International Journal of Clinical Pharmacy

, Volume 36, Issue 3, pp 519–525 | Cite as

Performance of a clinical decision support system and of clinical pharmacists in preventing drug–drug interactions on a geriatric ward

  • Pieter CornuEmail author
  • Stephane Steurbaut
  • Sabina Šoštarić
  • Aleš Mrhar
  • Alain G. Dupont
Research Article


Background Drug–drug interactions (DDIs) can lead to adverse drug events and compromise patient safety. Two common approaches to reduce these interactions in hospital practice are the use of clinical decision support systems and interventions by clinical pharmacists. Objective To compare the performance of both approaches with the main objective of learning from one approach to improve the other. Setting Acute geriatric ward in a university hospital. Methods Prospective single-centre, cohort study of patients admitted to the geriatric ward. An independent pharmacist compared the clinical decision support alerts with the DDIs identified by clinical pharmacists and evaluated their interventions. Contextual factors used by the clinical pharmacists for evaluation of the clinical relevance were analysed. Adverse drug events related to DDIs were investigated and the causality was evaluated by a clinical pharmacologist based on validated criteria. Main outcome measure Number of alerts, interventions and the acceptance rates. Results Fifty patients followed by the clinical pharmacists, were included. The clinical pharmacists identified 240 DDIs (median of 3.5 per patient) and advised a therapy change for 16 of which 13 (81.2 %) were accepted and three (18.8 %) were not. The decision support system generated only six alerts of which none were accepted by the physicians. Thirty-seven adverse drug events were identified for 29 patients that could be related to 55 DDIs. For two interactions the causality was evaluated as certain, for 31 as likely, for ten as possible and for 12 as unlikely. Mainly intermediate level interactions were related to adverse drug events. Contextual factors taken into account by the clinical pharmacists for evaluation of the interactions were blood pressure, international normalised ratio, heart rate, potassium level and glycemia. Additionally, the clinical pharmacists looked at individual administration intervals and drug sequence to determine the clinical relevance of the interactions. Conclusion Clinical pharmacists performed better than the decision support system mainly because the system screened only for high level DDIs and because of the low specificity of the alerts. This specificity can be increased by including contextual factors into the logic and by defining appropriate screening intervals that take into account the sequence in which the drugs are given.


Adverse drug events Belgium Clinical decision support systems Clinical pharmacy Computerized physician order entry system Drug–drug interactions Geriatrics Medication safety 



We thank Claudine Ligneel, Tinne Leysen, Eva De Baere and Hilde De Ridder for their support as clinical pharmacists.


The study was performed with the support of the Agency for Innovation by Science and Technology in Flanders, Belgium, which provided a research grant for the first author. The funding agreement ensured the authors’ independence in designing the study, interpreting the data, writing, and publishing the report.

Conflicts of interest

The authors have no conflicts of interest regarding this study.


