Lynn Unruh

Nurse staffing and patient, nurse, and financial outcomes.

Because theres no scientific evidence to support specific nurse-patient ratios, and in order to assess the impact of hospital nurse staffing levels on given patient, nurse, and financial outcomes, the author conducted a literature review. The evidence shows that adequate staffing and balanced workloads are central to achieving good outcomes, and the author offers recommendations for ensuring appropriate nurse staffing and for further research.

A Systems Framework for Evaluating Nursing Care Quality in Nursing Homes

Given the ongoing concerns about the quality of care in nursing homes, a theoretical framework to guide a systems approach to quality is important. Existing frameworks either do not model causality, or do so in a linear fashion in which the actual linkages between components of quality may not be well specified. Through a review of frameworks for nursing home quality, and empirical studies on the subject, the authors construct a framework for nursing home quality that links contextual components of quality with structure, structure with process, and process with outcomes, focusing on nursing care quality. Intrastructural relationships and feedback mechanisms are also modeled. The framework is matched with a discussion of multilevel structural equation analysis for statistical application. Future research should expand the framework to include non-nursing components of quality.

Nurse Staffing, Nursing Intensity, Staff Mix, and Direct Nursing Care Costs Across Massachusetts Hospitals

Objective: This study describes the distribution of patient-to-registered nurse (RN) ratios, RN intensity of care, total staff intensity of care, RN to total staff skill mix percent, and RN costs per patient day in 65 acute community hospitals and 9 academic medical centers in Massachusetts. Methods: We conducted a retrospective secondary analysis of the Patients First database published by the Massachusetts Hospital Association for planned nurse staffing in 601 inpatient nursing units in the state for 2005 using a multivariate linear statistical model controlling for hospital type and unit type. Nursing unit types were identified as adult and pediatric medical/surgical, step down, critical care, neonatal level II, and neonatal level III/IV nurseries. Results: Medical centers had significantly higher case-mix index (1.72 vs 1.20, P < .001), longer lengths of stay (5.18 vs 4.19, P < .001), more beds (574 vs 147, P < .001), discharges (31,597 vs 7,248, P < .001), and patient days (161,440 vs 31,020, P < .001) compared with to community hospitals. Medical centers had significantly lower patient-to-RN ratios (3.22 vs 4.64, P < .001), higher nursing intensity and total nursing staff intensity (9.62 vs 7.43/11.75 vs 9.87, both P < .001), higher percent of RN to all staff mix (79% vs 71%, P < .001), and higher RN costs per patient day (

Health Information Technology Adoption in U.S. Acute Care Hospitals

385 vs

Should I stay or should I go? Career change and labor force separation among registered nurses in the U.S.

297, P < .001) compared with to community hospitals. There were significant differences in adult med/surg units between community hospitals and medical centers for patient-to-RN staffing ratios (5.25 vs 4.08), nursing intensity (5.1 vs 6.2 hours daily), skill mix (67% vs 73% RN), and RN costs per patient day (

Predictors of Resident Outcome Improvement in Nursing Homes

203 vs

Projections and trends in RN supply: what do they tell us about the nursing shortage?

248, all P < .001). There were no significant differences between the adult step-down units. Conclusion: The significant differences between community hospitals and medical centers, unit type, as well as the high degree of variability in patient-to-RN ratios, nursing intensity, skill mix, and RN costs per patient day suggest that nursing resource expenditure at Massachusetts hospitals is complex and affected by case mix, unit size, and complexity of care.

Accountable care organizations in the USA: Types, developments and challenges

Previous studies show that the healthcare industry lags behind many other economic sectors in the adoption of information technology. The purpose of this study is to understand differences in structural characteristics between providers that do and that do not adopt Health Information Technology (HIT) applications. Publicly available secondary data were used from three sources: American Hospital Association (AHA) annual survey, Healthcare Information and Management Systems Society (HIMSS) analytics annual survey, and Healthcare Cost and Utilization Project (HCUP) Nationwide Inpatient Sample (NIS) databases. Fifty-two information technologies were grouped into three clusters: clinical, administrative, and strategic decision making ITs. Negative binomial regression was applied with adoption of technology as the dependent variables and eight organizational and contextual factors as the independent variables. Hospitals adopt a relatively larger proportion of administrative information technology as compared to clinical and strategic IT. Large size, urban location and HMO penetration were found to be the most influential hospital characteristics that positively affect information technology adoption. There are still considerable variations in the adoption of information technology across hospitals and in the type of technology adopted. Organizational factors appear to be more influential than market factors when it comes to information technology adoption. The future research may examine whether the Electronic Health Record (EHR) Incentive Program in 2011 would increase the information technology uses in hospitals as it provides financial incentives for HER adoptions and uses among providers.

Valuing Volunteers: The impact of Volunteerism on Hospital Performance

Efforts to retain nurses within the profession are critical for resolving the global nursing shortage, but very little research explores the phenomenon of nursing workforce attrition in the U.S. This study is the first to simultaneously investigate the timing of attrition through survival analysis, the exit path taken (career change vs. labor force separation), and the major socioeconomic, family structure, and demographic variables predicting attrition in this country. Using nationally representative U.S. data from the 2004 National Sample Survey of Registered Nurses (N=29,472), we find that the rate of labor force separation is highest after the age of 60, a typical pattern for retirement. However, a non-trivial proportion of career change also occurs at older ages (50+ years old), and the rate of labor force separation begins to climb at relatively young ages (30-40 years old). Particularly strong predictors of early labor force separation include being married and providing care to dependents in the home (young children or elderly parents). Career change is predicted strongly by higher levels of education, male gender, and current enrollment in a non-nursing degree program. Having an Advanced Practice credential reduced the hazards of attrition for both exit paths. The results suggest a fruitful path for future research and a number of policy approaches to curbing nurse workforce attrition.

Changes in Nursing Home Staffing Levels, 1997 to 2007

The effects of contextual characteristics and nursing-related factors on the overall quality improvement of resident outcomes, measured by a weighted index in incidents of pressure ulcers, physical restraints, and catheter use in nursing homes, were investigated by autoregressive latent trajectory modeling of panel data (1997-2003). Findings show that in the initial study period, nursing homes with a smaller bed size, being for-profit, caring for more Medicare residents, having residents with lower acuity levels, being located elsewhere than the South, having a high level of nurse staffing, and certified with lower frequencies of nursing care deficiencies had better quality. The intercept factor, representing the baseline of quality, was well predicted by six of the eight contextual and facility characteristics variables, and the slope trajectory of quality was only weakly predicted by them. The improved quality in resident outcomes was associated with facilities having fewer nursing care deficiency citations than their counterparts.