Advertisement

The European Journal of Health Economics

, Volume 11, Issue 4, pp 367–381 | Cite as

The challenge of corporatisation: the experience of Portuguese public hospitals

  • Guilhermina RegoEmail author
  • Rui Nunes
  • José Costa
Original Paper

Abstract

The inability of traditional state organisations to respond to new economic, technological and social challenges and the associated emerging problems has made it necessary to adopt new methods of health management. As a result, new directions have emerged in the reform of Public Administration together with the introduction of innovative models. The aim is to achieve a type of management that focusses on results as well as on effort and efficiency. We intend to analyse to what extent the adoption of business management models by hospital healthcare units can improve their performance, mainly in terms of standards of efficiency. Data envelopment analysis (DEA) was used to investigate the efficiency of a set of public Portuguese hospitals. The aim was to evaluate the impact of business management in Portuguese public hospitals with regards to efficiency, specifically taking into account the fact that lack of resources and increased health care needs are a present and future reality. From a total of 83 public hospitals, a sample of 59 hospitals was chosen, of which 21 are state-owned hospital enterprises (SA) and 38 are traditional public administration sector hospitals (SPA). This study evaluates hospital performance by calculating two efficiency measures associated with two categories of inputs. The first efficiency measures the costs associated with hospital production lines and the number of beds (representing fixed capacity) as inputs. The annual costs generated by the hospitals in the consumption of capital and work (direct and indirect costs) are used. A second measure of efficiency is calculated separately. This measure includes in the inputs the number of beds as well as the human resources available (number of doctors, number of nurses and other personnel) in each hospital. With regard to output, the variables that best reflect the hospital services rendered were considered: number of inpatient days, patients discharged, outpatient visits, emergencies services, sessions in hospital day care services and the number of surgeries. The results seem to suggest that the introduction of market processes and changes in organisational structure—such as managerial autonomy and corporatisation of public hospitals—have had a positive impact on Portuguese public hospitals. This positive evolution was particularly evident in SA hospitals, but further studies are needed to confirm these preliminary results.

Keywords

Corporatisation Efficiency measurement Hospital services Data envelopment analysis 