  1. 1.
    Classen DC, Phansalkar S, Bates DW. Critical drug–drug interactions for use in electronic health records systems with computerized physician order entry: review of leading approaches. J Patient Saf. 2011;7(2):61–5.PubMedCrossRefGoogle Scholar
  2. 2.
    Espinosa-Bosch M, Santos-Ramos B, Gil-Navarro MV, Santos-Rubio MD, Marin-Gil R, Villacorta-Linaza P. Prevalence of drug interactions in hospital healthcare. Int J Clin Pharm. 2012;34(6):807–17.PubMedCrossRefGoogle Scholar
  3. 3.
    Kuperman GJ, Bobb A, Payne TH, Avery AJ, Gandhi TK, Burns G, et al. Medication-related clinical decision support in computerized provider order entry systems: a review. J Am Med Inform Assoc. 2007;14(1):29–40.PubMedCentralPubMedCrossRefGoogle Scholar
  4. 4.
    Osheroff JA. Improving medication use and outcomes with clinical decision support: a step-by-step guide. Chicago: The Healthcare Information and Management Systems Society; 2009.Google Scholar
  5. 5.
    Horn JR, Gumpper KF, Hardy JC, McDonnell PJ, Phansalkar S, Reilly C. Clinical decision support for drug–drug interactions: improvement needed. Am J Health Syst Pharm. 2013;70(10):905–9.PubMedCrossRefGoogle Scholar
  6. 6.
    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.PubMedCentralPubMedCrossRefGoogle Scholar
  7. 7.
    van der Sijs H, Mulder A, van Gelder T, Aarts J, Berg M, Vulto A. Drug safety alert generation and overriding in a large Dutch university medical centre. Pharmacoepidemiol Drug Saf. 2009;18(10):941–7.PubMedCrossRefGoogle Scholar
  8. 8.
    van Zwart Rijkom JE, Uijtendaal UV, ten Berg MJ MJ, van Solinge WW, Egberts AC. Frequency and nature of drug–drug interactions in a Dutch university hospital. Br J Clin Pharmacol. 2009;68(2):187–93.CrossRefGoogle Scholar
  9. 9.
    Duke JD, Li X, Dexter P. Adherence to drug–drug interaction alerts in high-risk patients: a trial of context-enhanced alerting. J Am Med Inform Assoc. 2013;20(3):494–8.PubMedCentralPubMedCrossRefGoogle Scholar
  10. 10.
    Payne TH, Nichol WP, Hoey P, Savarino J. Characteristics and override rates of order checks in a practitioner order entry system. Proc AMIA Symp. 2002; 602–6.Google Scholar
  11. 11.
    Shah NR, Seger AC, Seger DL, Fiskio JM, Kuperman GJ, Blumenfeld B, et al. Improving acceptance of computerized prescribing alerts in ambulatory care. J Am Med Inform Assoc. 2006;13(1):5–11.PubMedCentralPubMedCrossRefGoogle Scholar
  12. 12.
    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.PubMedCentralPubMedCrossRefGoogle Scholar
  13. 13.
    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.PubMedCentralPubMedCrossRefGoogle Scholar
  14. 14.
    Phansalkar S, van der Sijs H, Tucker AD, Desai AA, Bell DS, Teich JM, et al. Drug–drug interactions that should be non-interruptive in order to reduce alert fatigue in electronic health records. J Am Med Inform Assoc. 2013;20(3):489–93.PubMedCentralPubMedCrossRefGoogle Scholar
  15. 15.
    Duke JD, Bolchini D. A successful model and visual design for creating context-aware drug–drug interaction alerts. AMIA Annu Symp Proc. 2011;2011:339–48.PubMedCentralPubMedGoogle Scholar
  16. 16.
    Berner ES. Clinical decision support systems: state of the Art. Rockville, Maryland: agency for healthcare research and quality, U.S. Department of Health and Human Services; 2009.Google Scholar
  17. 17.
    Phansalkar S, Edworthy J, Hellier E, Seger DL, Schedlbauer A, Avery AJ, et al. A review of human factors principles for the design and implementation of medication safety alerts in clinical information systems. J Am Med Inform Assoc. 2010;17(5):493–501.PubMedCentralPubMedCrossRefGoogle Scholar
  18. 18.
    Kaboli PJ, Hoth AB, McClimon BJ, Schnipper JL. Clinical pharmacists and inpatient medical care: a systematic review. Arch Intern Med. 2006;166(9):955–64.PubMedCrossRefGoogle Scholar
  19. 19.
    Bosma L, Jansman FG, Franken AM, Harting JW, Van den Bemt PM. Evaluation of pharmacist clinical interventions in a Dutch hospital setting. Pharm World Sci. 2008;30(1):31–8.PubMedGoogle Scholar
