Caring labour, intersectionality and worker satisfaction: an analysis of the National Nursing Assistant Study (NNAS).

Caring labour in long-term care settings is increasingly important as the US population ages. Ethnographic research on nursing assistants (NAs) portrays nursing home care as routine and fast paced in facilities that emphasise life maintenance more than care. Recent interview-based and small quantitative studies describe a mix of positive and negative aspects of NA work, including the rewards of caring, despite shortcomings in working conditions and pay. The current study continues this research but, for the first time, using national data. The 2004 Centers for Disease Control and Prevention's National Nursing Assistant Study (NNAS) provides survey data from 3,017 NAs working in long-term care facilities across the US. The NNAS results confirm the importance and centrality of caring to NAs' work. NAs motivated by caring for others were significantly more satisfied with their jobs than those motivated by other reasons, such as convenience or salary. Overall, NAs report surprisingly high job satisfaction, particularly with learning new skills, doing challenging work, and organisational support for caring labour. Areas of dissatisfaction were salary, time for reproductive labour, and turnover. Intersectional analysis revealed race and citizenship played a stronger role than gender in worker satisfaction.

Do different case-mix measures affect assessments of provider efficiency? Lessons from the Department of Veterans Affairs.

Although case-mix adjustment is critical for provider profiling, little is known regarding whether different case-mix measures affect assessments of provider efficiency. We examine whether two case-mix measures, Adjusted Clinical Groups (ACGs) and Diagnostic Cost Groups (DCGs), result in different assessments of efficiency across service networks within the Department of Veterans Affairs (VA). Three profiling indicators examine variation in resource use. Although results from the ACGs and DCGs generally agree on which networks have greater or lesser efficiency than average, assessments of individual network efficiency vary depending upon the case-mix measure used. This suggests that caution should be used so that providers are not misclassified based on reported efficiency.

Applying a risk-adjustment framework to primary care: can we improve on existing measures?

Outcome-based performance measurement and prospective payment are common features of the current managed care environment. Increasingly, primary care clinicians and health care organizations are being asked to assume financial risk for enrolled patients based on negotiated capitation rates. Therefore, the need for methods to account for differences in risk among patients enrolled in primary care organizations has become critical. Although current risk-adjustment measures represent significant advances in the measurement of morbidity in primary care populations, they may not adequately capture all the dimensions of patient risk relevant to primary care. We propose a risk-adjustment framework for primary care that incorporates clinical features related to patients' health status and nonclinical factors related to patients' health behaviors, psychosocial factors, and social environment. Without this broad perspective, clinicians with more unhealthy and more challenging populations are at risk of being inadequately compensated and inequitably compared with peers. The risk-adjustment framework should also be of use to health care organizations that have been mandated to deliver high-quality primary care but are lacking the necessary tools.

Profiling resource use: do different outcomes affect assessments of provider efficiency?

OBJECTIVES: To examine whether 2 outcome measures result in different assessments of efficiency across 22 service networks within the Department of Veterans Affairs (VA). STUDY DESIGN: A retrospective analysis using VA inpatient and outpatient administrative databases. METHODS: A 60% random sample of veterans who used healthcare services during fiscal year 1997 was split into a 40% sample (n = 1,046,803) for development and a 20% sample (n = 524,461) for validation. Weighted concurrent case-mix models using adjusted clinical groups were developed to explain variation in 2 outcomes: "days of care"--the sum of a patient's inpatient and outpatient annual visit days, and "average accounting costs"--the sum of the average service costs multiplied by the units of service for each patient. Two profiling indicators were calculated for each outcome: an unadjusted efficiency index and an adjusted efficiency index. These indices were compared to examine network efficiency. RESULTS: Although about half the networks were identified as "efficient" before and after case-mix adjustment, assessments of individual network efficiency were affected by the adjustment. The 2 outcomes differed on which networks were efficient. For example, 4 networks that appeared as efficient based on days of care appeared as inefficient based on average costs. CONCLUSIONS: Assessments of provider efficiency across the 22 networks depended on the outcome measure used. Knowledge about the extent to which assessments of provider efficiency depend on the outcome measure used is an important step toward improved and more equitable comparisons across providers.