title: Use of an on-demand drug-drug interaction checker by prescribers and consultants: a retrospective analysis in a Swiss teaching hospital.
abstract: BACKGROUND
  Offering a drug-drug interaction (DDI) checker on-demand instead of computer-triggered alerts is a strategy to avoid alert fatigue.
  
  
  OBJECTIVE
  The purpose was to determine the use of such an on-demand tool, implemented in the clinical information system for inpatients.
  
  
  METHODS
  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.
  
  
  RESULTS
  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).
  
  
  CONCLUSIONS
  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.
authors.affiliation: Research Center for Medical Informatics, Directorate of Research and Teaching, University Hospital Zurich, Sonneggstrasse 6, D5, 8091 Zurich, Switzerland.
authors.forename: Patrick Emanuel
authors.initials: PE
authors.lastname: Beeler
authors.forename: Emmanuel
authors.initials: E
authors.lastname: Eschmann
authors.forename: Christoph
authors.initials: C
authors.lastname: Rosen
authors.forename: Jürg
authors.initials: J
authors.lastname: Blaser
mesh_terms: D001291:Attitude of Health Personnel; D062313:Databases, Pharmaceutical; D020000:Decision Support Systems, Clinical; D004347:Drug Interactions; D064420:Drug-Related Side Effects and Adverse Reactions / Q000517:prevention & control*; D006784:Hospitals, Teaching; D006801:Humans; D057226:Information Seeking Behavior; D007388:Internal Medicine; D019300:Medical Errors / Q000517:prevention & control*; D005222:Mental Fatigue / Q000517:prevention & control; D010601:Pharmacology, Clinical; D010820:Physicians; D012189:Retrospective Studies; D012984:Software; D013043:Specialties, Surgical; D013557:Switzerland; D000078329:Workforce