Development of a Nurse Staffing Model to Accurately Reflect Complexity and Intensity of Patient Care Needs in an Urban Academic Medical Center

Abstract

Poster Presentation Purpose for the Program Our current staffing model, based on nursing hours per birth, does not accurately forecast registered nurse (RN) staffing needs during a 24‐hour period. Researchers have shown that the number of nurses may influence perinatal outcomes, which highlights the need to accurately project appropriate staffing. Perinatal outcomes are influenced by adequate staffing. Proposed Change To identify a staffing model for our labor and delivery area that more accurately matched actual patient care needs. Academic medical centers (AMC) pose a unique staffing challenge because of the larger percentage of patients without adequate prenatal care and complex medical comorbidities. Our current productivity statistic that drives nurse staffing is number of births per day, which does not account for acuity or intensity of care. Eleven percent of patients are unaccounted for in our current model because their stays do not result in birth, yet they require nursing care (e.g., trauma, preterm labor). Variation also exists in intensity of care for a subset of women that lack adequate prenatal care and who have complex medical comorbidities. Implementation, Outcomes, and Evaluation Wilson and Blegen developed a staffing model that provides a standardized measurement of nursing productivity, skill mix, and workload intensity that can be used to measure and evaluate outcomes. Based on their model, we captured the amount and intensity of nursing care using the following calculation of total hours of care needed in a 24‐hour period: number of births x standardized nursing workload allotted to each birth, labor evaluations/obstetric patients whose stays did not result in birth, and labor and delivery operating room time. During the month of April 2014, we tested our present model against the more comprehensive one developed by Wilson and Blegen and found that their model better captured the complexity and intensity of nursing care needs in our patient population. When we applied the Wilson and Blegen model to our data, we demonstrated a negative variance to budget of 6% rather than the actual budget overage of 8%. Implications for Nursing Practice In summary, this model provides a way to accurately forecast RN staffing requirements based on patient care needs in our labor and delivery unit. Next steps involve application of this model for budget planning and development of tools for charge nurses to ensure appropriate staffing in real time.