Dataset Information

Optimal staffing in community health centers to improve quality of care.

ABSTRACT:

Objective

To explore optimal workforce configurations in the production of care quality in community health centers (CHCs), accounting for interactions among occupational categories, as well as contributions to the volume of services.

Data sources

We linked the Uniform Data System from 2014 to 2016 with Internal Revenue Service nonprofit tax return data. The final database contained 3139 center-year observations from 1178 CHCs.

Study design

We estimated a system of two generalized linear production functions, with quality of care and volume of services as outputs, using the average percent of diabetic patients with controlled A1C level and hypertensive patients with controlled blood pressure as quality measures. To explore the substitutability and complementarity between staffing categories, we estimated a revenue function.

Findings

Primary care physicians and advanced practice clinicians achieve similar quality outcomes (3.2 percent and 3.0 percent improvement in chronic condition management per full-time equivalent (FTE), respectively). Advanced practice clinicians generate less revenue per FTE but are generally less costly to employ.

Conclusion

As quality incentives are further integrated into payment systems, CHCs will need to optimize their workforce configuration to improve quality. Given the relative efficiency of advanced practice clinicians in producing quality, further hiring of these professionals is a cost-effective investment for CHCs.

SUBMITTER: Luo Q

PROVIDER: S-EPMC7839644 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:A major source of primary health care for millions of Americans, community health centers (CHCs) act as a key point of access for diabetes care. The ability of a CHC to deliver high quality care, that supports patients' management of their diabetes, may be impacted by the unique set of resources and constraints it faces, both in terms of characteristics of its patient population and aspects of operations. This study examines how patient and regional characteristics, staffing patterns, and efficiency were associated with diabetes management at CHCs (percentage of patients with uncontrolled diabetes, HbA1C > 9%). Data on a sample of 1229 CHCs came from multiple sources. CHC-level information was obtained from the Uniform Data System and regional-level information from the Behavioral Risk Factor Surveillance System and the US Census American Community Survey. A clustering methodology, latent class analysis, identified seven underlying staffing patterns at CHCs. Data envelopment analysis was performed to evaluate the efficiency of CHCs, relative to centers with similar staffing patterns. Finally, generalized linear models were used to examine the association between staffing patterns, efficiency, and patient and regional-level characteristics. Findings from this study have sociological, practical, and methodological implications. Findings highlight that the intersection of patient racial composition with regional racial composition is significant; diabetes control appears to be worse at CHCs serving racial minorities living in predominantly White areas. Findings suggest that CHCs that incorporate more behavioral health care into their staffing mix have lower rates of uncontrolled diabetes among their patients. Finally, greater efficiency in CHC operations is associated with better diabetes control among patients. By identifying sociodemographic and operational characteristics associated with better hemoglobin control among diabetes patients, the current study contributes to our understanding of both health care operations and health inequalities.