  20. 20.
    Lea M, Rognan SE, Koristovic R, Wyller TB, Molden E. Severity and management of drug–drug interactions in acute geriatric patients. Drug Aging. 2013;30(9):721–7.CrossRefGoogle Scholar
  21. 21.
    Kucukarslan SN, Peters M, Mlynarek M, Nafziger DA. Pharmacists on rounding teams reduce preventable adverse drug events in hospital general medicine units. Arch Intern Med. 2003;163(17):2014–8.PubMedCrossRefGoogle Scholar
  22. 22.
    Cornu P, Steurbaut S, Leysen T, De Baere E, Ligneel C, Mets T, et al. Effect of medication reconciliation at hospital admission on medication discrepancies during hospitalization and at discharge for geriatric patients. Ann Pharmacother. 2012;46(4):484–94.PubMedCrossRefGoogle Scholar
  23. 23.
    Khalili H, Karimzadeh I, Mirzabeigi P, Dashti-Khavidaki S. Evaluation of clinical pharmacist’s interventions in an infectious diseases ward and impact on patient’s direct medication cost. Eur J Intern Med. 2013;24(3):227–33.PubMedCrossRefGoogle Scholar
  24. 24.
    Hasan SS, Lim KN, Anwar M, Sathvik BS, Ahmadi K, Yuan AW, et al. Impact of pharmacists’ intervention on identification and management of drug–drug interactions in an intensive care setting. Singap Med J. 2012;53(8):526–31.Google Scholar
  25. 25.
    Steurbaut S, Leemans L, Leysen T, De Baere E, Cornu P, Mets T, et al. Medication history reconciliation by clinical pharmacists in elderly inpatients admitted from home or a nursing home. Ann Pharmacother. 2010;44(10):1596–603.PubMedCrossRefGoogle Scholar
  26. 26.
    Cornu P, Steurbaut S, Leysen T, De Baere E, Ligneel C, Mets T, et al. Discrepancies in medication information for the primary care physician and the geriatric patient at discharge. Ann Pharmacother. 2012;46(7–8):983–90.PubMedCrossRefGoogle Scholar
  27. 27.
    Zaal RJ, Jansen MM, Duisenberg-van Essenberg M, Tijssen CC, Roukema JA, van den Bemt PM. Identification of drug-related problems by a clinical pharmacist in addition to computerized alerts. Int J Clin Pharm. 2013.Google Scholar
  28. 28.
    Van de Velde R. Framework for a clinical information system. Int J Med Inform. 2000;57(1):57–72.PubMedCrossRefGoogle Scholar
  29. 29.
    Lanssiers R, Everaert E, De Win M, Van De Velde R, De Clercq H. An integrated drug prescription and distribution system: challenges and opportunities. Stud Health Technol Inform. 2002;93:69–74.PubMedGoogle Scholar
  30. 30.
    APB DelphiCare 2013;; 17 Oct 2013.
  31. 31.
    UpToDate, Lexi-Comp Online: Lexi-Interact, 2013;; 17 Oct 2013.
  32. 32.
    Press TP, Stockley’s Interaction Alerts, 2013;; 17 Oct 2013.
  33. 33.
    Nebeker JR, Barach P, Samore MH. Clarifying adverse drug events: a clinician’s guide to terminology, documentation, and reporting. Ann Intern Med. 2004;140(10):795–801.PubMedCrossRefGoogle Scholar
  34. 34.
    Edwards IR, Aronson JK. Adverse drug reactions: definitions, diagnosis, and management. Lancet. 2000;356(9237):1255–9.PubMedCrossRefGoogle Scholar
  35. 35.
    Resetar E, Reichley RM, Noirot LA, Dunagan WC, Bailey TC. Customizing a commercial rule base for detecting drug–drug interactions. AMIA Annu Symp Proc. 2005;1094.Google Scholar
  36. 36.
    Mollon B, Chong JJ, Holbrook AM, Sung M, Thabane L, Foster G. Features predicting the success of computerized decision support for prescribing: a systematic review of randomized controlled trials. BMC Med Inform Decis Mak. 2009;9:11.PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© Koninklijke Nederlandse Maatschappij ter bevordering der Pharmacie 2014

Authors and Affiliations

  • Pieter Cornu
    • 1
    Email author
  • Stephane Steurbaut
    • 1
  • Sabina Šoštarić
    • 2
  • Aleš Mrhar
    • 2
  • Alain G. Dupont
    • 1
  1. 1.Faculty of Medicine and Pharmacy, Research Group Clinical Pharmacology and Clinical Pharmacy (KFAR)Vrije Universiteit BrusselBrusselsBelgium
  2. 2.Faculty of PharmacyUniversity of LjubljanaLjubljanaSlovenia

Personalised recommendations