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Using Data Envelopment Analysis to Analyse the Efficiency of Primary Care Units

  • Systems-Level Quality Improvement
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Abstract

In this paper we analyse the efficiency of primary care centres (PCCs) adopting Information and Communication Technology (ICT) devices, using a new database on primary care centres in the Basque Region in Spain. Using a four-stage Data Envelopment Analysis methodology, we are able to explicitly take into account the role of ICT in affecting the efficiency of primary care centres. We understand that this is the first time that ICT enters into the determination of efficiency of the health sector. The role of exogenous factors is explicitly considered in this analysis and shows that including these variables is not neutral to the efficiency evaluation, but leads to an efficiency indicator that only encompasses the effect of managerial skills. The paper provides some useful policy implications regarding the role of ICT in improving the efficiency of primary care units.

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Notes

  1. As for contributes using parametric techniques to analyse efficiency in primary care centres see Defelice and Bradford [11] and Gaynor and Pauly [19] who analyse efficiency in a sample of US primary care centres and Giuffrida and Gravelle [20] who consider the UK case.

  2. However, the stochastic approach takes stochastic error into account when measuring efficiency. From this perspective, using stochastic DEA would allow to handle appropriately measurement problems and other stochastic influences that would otherwise show up as causes of inefficiency.

  3. DMUs, henceforth.

  4. DEA allows a great flexibility in the choice of weights. However, this may cause efficiency scores to be calculated only on a small subset of inputs and outputs [14].

  5. Input and output slacks can be defined as input excesses and output shortfalls, respectively. In other words, the effective value of the input(output) minus(plus) the slack gives us the efficiency value of inputs (output).

  6. The existence of surgery services and the size of the PCC are examples of environmental variables. Mortality index, population density and birth rate can be instead considered as non-discretionary variables. As explained in the following section, we use mortality rate and the percentage of over 65 as non-discretionary, exogenous variables.

  7. However, because of the correlation among efficiency scores estimated in the first stage, one would need to correct efficiency scores using bootstrapping techniques [45].

  8. Cordero et al. [8] review most of the methods used to take non controllable inputs into account.

  9. CORDERO et al. [9] provide a review of the main methodologies used in order to take exogenous factors into account.

  10. Besides the four stage model, one-stage [2, 18] and two stage [31] and three stage models [14] Estelle et al. [13] have been developed in order to deal with external variables. See Cordero et al. for a review of the most used DEA methods to deal with non discretionary inputs.

  11. As CORDERO et al. [9] highlight, the literature did not provide with a solution for the bias issue. However, they suggest to follow SIMAR and WILSON [45] bootstrap procedure.

  12. See Cordero et al. [8], Murillo and Petraglia [39] and PUIG-JUNOY and ORTUN [43].

  13. Specifically, the number of visits refers to outpatient output. Even if most of the hospital efficiency studies included both inpatient and outpatient outputs (Roand Mutter, 2008), given the peculiar activity of the primary care centres we only use the number of outpatient visits as output. This measure has been consistently used in all of the hospital efficiency studies.

  14. As pointed out by CORDERO-FERRERA et al. [10], assuming VRS help to reduce potential inefficiencies due to the size of the units.

  15. The four-stage model adjust original values of inputs using the least favourable operating environment in order to provide a performance target that managers can attain regardless of their operating environment ([16]; [16], p. 256). As a result, the data are adjusted by increasing the input levels for firms in more favourable circumstances. This is part of the explanation of the real reason why the evaluated units have a lower efficiency score when the four-stage method is applied.

  16. See table in the appendix

  17. Table 5. in the appendix shows the ranking and the efficiency scores calculated in the first and in the fourth stage, respectively.

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Acknowledgments

This study has been funded by Institute for Prospective Technological Studies (IPTS), European Commission’s Joint Research Centre (JRC). The authors would like to thank Roberto Nuño-Solinís and Juan F. Orueta Media who provided us the data utilised in this analysis.

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Authors and Affiliations

  1. Dipartimento di Scienze economiche e Aziendali, Università di Cagliari, Viale S. Ignazio 17, 09123, Cagliari, Italy

    Manuela Deidda

  2. Universitat Oberta de Catalunya, Barcelona, Spain

    Francisco Lupiáñez-Villanueva

  3. Dipartimento di Scienze Sociali e Politiche, Università degli Studi di Milano, Milano, Italy

    Cristiano Codagnone

  4. European Commission - Joint Research Centre, Institute for Prospective Technological Studies (IPTS), Seville, Spain

    Ioannis Maghiros

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  1. Manuela Deidda
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  2. Francisco Lupiáñez-Villanueva
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  4. Ioannis Maghiros
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Correspondence to Manuela Deidda.

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This article is part of the Topical Collection on Systems-Level Quality Improvement

Appendix

Appendix

Table 5 Ranking and efficiency scores of the DMUs calculated in the first and in the fourth stage
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Deidda, M., Lupiáñez-Villanueva, F., Codagnone, C. et al. Using Data Envelopment Analysis to Analyse the Efficiency of Primary Care Units. J Med Syst 38, 122 (2014). https://doi.org/10.1007/s10916-014-0122-1

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