Primary Care Practice Workplace Social Capital: A Potential Secret Sauce for Improved Staff Well-Being and Patient Experience

Patient experiences with the health-care system are increasingly seen as a vital measure of health-care quality. This study examined whether workplace social capital and employee outcomes are associated with patients’ perceptions of care quality across multiple clinic sites in a diverse, urban safety net care setting. Data from clinic staff were collected using paper and pencil surveys and data from patients were collected via a telephone survey. A total of 8392 adult primary care patients and 265 staff (physicians, nurses, allied health, and support staff) were surveyed at 10 community health clinics. The staff survey included brief measures of workplace social capital, burnout, and job satisfaction. The patient-level outcome was patients’ overall rating of the quality of care. Factor analysis and reliability analysis were conducted to examine measurement properties of the employee data. Data were aggregated and measures were examined at the clinic site level. Workplace social capital had moderate to strong associations with burnout ( r = −0.40, P < .01) and job satisfaction ( r = 0.59, P < .01). Mean patient quality of care rating was 8.90 (95% confidence interval: 8.86-8.94) ranging from 8.57 to 9.18 across clinic sites. Pearson correlations with patient-rated care quality were high for workplace social capital ( r = 0.88, P = .001), employee burnout ( r = −0.74, P < .05), and satisfaction ( r = 0.69, P < .05). Patient-perceived clinic quality differences were largely explained by differences in workplace social capital, staff burnout, and satisfaction. Investments in workplace social capital to improve employee satisfaction and reduce burnout may be key to better patient experiences in primary care

Risk-period-cohort approach for averting identification problems in longitudinal models.

In epidemiology, gerontology, human development and the social sciences, age-period-cohort (APC) models are used to study the variability in trajectories of change over time. A well-known issue exists in simultaneously identifying age, period and birth cohort effects, namely that the three characteristics comprise a perfectly collinear system. That is, since age = period-cohort, only two of these effects are estimable at a time. In this paper, we introduce an alternative framework for considering effects relating to age, period and birth cohort. In particular, instead of directly modeling age in the presence of period and cohort effects, we propose a risk modeling approach to characterize age-related risk (i.e., a hybrid of multiple biological and sociological influences to evaluate phenomena associated with growing older). The properties of this approach, termed risk-period-cohort (RPC), are described in this paper and studied by simulations. We show that, except for pathological circumstances where risk is uniquely determined by age, using such risk indices obviates the problem of collinearity. We also show that the size of the chronological age effect in the risk prediction model associates with the correlation between a risk index and chronological age and that the RPC approach can satisfactorily recover cohort and period effects in most cases. We illustrate the advantages of RPC compared to traditional APC analysis on 27496 individuals from NHANES survey data (2005-2016) to study the longitudinal variability in depression screening over time. Our RPC method has broad implications for examining processes of change over time in longitudinal studies

Clinical Characteristics of Individuals With Serious Mental Illness and Type 2 Diabetes

View from west; fountains in foreground and chapel at lef

Best Practices: Optimizing Care for People With Serious Mental Illness and Comorbid Diabetes

Diabetes and obesity among patients with serious mental illness are common. Use of second-generation antipsychotics compounds risk, and widely prevalent unhealthy behaviors further contribute to negative outcomes. This column describes Targeted Training in Illness Management, a group-based psychosocial treatment that blends psychoeducation, problem identification, goal setting, and behavioral modeling and reinforcement. The intervention has been adapted to the primary care setting and is targeted at individuals with serious mental illness and diabetes. A key feature of the intervention is the use of peer educators with serious mental illness and diabetes to teach and model self-management. Promising results from a 16-week trial are reported