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A DEA based composite measure of quality and its associated data uncertainty interval for health care provider profiling and pay-for-performance

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  • Shwartz, Michael
  • Burgess, James F.
  • Zhu, Joe

Abstract

Composite measures calculated from individual performance indicators increasingly are used to profile and reward health care providers. We illustrate an innovative way of using Data Envelopment Analysis (DEA) to create a composite measure of quality for profiling facilities, informing consumers, and pay-for-performance programs. We compare DEA results to several widely used alternative approaches for creating composite measures: opportunity-based-weights (OBW, a form of equal weighting) and a Bayesian latent variable model (BLVM, where weights are driven by variances of the individual measures). Based on point estimates of the composite measures, to a large extent the same facilities appear in the top decile. However, when high performers are identified because the lower limits of their interval estimates are greater than the population average (or, in the case of the BLVM, the upper limits are less), there are substantial differences in the number of facilities identified: OBWs, the BLVM and DEA identify 25, 17 and 5 high-performers, respectively. With DEA, where every facility is given the flexibility to set its own weights, it becomes much harder to distinguish the high performers. In a pay-for-performance program, the different approaches result in very different reward structures: DEA rewards a small group of facilities a larger percentage of the payment pool than the other approaches. Finally, as part of the DEA analyses, we illustrate an approach that uses Monte Carlo resampling with replacement to calculate interval estimates by incorporating uncertainty in the data generating process for facility input and output data. This approach, which can be used when data generating processes are hierarchical, has the potential for wider use than in our particular application.

Suggested Citation

  • Shwartz, Michael & Burgess, James F. & Zhu, Joe, 2016. "A DEA based composite measure of quality and its associated data uncertainty interval for health care provider profiling and pay-for-performance," European Journal of Operational Research, Elsevier, vol. 253(2), pages 489-502.
  • Handle: RePEc:eee:ejores:v:253:y:2016:i:2:p:489-502
    DOI: 10.1016/j.ejor.2016.02.049
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    Cited by:

    1. Jesús A. Tapia & Bonifacio Salvador, 2022. "Data envelopment analysis efficiency in the public sector using provider and customer opinion: An application to the Spanish health system," Health Care Management Science, Springer, vol. 25(2), pages 333-346, June.
    2. Verbunt, Pim & Rogge, Nicky, 2018. "Geometric composite indicators with compromise Benefit-of-the-Doubt weights," European Journal of Operational Research, Elsevier, vol. 264(1), pages 388-401.
    3. De Witte, Kristof & Schiltz, Fritz, 2018. "Measuring and explaining organizational effectiveness of school districts: Evidence from a robust and conditional Benefit-of-the-Doubt approach," European Journal of Operational Research, Elsevier, vol. 267(3), pages 1172-1181.
    4. Mussard, Stéphane & Pi Alperin, María Noel, 2021. "Accounting for risk factors on health outcomes: The case of Luxembourg," European Journal of Operational Research, Elsevier, vol. 291(3), pages 1180-1197.
    5. Matthias Klumpp, 2017. "Do Forwarders Improve Sustainability Efficiency? Evidence from a European DEA Malmquist Index Calculation," Sustainability, MDPI, vol. 9(5), pages 1-33, May.
    6. Amin Mostafaee & Milan Hladík, 2020. "Optimal value bounds in interval fractional linear programming and revenue efficiency measuring," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(3), pages 963-981, September.
    7. Margit Sommersguter-Reichmann, 2022. "Health care quality in nonparametric efficiency studies: a review," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(1), pages 67-131, March.
    8. Matthias Klumpp, 2018. "How to Achieve Supply Chain Sustainability Efficiently? Taming the Triple Bottom Line Split Business Cycle," Sustainability, MDPI, vol. 10(2), pages 1-23, February.
    9. Lin, Winston T. & Chen, Yueh H. & Hung, TingShu, 2019. "A partial adjustment valuation approach with stochastic and dynamic speeds of partial adjustment to measuring and evaluating the business value of information technology," European Journal of Operational Research, Elsevier, vol. 272(2), pages 766-779.
    10. An, Qingxian & Zhang, Qiaoyu & Tao, Xiangyang, 2023. "Pay-for-performance incentives in benchmarking with quasi S-shaped technology," Omega, Elsevier, vol. 118(C).
    11. Zaiwu Gong & Xiaoqing Chen, 2017. "Analysis of Interval Data Envelopment Efficiency Model Considering Different Distribution Characteristics—Based on Environmental Performance Evaluation of the Manufacturing Industry," Sustainability, MDPI, vol. 9(12), pages 1-25, November.
    12. Toloo, Mehdi & Keshavarz, Esmaeil & Hatami-Marbini, Adel, 2018. "Dual-role factors for imprecise data envelopment analysis," Omega, Elsevier, vol. 77(C), pages 15-31.
    13. Martin Boďa & David Cole & Mária Murray Svidroňová & Jolana Gubalová, 2022. "Prevailing narratives versus reality of a small and medium town decline in a CEE country," Operational Research, Springer, vol. 22(3), pages 3113-3145, July.
    14. Kalinichenko, Olena & Amado, Carla A.F. & Santos, Sérgio P., 2022. "Exploring the potential of Data Envelopment Analysis for enhancing pay-for-performance programme design in primary health care," European Journal of Operational Research, Elsevier, vol. 298(3), pages 1084-1100.
    15. Surakiat PARICHATNON & Kamonthip MAICHUM & Ke-Chung PENG, 2018. "Measuring technical efficiency of Thai rubber production using the three-stage data envelopment analysis," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 64(5), pages 227-240.
    16. Cook, Wade D. & Ramón, Nuria & Ruiz, José L. & Sirvent, Inmaculada & Zhu, Joe, 2019. "DEA-based benchmarking for performance evaluation in pay-for-performance incentive plans," Omega, Elsevier, vol. 84(C), pages 45-54.
    17. Joe Zhu, 2022. "DEA under big data: data enabled analytics and network data envelopment analysis," Annals of Operations Research, Springer, vol. 309(2), pages 761-783, February.
    18. Li, Yongjun & Xie, Jianhui & Wang, Meiqiang & Liang, Liang, 2016. "Super efficiency evaluation using a common platform on a cooperative game," European Journal of Operational Research, Elsevier, vol. 255(3), pages 884-892.
    19. Fainman, Emily Zhu & Kucukyazici, Beste, 2020. "Design of financial incentives and payment schemes in healthcare systems: A review," Socio-Economic Planning Sciences, Elsevier, vol. 72(C).
    20. Youchao Tan & Yang Zhang & Roohollah Khodaverdi, 2017. "Service performance evaluation using data envelopment analysis and balance scorecard approach: an application to automotive industry," Annals of Operations Research, Springer, vol. 248(1), pages 449-470, January.

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