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The analysis of efficiency among a small number of organisations: How inferences can be improved by exploiting patient‐level data

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  • Kim Rose Olsen
  • Andrew Street

Abstract

Those responsible for monitoring and managing the performance of health‐care organisations face the common problem that the relationship between observed performance and effort is difficult to establish. A solution is to compare the performance of multiple organisations, but this requires a sufficient number of comparators. Faced with a small sample, it may be possible to exploit other information sources. Multilevel regression models are applied to analyse the performance of six Danish vascular departments in 2004 using a patient‐level data set. We find that treatment costs are higher for smokers, older patients, patients with cerebrovascular and pulmonal diseases and for those subject to acute hospitalisation and with longer lengths of stay. Costs are lower for patients who are having follow‐up surgery and for patients who receive some form of home care, suggesting that there may be some substitution of care input between vascular departments and other care providers. We estimate the relative efficiency of each department. The construction of confidence intervals allows the six departments to be sorted into two groups containing the least and most efficient departments. Conclusions about relative efficiency are robust to model specification, choice of estimator and hold at the 95% confidence level. Copyright © 2007 John Wiley & Sons, Ltd.

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  • Kim Rose Olsen & Andrew Street, 2008. "The analysis of efficiency among a small number of organisations: How inferences can be improved by exploiting patient‐level data," Health Economics, John Wiley & Sons, Ltd., vol. 17(6), pages 671-681, June.
  • Handle: RePEc:wly:hlthec:v:17:y:2008:i:6:p:671-681
    DOI: 10.1002/hec.1281
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    2. Carine Milcent, 2017. "Premier bilan de la T2A sur la variabilité des coûts hospitaliers," Working Papers halshs-01649554, HAL.
    3. Andrew Street & Conrad Kobel & Thomas Renaud & Josselin Thuilliez & ON BEHALF OF THE EURODRG GROUP, 2012. "How Well Do Diagnosis‐Related Groups Explain Variations In Costs Or Length Of Stay Among Patients And Across Hospitals? Methods For Analysing Routine Patient Data," Health Economics, John Wiley & Sons, Ltd., vol. 21(S2), pages 6-18, August.
    4. Carine Milcent, 2017. "Premier bilan de la tarification à l’activité (T2A) sur la variabilité des coûts hospitaliers," Post-Print halshs-01522581, HAL.
    5. Anne Hvenegaard & Jacob Arendt & Andrew Street & Dorte Gyrd-Hansen, 2011. "Exploring the relationship between costs and quality: Does the joint evaluation of costs and quality alter the ranking of Danish hospital departments?," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 12(6), pages 541-551, December.
    6. Carine Milcent, 2015. "Tarification et variabilité des coûts hospitaliers : Le cas de l'infarctus du myocarde," PSE Working Papers halshs-01202684, HAL.
    7. Carine Milcent, 2014. "Coût des soins hospitaliers : effet de la concurrence par la qualité," PSE Working Papers halshs-01010327, HAL.
    8. James Gaughan & Anne Mason & Andrew Street & Padraic Ward, 2012. "English Hospitals Can Improve Their Use of Resources: An Analysis of Costs and Length of Stay for Ten Treatments," Working Papers 078cherp, Centre for Health Economics, University of York.
    9. Yan Meng & Xueyan Zhao & Xibin Zhang & Jiti Gao, 2017. "A panel data analysis of hospital variations in length of stay for hip replacements: Private versus public," Monash Econometrics and Business Statistics Working Papers 20/17, Monash University, Department of Econometrics and Business Statistics.
    10. Hauck, Katharina & Zhao, Xueyan & Jackson, Terri, 2012. "Adverse event rates as measures of hospital performance," Health Policy, Elsevier, vol. 104(2), pages 146-154.
    11. Gyrd-Hansen, Dorte & Olsen, Kim R. & Sørensen, Torben H., 2012. "Socio-demographic patient profiles and hospital efficiency: Does patient mix affect a hospital's ability to perform?," Health Policy, Elsevier, vol. 104(2), pages 136-145.
    12. Laudicella, Mauro & Olsen, Kim Rose & Street, Andrew, 2010. "Examining cost variation across hospital departments-a two-stage multi-level approach using patient-level data," Social Science & Medicine, Elsevier, vol. 71(10), pages 1872-1881, November.
    13. Daidone, Silvio & Street, Andrew, 2013. "How much should be paid for specialised treatment?," Social Science & Medicine, Elsevier, vol. 84(C), pages 110-118.
    14. Kollerup, Anna & Wadmann, Sarah & Bek, Toke & Kjellberg, Jakob, 2022. "National clinical guidelines and treatment centralization do not guarantee consistency in healthcare delivery. A mixed-methods study of wet age-related macular degeneration treatment in Denmark," Health Policy, Elsevier, vol. 126(12), pages 1291-1302.
    15. Katharina Hauck & Xueyan Zhao & Terri Jackson, 2010. "Adverse events in surgical inpatients: A comparative analysis of public hospitals in Victoria," Monash Econometrics and Business Statistics Working Papers 5/10, Monash University, Department of Econometrics and Business Statistics.
    16. Sørensen, Torben H. & Olsen, Kim R. & Gyrd-Hansen, Dorte, 2009. "Differences in general practice initiated expenditures across Danish local health authorities--A multilevel analysis," Health Policy, Elsevier, vol. 92(1), pages 35-42, September.
    17. Thanassoulis, Emmanuel & Silva Portela, Maria & Graveney, Mike, 2016. "Identifying the scope for savings at inpatient episode level: An illustration applying DEA to chronic obstructive pulmonary disease," European Journal of Operational Research, Elsevier, vol. 255(2), pages 570-582.
    18. Wenqiang Tian & Gerben DeJong & Susan D. Horn & Koen Putman & Ching-Hui Hsieh & Joan E. DaVanzo, 2012. "Efficient Rehabilitation Care for Joint Replacement Patients," Medical Decision Making, , vol. 32(1), pages 176-187, January.

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