Background: Little is known about the recent development of the quality of nursing care.
Objective: To examine trends in the rate of total inpatient falls, one of the nursing-sensitive quality indicators, in US hospitals.
Research Design: A longitudinal study of unit-level data collected during 2004–2009 by the National Database of Nursing Quality Indicators. Hierarchical Poisson regression models were used for the analysis of the unit-level fall rate.
Subjects: Approximately 37,000 observations from 8915 nursing units (1994 critical care, 1328 step-down, 1663 medical, 1279 surgical, 2217 medical-surgical, and 434 rehabilitation units) in 1171 hospitals were examined.
Measures: The outcome measure was the annual count of unit-level inpatient falls with the annual count of unit-level patient days taken as the exposure variable. Independent variables included hospital size (≥300 or <300 beds), teaching status, and Magnet status and unit-level total nursing hours per patient day and proportion of total nursing hours supplied by RNs (skill-mix) at baseline.
Results: The mean fall rates for most unit types remained stable or decreased, whereas those for surgical units increased over time. A higher register nurses skill-mix and the total nursing hours per patient day were both associated with lower fall rates (P<0.001); hospitals with more beds tended to have lower fall rates (P=0.001). Hospital Magnet and teaching status were not associated with the fall rate.
Conclusions: Overall, the fall rate in the United States hospitals decreased over time, but the large variation in the fall rate at both the hospital and the unit level indicates much room for improvement in the quality of nursing care related to fall prevention.
*Department of Biostatistics
†School of Nursing, University of Kansas Medical Center, Kansas City, KS
This research was conducted with funding from the American Nurses Association.
The authors declare no conflict of interest.
Reprints: Jianghua He, PhD, Department of Biostatistics, University of Kansas Medical Center, MS 1026, 3901 Rainbow BLVD, Kansas City, KS 66160. E-mail: email@example.com.