Development of an inpatient operational pharmacy productivity model.

PURPOSE: An innovative model for measuring the operational productivity of medication order management in inpatient settings is described. METHODS: Order verification within a computerized prescriber order-entry system was chosen as the pharmacy workload driver. To account for inherent variability in the tasks involved in processing different types of orders, pharmaceutical products were grouped by class, and each class was assigned a time standard, or "medication complexity weight" reflecting the intensity of pharmacist and technician activities (verification of drug indication, verification of appropriate dosing, adverse-event prevention and monitoring, medication preparation, product checking, product delivery, returns processing, nurse/provider education, and problem-order resolution). The resulting "weighted verifications" (WV) model allows productivity monitoring by job function (pharmacist versus technician) to guide hiring and staffing decisions. A 9-month historical sample of verified medication orders was analyzed using the WV model, and the calculations were compared with values derived from two established models­one based on the Case Mix Index (CMI) and the other based on the proprietary Pharmacy Intensity Score (PIS). RESULTS: Evaluation of Pearson correlation coefficients indicated that values calculated using the WV model were highly correlated with those derived from the CMI-and PIS-based models (r = 0.845 and 0.886, respectively). Relative to the comparator models, the WV model offered the advantage of less period-to-period variability. CONCLUSION: The WV model yielded productivity data that correlated closely with values calculated using two validated workload management models. The model may be used as an alternative measure of pharmacy operational productivity.

Interrater agreement with a standard scheme for classifying medication errors.

PURPOSE: The interrater agreement for and reliability of the National Coordinating Council for Medication Error Reporting and Prevention (NCC MERP) index for categorizing medication errors were determined. METHODS: A letter was sent by the U.S. Pharmacopeia to all 550 contacts in the MEDMARX system user database. Participants were asked to categorize 27 medication scenarios using the NCC MERP index and were randomly assigned to one of three tools (the index alone, a paper-based algorithm, or a computer-based algorithm) to assist in categorization. Because the NCC MERP index accounts for harm and cost, and because categories could be interpreted as substantially similar, study results were analyzed after the nine error categories were collapsed to six. The interrater agreement was measured using Cohen's kappa value. RESULTS: Of 119 positive responses, 101 completed surveys were returned for a response rate of 85%. There were no significant differences in baseline demographics among the three groups. The overall interrater agreement for the participants, regardless of group assignment, was substantial at 0.61 (95% confidence interval [CI], 0.41-0.81). There was no difference among the kappa values of the three study groups and the tools used to aid in medication error classification. When the index was condensed from nine categories to six, the interrater agreement increased with a kappa value of 0.74 (95% CI, 0.56-0.90). CONCLUSION: Overall interrater agreement for the NCC MERP index for categorizing medication errors was substantial. The tool provided to assist with categorization did not influence overall categorization. Further refining of the scale could improve the usefulness and validity of medication error categorization.