JEL Classification

I-1 I-18 

References

  1. 1.
    Hughes, O.: Public Management and Administration, 2nd edn. Palgrave, Hampshire (1998)Google Scholar
  2. 2.
    Proeller, I.: Trends in local government in Europe. Public Manage. Rev. 8(1), 7–29 (2006). doi: 10.1080/14719030500518642 CrossRefGoogle Scholar
  3. 3.
    Boyne, G., Farrell, C., Law, J., et al.: Evaluating Public Management Reforms. Principles and Practice. Managing the Public Services Series. Open University Press, Philadelphia (2003)Google Scholar
  4. 4.
    Conrad, R., Strauss, R.: A multiple-output multiple-input model of the hospital industry in North Carolina. Appl. Econ. 15, 341–352 (1983). doi: 10.1080/00036848300000005 CrossRefGoogle Scholar
  5. 5.
    Cowing, T., Holtman, A.: Multiproduct short-run hospital cost functions: empirical evidence and policy implications from cross-section data. South. Econ. J. 49, 637–653 (1983). doi: 10.2307/1058706 CrossRefGoogle Scholar
  6. 6.
    Grannemann, T., Brown, R., Pauly, M.: Estimating hospital costs. A multiple-output analysis. J. Health Econ. 5, 107–127 (1986). doi: 10.1016/0167-6296(86)90001-9 CrossRefGoogle Scholar
  7. 7.
    Vita, M.: Exploring hospital production relationships with flexible functional forms. J. Health Econ. 9, 1–21 (1990). doi: 10.1016/0167-6296(90)90038-5 CrossRefGoogle Scholar
  8. 8.
    Scuffham, P.A., Devlin, N.J., Jaforullah, M.: The structure of costs and production in New Zealand public hospitals: an application of the transcendental logarithmic variable cost function. Appl. Econ. 28, 75–85 (1996). doi: 10.1080/00036849600000010 CrossRefGoogle Scholar
  9. 9.
    Fournier, G.M., Mitchell, J.M.: Hospital costs and competition services: a multiproduct analysis. Rev. Econ. Stat. 74(4), 627–634 (1992). doi: 10.2307/2109376 CrossRefGoogle Scholar
  10. 10.
    Rosko, M., Broyles, R.: Short-term responses of hospitals to the DRG prospective pricing mechanism in New Jersey. Med. Care 25(2), 307–318 (1987). doi: 10.1097/00005650-198702000-00002 CrossRefGoogle Scholar
  11. 11.
    Hadley, J., Zuckerman, S., Feder, J.: Profits and fiscal pressure in the prospective payment system: their impacts on hospitals. Inquiry 26(3), 354–365 (1989)Google Scholar
  12. 12.
    Chesney, J.: Utilization trends before and after PPS. Inquiry 27(4), 376–381 (1990)Google Scholar
  13. 13.
    Menke, T.: Impacts of PPS on Medicare–Part B expenditures and utilization for hospital episodes of care. Inquiry 27(2), 114–126 (1990)Google Scholar
  14. 14.
    Lave, J., Frank, R.: Effect of the structure of hospital payment on length of stay. Health Serv. Res. 25(2), 327–347 (1990)Google Scholar
  15. 15.
    Hodgkin, D., McGuire, T.: Payments levels and hospital response to prospective payment. J. Health Econ. 13, 1–29 (1994). doi: 10.1016/0167-6296(94)90002-7 CrossRefGoogle Scholar
  16. 16.
    Ellis, R., McGuire, T.: Hospital response to prospective payment: moral hazard, selection and practice-style effects. J. Health Econ. 15, 257–277 (1996). doi: 10.1016/0167-6296(96)00002-1 CrossRefGoogle Scholar
  17. 17.
    Antioch, K.M., Walsh, M.K.: Risk adjusted capitation funding models for chronic diseases in Australia: alternatives to casemix funding. Eur. J. Health Econ. 3, 83–93 (2002). doi: 10.1007/s10198-002-0096-7 CrossRefGoogle Scholar
  18. 18.
    Antioch, K.M., Walsh, M.K.: Risk adjusted vision beyond casemix (DRG) funding in Australia: international lessons in high complexity and capitation. Eur. J. Health Econ. 5, 95–109 (2004). doi: 10.1007/s10198-003-0208-z CrossRefGoogle Scholar
  19. 19.
    Antioch, K.M., Ellis, R.P., Gillett, S., et al.: Risk adjustment policy options for casemix funding: international lessons in financing reforms. Eur. J. Health Econ. 8, 195–212 (2007)CrossRefGoogle Scholar
  20. 20.
    Zuckerman, S., Hadley, J., Lezzoni, L.: Measuring hospital efficiency with frontier cost functions. J. Health Econ. 13(3), 255–281 (1994)CrossRefGoogle Scholar
  21. 21.
    Farrell, M.J.: The measurement of productive efficiency. J. R. Stat. Soc. [Ser A] 120(Part 3), 253–290 (1957). http://www.rss.org.uk/main.asp?page=1711 doi: 10.2307/2343100
  22. 22.
