2012 OPEN FORUM Abstracts
WORKLOAD PROJECTION TOOL; MATCHING STAFFING TO WORKLOAD FOR SMART PRODUCTIVITY.
Christopher Kircher, Ford Jeffrey, Martin Marlin, Smith Robin, Saunders Peter, Madden Maria, Reza Hamid, Davis Matthew; Respiratory Care Services, University of Maryland Medical Center, Baltimore, MD
BACKGROUND The management of appropriate staffing levels is a multi-factored process that requires knowledge of seasonal workload, facility geography, budget and productivity. Unfortunately, departments often lack a shift by shift process for consistent flexing of staff against workload. To determine correct staffing levels for Respiratory Care Services at the University of Maryland Medical Center (UMMC), a computer based workload projection tool was developed that allows staff entry of key work indicators and calculates staffing required in each clinical area. METHOD In the late 1990s, an extensive internal time study was performed for the most common tasks performed by a respiratory therapist. After validating results against AARC time standards, appropriate values were used for the primary work drivers per area and mathematically determined each shifts staff level through the use of an Excel® spreadsheet. These work value units (WVUs) are multiplied by the total numbers of each work driver to obtain total WVUs per area. The design of the original model evolved as work requirements, budgetary constraints, and facility geography has changed. As hospitals are adapting to widespread changes with in healthcare, there exists an ever increasing need to financially adjust and manage staffing in leaner and more cost efficient ways. UMMC has helped provide counsel to department managers who have been tasked to reevaluate their staffing roster and offer up potential increases in efficiency. In all cases, these managers lacked a formal tool to match staffing to workload, and could not justify the importance of specific staffing levels. All have since adopted a version of the UMMC staffing tool. RESULTS The workload projection tool has provided a reliable, data driven method to flex staffing levels for each shift, enabling consistent decisions to activate or cancel staff. The reflection against productivity and utilization reports has validated the tool and is shared with the staff to show how the management team balances financial constraints against increased clinical demands. CONCLUSION Smart management requires constant reflection on many factors to maintain fiscal responsibility and optimal customer service. Appropriate staffing tools are essential, but should not be too cumbersome to use. The computerized model at UMMC easily adapts with facility growth, can be utilized by lead therapists and supervisors, and assures safe and efficient staffing. Sponsored Research - None Individual screens are used for day and night shifts. Staffing data is collected daily and analyzed on a weekly and monthly basis.