Damian Everhart1, Jessica R Schumacher, R Paul Duncan, Allyson G Hall, Donna F Neff, Ronald I Shorr. 1. Damian Everhart, PhD, RN, is Assistant Professor, Brooks College of Health, University of North Florida, Jacksonville. E-mail: damian.everhart@unf.edu. Jessica R. Schumacher, PhD, MS, is Associate Director of Analytics, Department of Population Health Sciences, University of Wisconsin-Madison. R. Paul Duncan, PhD, MS, is Malcom and Christine Randall Professor and Chair, Department of Health Services Research, Management and Policy, University of Florida, Gainesville. Allyson G. Hall, PhD, MBA, MHS, is Associate Professor, Department of Health Services Research, Management and Policy, University of Florida, Gainesville. Donna F. Neff, PhD, RN, DSNAP, is Associate Professor, College of Nursing, University of Central Florida, Orlando. Ronald I. Shorr, MD, MS, is Professor, Department of Epidemiology, University of Florida, Gainesville.
Abstract
BACKGROUND: Patient falls in acute care hospitals represent a significant patient safety concern. Although cross-sectional studies have shown that fall rates vary widely between acute care hospitals, it is not clear whether hospital fall rates remain consistent over time. PURPOSE: The aim of this study was to determine whether hospitals can be categorized into fall rate trajectory groups over time and to identify nurse staffing and hospital characteristics associated with hospital fall rate trajectory groups. METHODOLOGY/APPROACH: We conducted a 54-month (July 2006-December 2010) longitudinal study of U.S. acute care general hospitals participating in the National Database for Nursing Quality Indicators (2007). We used latent class growth modeling to categorize hospitals into groups based on their long-term fall rates. Nurse staffing and hospital characteristics associated with membership in the highest hospital fall rate group were identified using logistic regression. FINDINGS: A sample of 1,529 hospitals (mean fall rate of 3.65 per 1,000 patient days) contributed data to the analysis. Latent class growth modeling findings classified hospital into three groups based on fall rate trajectories: consistently high (mean fall rate of 4.96 per 1,000 patient days), consistently medium (mean fall rate of 3.63 per 1,000 patient days), and consistently low (mean fall rate of 2.50 per 1,000 patient days). Hospitals with higher total nurse staffing (odds ratio [OR] = 0.92, 95% confidence interval [CI] [0.85, 0.99]), Magnet status (OR = 0.49, 95% CI [0.35, 0.70]), and bed size greater than 300 beds (OR = 0.70, 95% CI [0.51, 0.94]) were significantly less likely to be categorized in the "consistently high" fall rate group. PRACTICE IMPLICATIONS: Over this 54-month period, hospitals were categorized into three groups based on long-term fall rates. Hospital-level factors differed among these three groups. This suggests that there may be hospitals in which "best practices" for fall prevention might be identified. In addition, administrators may be able to reduce fall rates by maintaining greater nurse staffing ratios as well as fostering an environment consistent with that of Magnet hospitals.
BACKGROUND:Patient falls in acute care hospitals represent a significant patient safety concern. Although cross-sectional studies have shown that fall rates vary widely between acute care hospitals, it is not clear whether hospital fall rates remain consistent over time. PURPOSE: The aim of this study was to determine whether hospitals can be categorized into fall rate trajectory groups over time and to identify nurse staffing and hospital characteristics associated with hospital fall rate trajectory groups. METHODOLOGY/APPROACH: We conducted a 54-month (July 2006-December 2010) longitudinal study of U.S. acute care general hospitals participating in the National Database for Nursing Quality Indicators (2007). We used latent class growth modeling to categorize hospitals into groups based on their long-term fall rates. Nurse staffing and hospital characteristics associated with membership in the highest hospital fall rate group were identified using logistic regression. FINDINGS: A sample of 1,529 hospitals (mean fall rate of 3.65 per 1,000 patient days) contributed data to the analysis. Latent class growth modeling findings classified hospital into three groups based on fall rate trajectories: consistently high (mean fall rate of 4.96 per 1,000 patient days), consistently medium (mean fall rate of 3.63 per 1,000 patient days), and consistently low (mean fall rate of 2.50 per 1,000 patient days). Hospitals with higher total nurse staffing (odds ratio [OR] = 0.92, 95% confidence interval [CI] [0.85, 0.99]), Magnet status (OR = 0.49, 95% CI [0.35, 0.70]), and bed size greater than 300 beds (OR = 0.70, 95% CI [0.51, 0.94]) were significantly less likely to be categorized in the "consistently high" fall rate group. PRACTICE IMPLICATIONS: Over this 54-month period, hospitals were categorized into three groups based on long-term fall rates. Hospital-level factors differed among these three groups. This suggests that there may be hospitals in which "best practices" for fall prevention might be identified. In addition, administrators may be able to reduce fall rates by maintaining greater nurse staffing ratios as well as fostering an environment consistent with that of Magnet hospitals.
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