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Objective This study modeled the predictive power of unit/patient characteristics, nurse workload, nurse expertise, and hospital-acquired pressure ulcer (HAPU) preventive clinical processes of care on unit-level prevalence of HAPUs. Data Sources Seven hundred and eighty-nine medical-surgical units (215 hospitals) in 2009. Study Design Using unit-level data, HAPUs were modeled with Poisson regression with zero-inflation (due to low prevalence of HAPUs) with significant covariates as predictors. Data Collection/Extraction Methods Hospitals submitted data on NQF endorsed ongoing performance measures to CALNOC registry. Principal Findings Fewer HAPUs were predicted by a combination of unit/patient characteristics (shorter length of stay, fewer patients at-risk, fewer male patients), RN workload (more hours of care, greater patient [bed] turnover), RN expertise (more years of experience, fewer contract staff hours), and processes of care (more risk assessment completed). Conclusions Unit/patient characteristics were potent HAPU predictors yet generally are not modifiable. RN workload, nurse expertise, and processes of care (risk assessment/interventions) are significant predictors that can be addressed to reduce HAPU. Support strategies may be needed for units where experienced full-time nurses are not available for HAPU prevention. Further research is warranted to test these finding in the context of higher HAPU prevalence.

Public demand for safer care has catapulted the healthcare industry's efforts to understand relationships between patient safety and hospital performance. This study explored linkages between staff perceptions of safety culture (SC) and ongoing measures of hospital nursing unit-based structures, care processes, and adverse patient outcomes. Relationships between nursing-sensitive measures of hospital performance and SC were explored at the unit-level from 9 California hospitals and 37 nursing units. SC perceptions were measured 6 months prior to collection of nursing metrics and relationships between the two sets of data were explored using correlational and regression analyses. Significant relationships were found with reported falls and process measures for fall prevention. Multiple associations were identified with SC and the structure of care delivery: skill mix, staff turnover, and workload intensity demonstrated significant relationships with SC, explaining 22-45% of the variance. SC was an important factor to understand in the quest to advance safe patient care. These findings have affordability and care quality implications for hospital leadership. When senior leaders prioritized a safety culture, patient outcomes may have improved with less staff turnover and more productivity. A business case could be made for investing in patient safety systems to provide reliably safe care.

The Institute of Medicine released a consensus report in October 2010, titled The Future of Nursing (FON): Leading Change, Advancing Health, which concluded significant change was needed in nurses' roles, responsibilities, and education to meet the increased demand for care that will be created by health care reform and to advance improvements in America's increasingly complex health system (http://www.iom.edu/Reports/2010/The-Future-of-Nursing-Leading-Change-Advancing-Health.aspx). Dr. Donna Shalala, Chair of the study, and Dr. Linda Burnes Bolton, Vice Chair of the study, spoke about the Future of Nursing (FON) at the Collaborative Alliance for Nursing Outcomes (CALNOC) conference to a predominately nursing and quality professional audience. This follow-up interview expands the discussion specifically for quality professionals, many of whom are nurses.

Benchmarking is an indispensable tool as hospital leaders face challenges to balance efficiency with safe and effective care. Selection of appropriate "like" hospitals is critical to the benchmarking aim of understanding comparative performance. Based on 10 years of observed outcome differences between small and large hospitals, the Collaborative Alliance for Nursing Outcomes (CALNOC) sought to empirically define small hospitals, and to determine if there were statistical differences between small and large hospitals for selected nursing sensitive outcome indicators. This article reports the examination of hospital size as a proxy characteristic to define "like" hospitals for the purpose of benchmarking outcomes. Findings suggest that optimal classifications into small and large hospital size based on the outcome indicators of falls, falls with injury, and hospital-acquired pressure ulcers stage 2 or worse (HAPU 2+) were not consistent with historical administrative categories based on average daily census and not consistent by outcome. Statistical differences were only found with HAPU 2+ in critical care units, with no differences in the fall outcomes. These data did not support the use of size-based categories to define like hospitals for benchmark comparisons.

Numerous research reports have examined patterns of nurse staffing (Aiken, Sochalski, and Anderson 1996; Unruh, Fottler, and Talbott 2003; Aydin et al. 2004) and the relationship between nurse staffing and patient outcomes (Aiken et al. 2002; Needleman et al. 2002; Lang et al. 2004; Donaldson et al. 2005). These studies often reach different conclusions about historical changes in nurse staffing and the relationship of nurse staffing to patient outcomes. For example, two publications in the 1990s offered opposing findings about whether nurse staffing had declined in California, using different data sources (Anderson and Kohn 1996;Spetz 1998). Discrepancies also are found in the literature on the relationship between nurse staffing and patient outcomes. While many studies that use hospital-level data have found that higher levels of nurse staffing are associated with improved patient outcomes and lower mortality rates (Aiken et al. 2002; Needleman et al. 2002), most studies that examine data at the level of the hospital unit have found a weaker relationship or no relationship at all (e.g., Donaldson et al. 2005). It is unknown whether these different findings are the result of the use of varying measures of nurse staffing, differences in the importance of overall hospital staffing versus unit-level staffing, measurement error, or some other factor. Why do these studies obtain such different results? One possibility lies in different methods of measuring nurse staffing. Datasets used to measure nurse staffing can provide data by hospital, type of hospital unit, or specific unit. Nurse staffing can be measured as full-time equivalent employment (FTEE), nursing hours per patient day, share of registered nurses in total nursing staff, nurse-to-patient ratios, or other metrics. This paper compares nurse staffing measurements in two commonly used datasets—the American Hospital Association (AHA) Annual Survey of Hospitals and California's Office of Statewide Health Planning and Development (OSHPD) Hospital Annual Disclosure Report—as well as unit-level data collected by the California Nursing Outcomes Coalition (CalNOC) and the California Workforce Initiative (CWI). In 2006, Jiang, Stocks, and Wong analyzed disparities between AHA and OSHPD data, concluding that the OSHPD data on nurse staffing appeared more complete and, thus, more closely associated with patient outcomes. In this report, we examine principal differences between the AHA and OSHPD datasets as well as unit-level CalNOC and CWI data, compare the levels of nurse staffing measured by each, and discuss the ramifications of using alternative data sources and measures for nurse staffing and outcomes research.

