2. 2.Cantonal PharmacyZurichSwitzerland

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