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Health system efficiency in OECD countries: dynamic network DEA approach

Author

Listed:
  • Beata Gavurova

    (Tomas Bata University in Zlín)

  • Kristina Kocisova

    (Technical University of Košice)

  • Jakub Sopko

    (Technical University of Košice)

Abstract

Background In recent years, measuring and evaluating the efficiency of health systems has been explored in the context of seeking resources to ensure the sustainability of ‘countries’ health and social systems and addressing various crises in the health sector. The study aims to quantify and compare the efficiency of OECD health systems in 2000, 2008, and 2016. The contribution to research in the field of efficiency in the healthcare system can be seen in the application of Dynamic Network Data Envelopment Analysis (DNDEA), which help us to analyse not only the overall efficiency of the healthcare system but analyse the overall efficiency as the result of the efficiencies of individual interconnected areas (public and medical care area). By applying the DNDEA model, we can realise the analysis not only within one year, but we can find out if the measures and improvements taken in the healthcare sector have a positive impact on its efficiency in a later period (eight-year interval). Methods The analysis focuses on assessing the efficiency of the health systems of OECD countries over three periods: 2000, 2008, and 2016. Data for this study were derived from the existing OECD database, which provides aggregated data on OECD countries on a comparable basis. In this way, it was possible to compare different countries whose national health statistics may have their characteristics. The input-oriented Dynamic Network Data Envelopment Analysis model was used for data processing. The efficiency of OECD health systems has been analysed and evaluated comprehensively and also separately in two divisions: public health sub-division and medical care sub-division. The analysis combines the application of conventional and unconventional methods of measuring efficiency in the health sector. Results The results for the public health sub-division, medical care sub-division and overall health system for OECD countries under the assumption of constant returns to scale indicate that the average overall efficiency was 0.8801 in 2000, 0.8807 in 2008 and 0.8472 in 2016. The results of the input-oriented model with the assumption of constant returns to scale point to the overall average efficiency of health systems at the level of 0.8693 during the period. According to the Malmquist Index results, the OECD countries improved the efficiency over the years, with performance improvements of 19% in the public health division and 8% in the medical care division. Conclusions The results of the study are beneficial for health policymakers to assess and compare health systems in countries and to develop strategic national and regional health plans. Similarly, the result will support the development of international benchmarks in this area. The issue of health efficiency is an intriguing one that could be usefully explored in further research. A greater focus on combining non-parametric and parametric models could produce interesting findings for further research. The consistency in the publication and updating of the data on health statistics would help us establish a greater degree of accuracy.

Suggested Citation

  • Beata Gavurova & Kristina Kocisova & Jakub Sopko, 2021. "Health system efficiency in OECD countries: dynamic network DEA approach," Health Economics Review, Springer, vol. 11(1), pages 1-25, December.
    • Handle: RePEc:spr:hecrev:v:11:y:2021:i:1:d:10.1186_s13561-021-00337-9
    DOI: 10.1186/s13561-021-00337-9
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    References listed on IDEAS

    as
    1. Yasar A. Ozcan, 2014. "Evaluation of Performance in Health Care," International Series in Operations Research & Management Science, in: Health Care Benchmarking and Performance Evaluation, edition 2, chapter 0, pages 3-14, Springer.
    2. Rolf Färe & Shawna Grosskopf & Gerald Whittaker, 2014. "Network DEA II," International Series in Operations Research & Management Science, in: Wade D. Cook & Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 0, pages 307-327, Springer.
    3. João Medeiros & Christoph Schwierz, 2015. "Efficiency estimates of health care systems," European Economy - Economic Papers 2008 - 2015 549, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    4. Joe Zhu, 2014. "Quantitative Models for Performance Evaluation and Benchmarking," International Series in Operations Research and Management Science, Springer, edition 3, number 978-3-319-06647-9, September.
    5. Ane Auraaen & Rie Fujisawa & Grégoire de Lagasnerie & Valérie Paris, 2016. "How OECD health systems define the range of good and services to be financed collectively," OECD Health Working Papers 90, OECD Publishing.
    6. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2007. "Data Envelopment Analysis," Springer Books, Springer, edition 0, number 978-0-387-45283-8, September.
    7. Yasar A. Ozcan, 2014. "Health Care Benchmarking and Performance Evaluation," International Series in Operations Research and Management Science, Springer, edition 2, number 978-1-4899-7472-3, September.
    8. Joe Zhu, 2014. "Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Quantitative Models for Performance Evaluation and Benchmarking, edition 3, chapter 1, pages 1-9, Springer.
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    Cited by:

    1. Chen, Kuan-Chen & Lin, Sun-Yuan & Yu, Ming-Miin, 2022. "Exploring the efficiency of hospital and pharmacy utilizations in Taiwan: An application of dynamic network data envelopment analysis," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    2. Peter Akioyamen & Mehmet A. Begen, 2023. "A Spatio-Temporal Analysis of OECD Member Countries’ Health Care Systems: Effects of Data Missingness and Geographically and Temporally Weighted Regression on Inference," IJERPH, MDPI, vol. 20(13), pages 1-18, June.
    3. José A. García-Berná & Raimel Sobrino-Duque & Juan M. Carrillo de Gea & Joaquín Nicolás & José L. Fernández-Alemán, 2022. "Automated Workflow for Usability Audits in the PHR Realm," IJERPH, MDPI, vol. 19(15), pages 1-30, July.
    4. Aydın Özdemir & Hakan Kitapçı & Mehmet Şahin Gök & Erşan Ciğerim, 2021. "Efficiency Assessment of Operations Strategy Matrix in Healthcare Systems of US States Amid COVID-19: Implications for Sustainable Development Goals," Sustainability, MDPI, vol. 13(21), pages 1-17, October.
    5. Yusi Cheng & Xuejie Bai & Yung‐Ho Chiu, 2023. "Performance evaluation for health‐care sectors using a dynamic network data envelopment analysis approach," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(4), pages 2237-2253, June.
    6. Alvaro Almeida, 2024. "The trade-off between health system resiliency and efficiency: evidence from COVID-19 in European regions," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 25(1), pages 31-47, February.

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    More about this item

    Keywords

    Public health; Data envelopment analysis; DNDEA; OECD; Efficiency;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
    • R58 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - Regional Development Planning and Policy

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