The purpose of this article is to present findings from a review of published systematic/integrative reviews and meta-analyses on nursing interventions and patient outcomes in acute care settings. A literature search was conducted for the period 1999-2005, producing 4,000 systematic/integrative reviews and 500 meta-analyses covering seven topics selected by the authors: elder care, caregivers, developmental care of neonates and infants, symptom management, pressure ulcer prevention/treatment, incontinence, and staffing. The association between nursing care interventions/processes and patient outcomes in acute care settings was found to be limited in the articles reviewed. The strongest evidence was for the use of patient risk-assessment tools and interventions implemented by nurses to prevent patient harm. We observed significant variation in methods to measure the effect of independent variables (nursing interventions) on patient outcomes. Results indicate the need for more research measuring the effect of specific nursing interventions that may impact acute care patient outcomes.

The Kaiser Permanente North East Bay service area redesigned its quality program beginning in 1995, to better mirror how care was provided across the continuum. The old model had evolved over time, was based on departmental structure, and did not focus on all patient populations. The purpose of this article is to describe the redesign process, the quality model implemented, and future directions, with the hope that the lessons learned will provide other healthcare quality professionals some of the knowledge needed and, perhaps, the courage to "design" their quality programs.

The California Nursing Outcomes Coalition (CalNOC) project is an initiative that has become the largest ongoing nursing quality measurement repository in the nation. Launched in 1996 by California nursing leaders concerned with trends in hospital care, CalNOC has created reliable quality benchmark data to define patient safety thresholds in California. This article describes CalNOC's effort, which aligns with the strategy of the National Quality Forum for measuring and reporting healthcare quality. By tracing the evolution of the CalNOC project and its future potential, we hope to encourage other grassroots efforts to build the database repositories needed for healthcare quality measurement in the 21st century.

Purpose: To explore the need for evidence-based health policy, as illustrated by the mandatory staffing bill passed by the California state legislature in 1999. Design: Prospective data were collected from a voluntary sample of California acute care representatives to describe selected patient safety and clinical outcomes and nurse staffing variables at the patient-care unit level. Methods: Data for descriptive analysis were collected on hospital nurse staffing, patient falls, and pressure ulcers from 257 medical, surgical, medical-surgical combined, step-down, 24-hour observation units, and critical care patient care units in 38 California acute care hospitals from June 1998 to June 1999. Findings: Nursing staffing ratios varied among the 257 units. RNs provided 91% of the nursing care in critical care units. Patients in medical-surgical units received 59% of their care from RNs, 11% from licensed vocational nurses, and 30% from other caregivers. Preliminary data showed no relationships between reported staffing ratios in these hospitals and the incidence of patient falls or hospital-acquired pressure ulcers. Conclusions: California Nursing Outcomes Coalition (CalNOC) data showed wide variations in staffing ratios, patient falls, and hospital-acquired pressure ulcers among nursing units and hospitals. These early findings indicate the need for additional research before determining minimal RN staffing requirements. Analysis of multiple sources of data may be necessary to determine safe staffing ratios and to provide evidence-based data for public policy.

Benchmarking expedites the quest for best practices and is crucial to hospitals' effective, reliable, and superior performance. Comparative performance data are used by accrediting and regulatory bodies to evaluate performance and by consumers in making decisions on where to seek healthcare. Nursing-sensitive quality measures affirmed by the National Quality Forum are now used in public reporting and pay-for-performance in addition to traditional medical outcome metrics. This report provides hospital nursing-sensitive benchmarks from medical/surgical, critical care, and step-down units drawn from 196 hospitals during six quarters in 2007 and 2008. Outcome measures include pressure ulcer prevalence rates and fall/falls with injury rates. Additional indicators that describe nursing care (nurse staffing care hours, skill mix, nurse/patient ratios, workload intensity, voluntary turnover, and use of sitters) and patient descriptors (age, gender, and diagnosis description) were also included. Specific benchmarks are provided using the 10th and the 90th percentiles, as well as quartiles to allow hospitals an opportunity to understand comparative performance with specificity. The purpose of this article is to provide hospitals not currently participating in comparative benchmarking databases with nursing-sensitive data from the Collaborative Alliance for Nursing Outcomes for use in performance improvement processes.

Quality professionals are the first to understand challenges of transforming data into meaningful information for frontline staff, operational managers, and governing bodies. To understand an individual facility, service, or patient care unit's comparative performance from within large data sets, prioritization and focused data presentation are needed. This article presents a methodology for translating data from large data sets into dashboards for setting performance improvement priorities, in a simple way that takes advantage of tools readily available and easily used by support staff. This methodology is illustrated with examples from a large nursing quality data set, the California Nursing Outcomes Coalition.

Healthcare quality professionals in the United States have opportunities to teach healthcare providers in other countries how to measure and monitor quality. Special preparation is required to effectively teach an international audience. This article offers tips for teaching basic quality concepts based upon the authors' experiences in the People's Republic of China. Working with health professionals in other countries is exciting and challenging for everyone involved. Approaching the opportunity with systematic preparation may be the key to success.