Examining the Impact of Nursing Assistive Personnel Staffing Levels on Injurious Inpatient Hospital Falls

This investigation was an exploratory, retrospective examination designed to evaluate whether there was an association between the number of nursing-assistive-personnel (NAP) and injurious inpatient falls in non-federal general acute care hospitals in Pennsylvania during FY 2012. The U.S. healthcare system has been striving to strike a balance among the provision of safe, quality care for all while curtailing rising costs. The healthcare system is described as having three interrelated and often competing elements, i.e., access, quality, and cost-containment. As the need for access to healthcare rises, so too does cost, and researchers have found that this can be at the expense of the quality of care. The purpose of this study was to determine whether what has been historically identified as a cost-containment strategy of using lower wage NAP affected the quality of care using injurious fall rate as the quality measure. Regression analyses were performed using inpatient fall data obtained from the Pennsylvania Health Care Cost Containment Council. Injurious fall rates and nursing staff size (NAP, RN, LPN/LVN) were standardized by individual hospitals’ annual inpatient days and aggregated at the hospital level. There were 137 general acute care, nonfederal hospitals included in the sample. A total of 2,219 injurious falls were reported during FY 2012 in the sample hospitals with a mean of 16.2 and a mean injurious fall rate of .40/1,000 inpatient days. The mean number of NAP per 1,000 inpatient days was 2.0 with licensed nurses (RN + LPN/LVN) being 9.1 per 1,000 inpatient days. Hierarchical regression results identified a significant positive relationship between the injurious fall rate and NAP (beta = .623, p < .01), but concurrently found a significant negative relationship between fall rate and the ratio of NAP to licensed nurses (beta = -350, p <.05) yielding a paradoxical result. Findings from a second step-wise regression supported these findings, identified occupancy rate as a third significant predictor, and NAP/1000 inpatient days as the strongest predictor among all the independent variables. Future studies may benefit by using a longitudinal design