Mathematical Modeling to Reduce Waste of Compounded Sterile Products in Hospital Pharmacies. Issue 7 (July 2014)

• Record Type: Journal Article
• Authors: Tilson, Vera; Dobson, Gregory; Haas, Curtis E.; Tilson, David
• Is Part Of: Hospital pharmacy. Volume 49:Issue 7(2014)
• Page Start: 616
• Page End: 627
• Publication Date: 2014-07
• Subjects: CSP batch preparation -- mathematical inventory models -- Monte Carlo simulation -- waste reduction; Hospital pharmacies -- Periodicals; Pharmacy Service, Hospital; Hospital pharmacies; Periodicals; Periodicals; 615.1
• DOI: 10.1310/hpj4907-616
• Languages: English
• ISSNs: 0018-5787

Abstract

In recent years, many US hospitals embarked on "lean" projects to reduce waste. One advantage of the lean operational improvement methodology is that it relies on process observation by those engaged in the work and requires relatively little data. However, the thoughtful analysis of the data captured by operational systems allows the modeling of many potential process options. Such models permit the evaluation of likely waste reductions and financial savings before actual process changes are made. Thus the most promising options can be identified prospectively, change efforts targeted accordingly, and realistic targets set. This article provides one example of such a data-driven process redesign project focusing on waste reduction in an in-hospital pharmacy. A mathematical model of the medication prepared and delivered by the pharmacy is used to estimate the savings from several potential redesign options (rescheduling the start of production, scheduling multiple batches, or reordering production within a batch) as well as the impact of information system enhancements. The key finding is that mathematical modeling can indeed be a useful tool. In one hospital setting, it estimated that waste could be realistically reduced by around 50% by using several process changes and that the greatest benefit would be gained by rescheduling the start of production (for a single batch) away from the period when most order cancellations are made.