Optimization of critical care pharmacy clinical services: A gap analysis approach

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 models—one 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.

Development of an inpatient operational pharmacy productivity model

Purpose:This article identifies, prioritizes, and summarizes the published literature on the medication use process (MUP) from calendar year 2012 that can impact health-system pharmacists’ daily practice.Summary:The MUP is the foundational system that provides the framework for safe patient care within the health care environment. The MUP is defined in this article as having the following components: prescribing/transcribing, dispensing, administration, and monitoring. A PubMed search was conducted in January 2013 for calendar year 2012 using targeted Medical Subject Headings (MeSH) keywords, providing a total of 944 articles. A thorough review identified 46 potentially significant articles: 14 for prescribing/transcribing, 12 for dispensing, 10 for administration, and 10 for monitoring. Peer review led to the selection of key articles from each category. These articles are briefly summarized, with a mention of why this article is important within health-system pharmacy. The other articles are listed for ...

Significant Publications About the Medication Use Process in 2012

Purpose The development and implementation of an operational productivity tool in an academic cancer treatment center are described. Summary Based on the increasing complexity of care delivery within the oncology setting, solutions were explored within Cleveland Clinic pharmacy’s productivity model. Data were electronically captured based on orders processed through the outpatient oncology setting, including hazardous and nonhazardous medications. Based on current workflow, inpatient and outpatient orders were reviewed in productivity metrics. The metric defining the variability of the workload itself was weighted dispense type as it was the best representation of a mixed-skill workflow. After conducting workflow process mapping, discrete measurable steps were assessed and evaluated daily. Operational components of interest included pharmacist verification activities and technician compounding activities. Historical production data were sampled for assigning relative value units (RVUs) respective to time to normalize workload into a common unit (i.e., 1 hour) and to relate work demand in a highly variable setting. RVUs were assigned and delineated by cognitive and distributive activities for pharmacists and technicians, respectively. The Cleveland Clinic department of pharmacy developed a productivity tool to retrospectively measure workload involving time to review, verify, reconstitute, admix, and deliver chemotherapeutic agents. The weighting of each medication allowed for precise and meaningful evaluation of productivity. With RVUs assigned to 2 years of operational metrics, there now exists an opportunity to monitor performance trends within the cancer treatment center pharmacy. The data are readily retrievable within the electronic health record. Conclusion The productivity data provided precise information to assess trends in operations within the pharmacy of an outpatient cancer treatment center.

Development of an operational productivity tool within a cancer treatment center pharmacy.

Hospital Pharmacy presents this feature to keep pharmacists abreast of new publications in the medical/pharmacy literature. Articles of interest regarding a broad scope of topics are abstracted monthly. Suggestions or comments may be addressed to Flint Russett, St. Claire Regional Medical Center, 222 Medical Circle, Morehead, KY 40351, or e-mail: FSRussett@st-claire.org.

Recent Publications on Medications and Pharmacy

Purpose The creation and implementation of data-driven staffing-to-demand models at two institutions are described.

Summary Predictive workload tools provide a guideline for pharmacy managers to adjust staffing needs based on hospital volume metrics. At Abbott Northwestern Hospital, management worked with the department’s staff and labor management committee to clearly outline the productivity monitoring system and the process for reducing hours. Reference charts describing the process for reducing hours and a form to track the hours of involuntary reductions for each employee were created to further enhance communication, explain the rationale behind the new process, and promote transparency. The University of Minnesota Medical Center—Fairview, found a strong correlation between measured pharmacy workload and an adjusted census formula. If the daily census and admission report indicate that the adjusted census will provide enough workload for the fully staffed department, no further action is needed. If the census report indicates the adjusted census is less than the breakeven point, staff members are asked to leave work, either voluntarily or involuntarily. The opposite holds true for days when the adjusted census is higher than the breakeven point, at which time additional staff are required to synchronize worked hours with predicted workload.

Conclusion Successful staffing-to- demand models were implemented in two hospital pharmacies. Financial savings, as indicated by decreased labor costs secondary to reduction of staffed shifts, were approximately $42,000 and $45,500 over a three-month period for Abbott Northwestern Hospital and the University of Minnesota Medical Center—Fairview, respectively. Maintenance of 100% productively allowed the departments to continue to replace vacant positions and avoid permanent staff reductions.

Jason Ernster

Papers

Creating pharmacy staffing-to-demand models: Predictive tools used at two institutions