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

BackgroundOffering a drug–drug interaction (DDI) checker on-demand instead of computer-triggered alerts is a strategy to avoid alert fatigue.ObjectiveThe purpose was to determine the use of such an on-demand tool, implemented in the clinical information system for inpatients.MethodsThe 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.ResultsThe 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).ConclusionsThe 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.

[1]  Edward P. Armstrong,et al.  Identification of serious drug-drug interactions: results of the partnership to prevent drug-drug interactions. , 2004, Journal of the American Pharmacists Association : JAPhA.

[2]  Paul R Dexter,et al.  Computerized decision support for medication dosing in renal insufficiency: a randomized, controlled trial. , 2010, Annals of emergency medicine.

[3]  T K Hazlet,et al.  ORCA: OpeRational ClassificAtion of drug interactions. , 2001, Journal of the American Pharmaceutical Association.

[4]  M. Oertle.  Frequency and nature of drug-drug interactions in a Swiss primary and secondary acute care hospital. , 2012, Swiss medical weekly.

[5]  L. Kohn,et al.  To Err Is Human : Building a Safer Health System , 2007 .

[6]  E. Oren,et al.  Impact of emerging technologies on medication errors and adverse drug events. , 2003, American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists.

[7]  R. Hamilton,et al.  Frequency of Hospitalization after Exposure to Known Drug‐Drug Interactions in a Medicaid Population , 1998, Pharmacotherapy.

[8]  Willemijn L. Eppenga,et al.  Comparison of a basic and an advanced pharmacotherapy-related clinical decision support system in a hospital care setting in the Netherlands , 2012, J. Am. Medical Informatics Assoc..

[9]  Charles E. Leonard,et al.  Unintended effects of a computerized physician order entry nearly hard-stop alert to prevent a drug interaction: a randomized controlled trial. , 2010, Archives of internal medicine.

[10]  Diane L. Seger,et al.  Viewpoint Paper: Tiering Drug-Drug Interaction Alerts by Severity Increases Compliance Rates , 2009, J. Am. Medical Informatics Assoc..

[11]  A. Wall,et al.  Book ReviewTo Err is Human: building a safer health system Kohn L T Corrigan J M Donaldson M S Washington DC USA: Institute of Medicine/National Academy Press ISBN 0 309 06837 1 $34.95 , 2000 .

[12]  B. Mannheimer,et al.  Drug–drug interactions that reduce the formation of pharmacologically active metabolites: a poorly understood problem in clinical practice , 2010, Journal of internal medicine.

[13]  Robyn Tamblyn,et al.  A randomized trial of the effectiveness of on-demand versus computer-triggered drug decision support in primary care. , 2008, Journal of the American Medical Informatics Association : JAMIA.

[14]  Gordon Schiff,et al.  Electronic Drug Interaction Alerts in Ambulatory Care , 2011, Drug safety.

[15]  D. Bates,et al.  Effect of computerized physician order entry and a team intervention on prevention of serious medication errors. , 1998, JAMA.

[16]  E. Balas,et al.  Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success , 2005, BMJ : British Medical Journal.

[17]  Marc Berg,et al.  Research Paper: Turning Off Frequently Overridden Drug Alerts: Limited Opportunities for Doing It Safely , 2008, J. Am. Medical Informatics Assoc..

[18]  Receptivity of Physicians in a Teaching Hospital to a Computerized Drug Interaction Monitoring and Reporting System , 1977, Medical care.

[19]  Bruce Kaplan,et al.  Evaluation of Drug Interaction Software to Identify Alerts for Transplant Medications , 2005, The Annals of pharmacotherapy.

[20]  Thomas H. Payne,et al.  Characteristics and override rates of order checks in a practitioner order entry system , 2002, AMIA.

[21]  Jens Kaltschmidt,et al.  Successful strategy to improve the specificity of electronic statin–drug interaction alerts , 2009, European Journal of Clinical Pharmacology.

[22]  David W. Bates,et al.  High-priority drug-drug interactions for use in electronic health records , 2012, J. Am. Medical Informatics Assoc..

[23]  Diane L. Seger,et al.  Factors influencing alert acceptance: a novel approach for predicting the success of clinical decision support , 2011, J. Am. Medical Informatics Assoc..

[24]  Marc Berg,et al.  Overriding of drug safety alerts in computerized physician order entry. , 2006, Journal of the American Medical Informatics Association : JAMIA.

[25]  Barbara Simon,et al.  Exposure to Automated Drug Alerts Over Time: Effects On Clinicians’ Knowledge And Perceptions , 2006, Medical care.

[26]  A. Egberts,et al.  Frequency of and Risk Factors for Preventable Medication-Related Hospital Admissions in the Netherlands , 2009 .

[27]  Michael Weller,et al.  Comparative evaluation of the drug interaction screening programs MediQ and ID PHARMA CHECK in neurological inpatients , 2012, Pharmacoepidemiology and drug safety.

[28]  C. Marano,et al.  To err is human. Building a safer health system , 2005 .

[29]  Tuomas Korhonen,et al.  SFINX—a drug-drug interaction database designed for clinical decision support systems , 2009, European Journal of Clinical Pharmacology.

[30]  P. Beeler,et al.  Sustained impact of electronic alerts on rate of prophylaxis against venous thromboembolism , 2011, Thrombosis and Haemostasis.

[31]  Social contexts of sports-practicing youths' hazardous drinking. , 2012, Swiss medical weekly.

[32]  Amy L. Seybert,et al.  Grading the Severity of Drug-Drug Interactions in the Intensive Care Unit: A Comparison Between Clinician Assessment and Proprietary Database Severity Rankings , 2010, The Annals of pharmacotherapy.

[33]  Amy J Grizzle,et al.  Concordance of severity ratings provided in four drug interaction compendia. , 2004, Journal of the American Pharmacists Association : JAPhA.

[34]  Donna C. Dare,et al.  Reasons provided by prescribers when overriding drug-drug interaction alerts. , 2007, The American journal of managed care.