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Measuring efficiency of community health centers: a multi-model approach considering quality of care and heterogeneous operating environments

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Abstract

Over 1300 federally-qualified health centers (FQHCs) in the US provide care to vulnerable populations in different contexts, addressing diverse patient health and socioeconomic characteristics. In this study, we use data envelopment analysis (DEA) to measure FQHC performance, applying several techniques to account for both quality of outputs and heterogeneity among FQHC operating environments. To address quality, we examine two formulations, the Two-Model DEA approach of Shimshak and Lenard (denoted S/L), and a variant of the Quality-Adjusted DEA approach of Sherman and Zhou (denoted S/Z). To mitigate the aforementioned heterogeneities, a data science approach utilizing latent class analysis (LCA) is conducted on a set of metrics not included in the DEA, to identify latent typologies of FQHCs. Each DEA quality approach is applied in both an aggregated (including all FQHCs in a single DEA model) and a partitioned case (solving a DEA model for each latent class, such that an FQHC is compared only to its peer group). We find that the efficient frontier for the aggregated S/L approach disproportionately included smaller FQHCs, whereas the aggregated S/Z approach’s reference set included many larger FQHCs. The partitioned cases found that both the S/L and S/Z aggregated models disproportionately disfavored (different) members of certain classes with respect to efficiency scores. Based on these results, we provide general insights into the trade-offs of using these two models in conjunction with a clustering approach such as LCA.

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Acknowledgements

Research reported in this publication was supported by the National Institute of General Medical Sciences (NIGMS) of the National Institutes of Health under Award Number 5P20GM104417. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author information

Authors and Affiliations

  1. Department of Industrial and Manufacturing Systems Engineering, University of Missouri, E3437 Lafferre Hall, Columbia, MO, 65211, USA

    Ronald G. McGarvey & Rohith Madhi Reddy

  2. Truman School of Public Affairs, University of Missouri, E3437 Lafferre Hall, Columbia, MO, 65211, USA

    Ronald G. McGarvey

  3. Jake Jabs College of Business & Entrepreneurship, Montana State University, 350 Jabs Hall, P.O. Box 173040, Bozeman, MT, 59717-3040, USA

    Andreas Thorsen

  4. Department of Sociology & Anthropology, Montana State University, 2-122 Wilson Hall, Bozeman, MT, 59717, USA

    Maggie L. Thorsen

Authors
  1. Ronald G. McGarvey
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  2. Andreas Thorsen
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  3. Maggie L. Thorsen
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  4. Rohith Madhi Reddy
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Corresponding author

Correspondence to Ronald G. McGarvey.

Appendices

Appendix 1: Statistical analyses for data sets

Table 13 Pairwise correlation coefficients between DEA inputs and DEA production outputs
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Table 14 Descriptive statistics for DEA input data
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Table 15 Descriptive statistics for LCA data, across 1111 FQHCs in DEA models
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Appendix 2: Algorithms for implementation of S/L and S/Z procedures

Sets:

  • R: DEA production outputs; R = r1,…,r2

  • Q: DEA quality outputs; Q = q1,…,q2

    • q1: Quality_Access

    • q2: Quality_LBWI

  • J: FQHCs; J = j1,…,j1111

Parameters:

  • k: iteration counter

  • Ek: number of FQHCs excluded due to iteration k

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McGarvey, R.G., Thorsen, A., Thorsen, M.L. et al. Measuring efficiency of community health centers: a multi-model approach considering quality of care and heterogeneous operating environments. Health Care Manag Sci 22, 489–511 (2019). https://doi.org/10.1007/s10729-018-9455-5

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  • DOI: https://doi.org/10.1007/s10729-018-9455-5

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