    Charnes, A., Cooper, W., Rhodes, E.: Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2(6), 429–444 (1978). http://www.elsevier.com/wps/find/journaldescription.cws_home/505543/description#description doi: 10.1016/0377-2217(78)90138-8 Google Scholar
  23. 23.
    Färe, R., Grosskopf, S., Lovell, A.: Production Frontiers. Cambridge University Press, Cambridge (1994)Google Scholar
  24. 24.
    Charnes, A., et al.: Data Envelopment Analysis. Theory, Methodology and Applications. Kluwer, Boston (1996)Google Scholar
  25. 25.
    Cooper, W., Seiford, L., Tone, K.: Data Envelopment Analysis. A Comprehensive Text with Models. Applications, References and DEA-Solver Software. Kluwer, Boston (2004)Google Scholar
  26. 26.
    Zhu, J.: Quantitative Models for Performance Evaluation and Benchmarking. Data Envelopment Analysis with Spreadsheets and DEA Excel Solver. Kluwer, Boston (2004)Google Scholar
  27. 27.
    Ramanathan, R.: An Introduction to Data Envelopment Analysis. A Tool for Performance Measurement. Sage, London (2003)Google Scholar
  28. 28.
    Chirikos, T., Sear, A.: Measuring hospital efficiency: a comparison of two approaches. Health Ser. Res. 34(6), 1389–1408 (2000)Google Scholar
  29. 29.
    Ganley, J.A., Cubbin, J.S.: Public Sector Efficiency Measurement: Applications of Data Envelopment Analysis. North-Holland, Amsterdam (1992)Google Scholar
  30. 30.
    Bauer, P.W.: Recent developments in the econometric estimation of frontier. J Econ. 46, 39–56 (1990). doi: 10.1016/0304-4076(90)90046-V Google Scholar
  31. 31.
    Grosskopf, S., Valdmanis, V.: Measuring hospital performance. A non-parametric approach. J. Health Econ., June, vol. 6 (2), pp. 89–108 (1987). http://www.elsevier.com/wps/find/journaldescription.cws_home/505560/description
  32. 32.
    Valdmanis, V.: Sensitivity analysis for DEA models: An empirical example using public versus NFP hospitals. Journal of Public Economics, Amsterdam, July, vol. 48 (2), pp. 185–206 (1992). http://www.elsevier.com/wps/find/journaldescription.cws_home/505578/description#description
  33. 33.
    Register, C., Brunning, E.: Profit incentives and technical efficiency in the production of hospital care. South. Econ. J. 53, 899–914 (1987). http://www.utc.edu/Outreach/SouthernEconomicAssociation/southern-economic-journal.html doi: 10.2307/1059684
  34. 34.
    Ozcan, Y., Luke, R.: A national study of the efficiency of hospitals in urban markets. Health Serv. Res. (Feb):719–739 (1993). http://www.hsr.org/hsr/abouthsr/journal.jsp
  35. 35.
    Chirikos, T., Sear, A.: Measuring hospital efficiency: A comparison of two approaches. Health Serv. Res. (Feb), vol. 34 (6), pp. 1389–1408 (2000). http://www.hsr.org/hsr/abouthsr/journal.jsp
  36. 36.
    Magnussen, J.: Efficiency measurement and the operationalization of hospital production. Health Serv. Res. 31(1), 21–37 (1996). http://www.hsr.org/hsr/abouthsr/journal.jsp
  37. 37.
    Dalmau, E., Puig-Junoy, J.: Market structure and hospital efficiency: Evaluating potential effects of deregulation in a National Health Service. Rev. Ind. Organ. 13, 447–466 (1998). http://www.springer.com/economics/industrial+organization/journal/11151 doi: 10.1023/A:1007775616593
  38. 38.
    Al-Shammari, M.: A multi-criteria data envelopment analysis model for measuring the productive efficiency of hospitals. Int. J. Oper. Prod. Manage. Bradf. 19(9), 879–890 (1999). http://info.emeraldinsight.com/products/journals/journals.htm?PHPSESSID=2pnd0pqs7n2v2ds9n2p4mgu914&id=ijopm doi: 10.1108/01443579910280205
  39. 39.
    Kirigia, J., Emrouznejad, A., Sambo, L.: Measurement of technical efficiency of public hospitals in Kenya: using data envelopment analysis. J. Med. Syst. 26(1), 39–45 (2002). http://www.springer.com/statistics/stats+life+sci/journal/10916 doi: 10.1023/A:1013090804067
  40. 40.
    Osei, D., D’Almeida, S., George, M., Kirigia, J., et al.: Technical efficiency of public district hospitals and health centres in Ghana: a pilot study. Cost Eff. Resour. Alloc. 3(9), (2005). http://www.resource-allocation.com/
  41. 41.
    Ouellette, P., Vierstraete, V.: Technological change and efficiency in the presence of quasi-fixed inputs: A DEA application to the hospital sector. Eur. J. Oper. Res. 154, 755–764 (2004). http://www.elsevier.com/wps/find/journaldescription.cws_home/505543/description#description doi: 10.1016/S0377-2217(02)00712-9 Google Scholar
