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Purpose Medication-error alerts for warfarin orders detected by a bar-code-assisted medication administration (BCMA) system were evaluated. Methods All patients receiving warfarin who were admitted to a university medical center between July 1, 2008, and February 6, 2009, in inpatient units with BCMA systems were candidates for inclusion in this study. Medication-error alerts displayed to the nurse administering the warfarin were reviewed to determine whether a true potential error was detected. Each alert was converted to a scenario, and its potential to require treatment or cause patient harm was rated using a validated severity scale of 0–10, where a score of 0 indicated no probable effect on the patient and 10 indicated that the error would likely result in patient death. A severity score was obtained by averaging the scores of four pharmacist reviewers. Results Of the 18,393 warfarin doses ordered during the study period for 2,404 patients, error alerts associated with only 99 warfarin doses were found to be clinically meaningful. The mean ± S.D. severity rating of these alerts was low (2.93 ± 1.42), with a standardized Cronbach’s coefficient alpha of 0.845. The mean ± S.D. warfarin dose attempted when the nurse received an alert was 4.10 ± 2.48 mg. The majority of doses with alerts (70%) were for patients who had an active order for warfarin. Conclusion Of the large number of medication-error alerts generated through a BCMA system, only a small proportion were considered clinically significant. This indicated that the rate of false-positive alerts was unexpectedly high, increasing the risk of alert fatigue.

Background Up to 38% of inpatient medication errors occur at the administration stage. Although they reduce prescribing errors, computerized provider order entry (CPOE) systems do not prevent administration errors or timing discrepancies. This study determined the degree to which CPOE medication orders matched actual dose administration times. METHODS At a 658-bed academic hospital with CPOE but lacking electronic medication administration charting, authors randomly selected adult patients with eligible medication orders from historical 1999-2003 CPOE log files. Retrospective manual chart audits compared expected (from CPOE) and actual timing of medication administrations. Outcomes included: dose omissions, median lag times between ordered and charted administrations, unauthorized doses, wrong dose errors, and the rate of nurses' medication schedule shifting. RESULTS Dose omissions occurred in 756 of 6019 (12.6%) audited administration opportunities; only 313 of the omissions (5.2% of opportunities) were unexplained. Wrong doses and unexpected doses occurred for 0.1% and 0.7% of opportunities, respectively. Median lag from expected first dose to actual charted administration time was 27 minutes (IQR 0-127). Nursing staff shifted from ordered to alternate administration schedules for 10.7% of regularly scheduled recurring medication orders. Chart review identified reasons for dose omissions, delays, and dose shifting. CONCLUSION Inpatient CPOE orders are legible and conveyed electronically to nurses and the pharmacy. Nonetheless, ward-based medication administrations do not consistently occur as ordered. Medication administration discrepancies are likely to persist even after implementing CPOE and bar-coded medication administration unless recommended interventions are made to address issues such as determining the true urgency of medication administration, avoiding overlapping duplicative medication orders, and developing a safe means for shifting dosing schedules.

To facilitate the future implementation of an electronic prescribing system, this case study modeled prescription management processes in various primary care settings. The Vanderbilt e-prescribing design team conducted initial interviews with clinic managers, physicians and nurses, and then represented the sequences of steps carried out to complete prescriptions in activity diagrams. The diagrams covered outpatient prescribing for patients during a clinic visit and between clinic visits. Practice size, practice setting, and practice specialty type influenced the prescribing processes used. The model developed may be useful to others engaged in building or tailoring an e-prescribing system to meet the specific workflows of various clinic settings.