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Physician benchmarking: measuring variation in practice behavior in treatment of otitis media

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  • Yasar Ozcan

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Abstract

The study uses Data Envelopment Analysis (DEA) to analyze physician practice behavior and develops measures of physician practice efficiency as a basis for improving productivity and reducing costs in otitis media treatment. Other objectives include determining geographic variations in practice patterns for otitis media, and the impact of inefficient practice patterns on the cost of treatment of otitis media. Only 46 (28.8%) of the 160 physicians were classified as efficient. Average total cost of an episode by efficient providers was $357.03 and $492.06 for inefficient providers. By restricting particular inputs and outputs, and directing all physicians to treat otitis media through a balanced primary care model, physicians would be able to provide the same quality care at an average savings of 23.7% per efficient and 4.4% per inefficient provider. Copyright Kluwer Academic Publishers 1998

Suggested Citation

  • Yasar Ozcan, 1998. "Physician benchmarking: measuring variation in practice behavior in treatment of otitis media," Health Care Management Science, Springer, vol. 1(1), pages 5-17, September.
  • Handle: RePEc:kap:hcarem:v:1:y:1998:i:1:p:5-17
    DOI: 10.1023/A:1019026114859
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    References listed on IDEAS

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    1. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    2. Grosskopf, S. & Valdmanis, V., 1987. "Measuring hospital performance : A non-parametric approach," Journal of Health Economics, Elsevier, vol. 6(2), pages 89-107, June.
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    Cited by:

    1. Lukas Kwietniewski & Jonas Schreyögg, 2018. "Profit efficiency of physician practices: a stochastic frontier approach using panel data," Health Care Management Science, Springer, vol. 21(1), pages 76-86, March.
    2. Filipe Amado, Carla Alexandra & Dyson, Robert G., 2008. "On comparing the performance of primary care providers," European Journal of Operational Research, Elsevier, vol. 185(3), pages 915-932, March.
    3. Mareike Heimeshoff & Jonas Schreyögg & Lukas Kwietniewski, 2014. "Cost and technical efficiency of physician practices: a stochastic frontier approach using panel data," Health Care Management Science, Springer, vol. 17(2), pages 150-161, June.
    4. Robert Rosenman & Daniel Friesner, 2004. "Scope and scale inefficiencies in physician practices," Health Economics, John Wiley & Sons, Ltd., vol. 13(11), pages 1091-1116, November.
    5. Luis Murillo-Zamorano & Carmelo Petraglia, 2011. "Technical efficiency in primary health care: does quality matter?," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 12(2), pages 115-125, April.
    6. Lukas Kwietniewski & Mareike Heimeshoff & Jonas Schreyögg, 2017. "Estimation of a physician practice cost function," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 18(4), pages 481-494, May.
    7. Lukas Kwietniewski & Jonas Schreyögg, 2018. "Efficiency of physician specialist groups," Health Care Management Science, Springer, vol. 21(3), pages 409-425, September.
    8. C A F Amado & R G Dyson, 2009. "Exploring the use of DEA for formative evaluation in primary diabetes care: An application to compare English practices," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(11), pages 1469-1482, November.
    9. Javier Fiallos & Jonathan Patrick & Wojtek Michalowski & Ken Farion, 2017. "Using data envelopment analysis for assessing the performance of pediatric emergency department physicians," Health Care Management Science, Springer, vol. 20(1), pages 129-140, March.
    10. Kalyan S. Pasupathy & Alexandra Medina-Borja, 2008. "Integrating Excel, Access, and Visual Basic to Deploy Performance Measurement and Evaluation at the American Red Cross," Interfaces, INFORMS, vol. 38(4), pages 324-337, August.
    11. Thanassoulis, Emmanuel & Silva Portela, Maria C.A. & Graveney, Mike, 2014. "Using DEA to estimate potential savings at GP units at medical specialty level," Socio-Economic Planning Sciences, Elsevier, vol. 48(1), pages 38-48.
    12. Changhee Kim & Hyun Jung Kim, 2019. "A study on healthcare supply chain management efficiency: using bootstrap data envelopment analysis," Health Care Management Science, Springer, vol. 22(3), pages 534-548, September.
    13. Antonio Giuffrida & Hugh Gravelle, 2001. "Measuring performance in primary care: econometric analysis and DEA," Applied Economics, Taylor & Francis Journals, vol. 33(2), pages 163-175.
    14. Chih-Ching Yang, 2017. "Measuring health indicators and allocating health resources: a DEA-based approach," Health Care Management Science, Springer, vol. 20(3), pages 365-378, September.
    15. José Cordero Ferrera & Eva Cebada & Luis Murillo Zamorano, 2014. "The effect of quality and socio-demographic variables on efficiency measures in primary health care," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 15(3), pages 289-302, April.
    16. Marina Evrim Johnson & Nagen Nagarur, 2016. "Multi-stage methodology to detect health insurance claim fraud," Health Care Management Science, Springer, vol. 19(3), pages 249-260, September.
    17. Palazzolo, Jennifer R. & Ozcan, Yasar A., 2018. "Do the most efficient accountable care organizations earn shared savings?," Socio-Economic Planning Sciences, Elsevier, vol. 63(C), pages 12-17.

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