  42. 42.
    Chang, H., Chang, W., Das, S., Li, S.: Healthcare regulation and the operating efficiency of hospitals: Evidence from Taiwan. J. Account. Public Policy 23, 483–510 (2004). http://www.elsevier.com/wps/find/journaldescription.cws_home/505721/description#description doi: 10.1016/j.jaccpubpol.2004.10.004 Google Scholar
  43. 43.
    Banker, R., Charnes, W., Cooper, W.: Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manage. Sci. 30(9), 1078–1092 (1984). http://www.informs.org/site/ManSci/ doi: 10.1287/mnsc.30.9.1078
  44. 44.
    Harrison, J., Coppola, M., Wakefield, M.: Efficiency of federal hospitals in the United States. J. Med. Syst., Oct., vol. 28 (5), pp. 411–422 (2004). http://www.springer.com/statistics/stats+life+sci/journal/10916
  45. 45.
    Chen, A., Yuhchang, H., Shao, B.: Measurement and sources of overall and input inefficiencies: evidences and implications in hospital services. Eur. J. Oper. Res. 161, 447–468 (2005). http://www.elsevier.com/wps/find/journaldescription.cws_home/505543/description#description doi: 10.1016/j.ejor.2003.09.017 Google Scholar
  46. 46.
    Bates, L., Mukherjee, K., Santerre, R.: Market structure and technical efficiency in the hospital services industry: A DEA approach. Med. Care Res. Rev. 63(4), 499–524 (2006). http://mcr.sagepub.com/current.dtl doi: 10.1177/1077558706288842
  47. 47.
    Kontodimopoulos, N., Niakas, D.: Efficiency measurement of hemodialysis units in Greece with data envelopment analysis. Health Policy 71, 195–204 (2005). http://www.elsevier.com/wps/find/journaldescription.cws_home/505962/description#description doi: 10.1016/j.healthpol.2004.08.004 Google Scholar
  48. 48.
    Mobley, L., Magnussen, J.: An international comparison of hospital efficiency. Does institutional environment matter? Appl. Econ. 30(8), 1089–1100 (1998). http://www.tandf.co.uk/journals/titles/00036846.asp doi: 10.1080/000368498325255 Google Scholar
  49. 49.
    Steinman, L., Dittrich, G., Karmann, A., Zweifel, P.: Measuring and comparing the (in)efficiency of German and Swiss hospitals. Eur. J. Health Econ. 5, 216–226 (2004). http://www.springerlink.com/content/110376/ doi: 10.1007/s10198-004-0227-4
  50. 50.
    Linna, M., Häkkinen, U., Magnussen, J.: Comparing hospital cost efficiency between Norway and Finland. Health Policy 77(3), 268–278 (2006). http://www.elsevier.com/wps/find/journaldescription.cws_home/505962/description#description doi: 10.1016/j.healthpol.2005.07.019 Google Scholar
  51. 51.
    Golany, B., Roll, Y.: An application procedure for DEA. Omega. Int. J. Manage. Sci 17, 237–250 (1989)CrossRefGoogle Scholar
  52. 52.
    IGIF–Instituto de Gestão Informática e Financeira da Saúde: Contabilidade Analítica 2004, Hospitais do SNS. Ministério da Saúde. Janeiro, Lisboa (2006)Google Scholar
  53. 53.
    Scheel, H.: EMS: Efficiency Measurement System User’s Manual. http://www.wiso.uni-dortmund.de/lsfg/or/scheel/sem; http://www.netlib.org. (2000)
  54. 54.
    Kuosmanen, T.: Modelling blank data entries in data envelopment analysis”. Nonparametric Methods in Economics of Production, Natural Resources and the Environment, http://www.sls.wageningen-ur.nl/enr/staff/kuosmanen/program1/Kuosmanen (2003)
  55. 55.
    Direcção-Geral da Saúde: O Hospital Português. Ministério da Saúde, Lisboa (1998)Google Scholar
  56. 56.
    Torgersen, A., Forsund, F., Kittlesen, S.: Slack-adjusted efficiency measures and ranking of efficient units. J. Prod. Anal. 7, 379–398 (1996). doi: 10.1007/BF00162048 CrossRefGoogle Scholar
  57. 57.
    Andersen, P., Petersen, N.: A procedure for ranking efficient units in data envelopment analysis. Manage. Sci. 39(10), 1261–1264 (1993). doi: 10.1287/mnsc.39.10.1261 CrossRefGoogle Scholar
  58. 58.
    Adler, N., Friedman, L., Sinuany-Stern, Z.: Review of ranking methods in the data envelopment analysis context. Eur. J. Oper. Res. 140, 249–265 (2002). doi: 10.1016/S0377-2217(02)00068-1 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  1. 1.Faculty of MedicineUniversity of PortoPortoPortugal
  2. 2.Faculty of EconomicsUniversity of PortoPortoPortugal

Personalised recommendations