PubMed · 10314221
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D L Day. 1987. Productivity-based staffing.. https://pubmed.ncbi.nlm.nih.gov/10314221/
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UNLABELLED: We performed a statistical power analysis to determine how many historical data are needed for optimal operating room (OR) management decision making. The work applies to hospitals that provide service for all of its surgeons' elective cases on whatever workday the surgeons and patients choose. The hospital and anesthesia group adjust OR staffing and patient scheduling to care for the patients while minimizing OR staffing costs and maximizing labor productivity. Two years of data were obtained from a seven-OR surgical suite. The data were repeatedly split into training and testing datasets. The optimal staffing solution was calculated for each training dataset to maximize the efficiency of OR time usage and was then applied to the corresponding testing dataset. Training datasets ranged in size from 30 to 270 consecutive workdays. With 30 workdays of data, the statistical method identified staffing solutions that had an average of 35% decreased costs and 27% increased productivity as compared to the existing staffing plan. There was no significant improvement in performance with more than 210 workdays (10 mo) of data. With 30 workdays of OR or anesthesia group data, the optimization method can significantly reduce staffing costs and increase productivity compared with existing staffing. When applied routinely for adjusting staffing (e.g., on a quarterly basis), 9 to 12 mo of data should be used. IMPLICATIONS: With 30 workdays of operating room or anesthesia group data, the optimization method can propose staffing solutions that significantly decrease costs and increase productivity compared with existing staffing solutions. We recommend that, when the statistical method is applied routinely for adjusting staffing (e.g., on a quarterly basis), 9 to 12 mo of data be used.
Technologies promise to make our organizations more productive, efficient or effective. But our experience does not always fulfill this promise. Public safety agencies are the target of a barrage of new information technologies offering better performance, many of which are attractive because they can be financed through federal grants. But when evaluations are performed of these technologies, improvements do not always result. This article describes the experiences of a state police agency (SPA) during its use of cellular digital packet data (CDPD) systems to support its problem-oriented policing (POP) project. The CDPD system should have improved the SPA's POP operations; in practice, it was not as clear. Although participants believed the CDPD technology worthwhile, there were only a few minor differences between POP officers that did and did not use it. This evaluation suggests the need for better databases to measure performance as well as more informed federal funding of ways to assess the impact of technologies.