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Journal of Medical Systems
Published in: Journal of Medical Systems 10/2014

01-10-2014 | Systems-Level Quality Improvement

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

Authors: Manuela Deidda, Francisco Lupiáñez-Villanueva, Cristiano Codagnone, Ioannis Maghiros

Published in: Journal of Medical Systems | Issue 10/2014

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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.
Appendix
Available only for authorised users
Footnotes
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.
 
Literature
1.
Adang, E. M., and Wensing, M., Economic barriers to implementation of innovations in health care: is the long runshort run efficiency discrepancy a paradox? Health Policy 88:236–42, 2008.CrossRef
2.
Banker, R. D., and Morey, R. C., Efficiency analysis for exogenously fixed inputs and outputs. Operations Research 34:513–521, 1986.MATHCrossRef
3.
Black, A. D., Car, J., Pagliari, C., Anandan, C., Cresswell, K., Bokun, T., Mckinstry, B., Procter, R., Majeed, A., and Sheikh, A., The impact of eHealth on the quality and safety of health care: a systematic overview. PLoS Med 8:e1000387, 2011.CrossRef
4.
Bresnahan, T. F., and Trajtenberg, M., General purpose technologies : “engines of growth ?”. National Bureau of Economic Research, Cambridge, 1992.CrossRef
5.
Charnes, A., Cooper, W. W., and Rhodes, E., Measuring the efficiency of decision making units. European Journal of Operational Research 2:429–444, 1978.MathSciNetMATHCrossRef
6.
Chaudhry, B., Wang, J., Wu, S., Maglione, M., Mojica, W., Roth, E., Morton, S. C., and Shekelle, P. G., Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med 144:742–52, 2006.CrossRef
7.
Christensen, M., and Remler, D. K., Information and communications technology in chronic disease care : why is adoption so slow and is slower better? National Bureau of Economic Research, Cambridge, 2007.
8.
Cordero, J. M., Pedraja, F., and Salinas, J., Measuring efficiency in education: an analysis of different approaches for incorporating non-discretionary inputs. Applied Economics 40(10):1323–1339, 2008.CrossRef
9.
Cordero, J. M., Pedraja, F., and Santin, D., Enhancing the inclusion of non-discretionary inputs in DEA. Journal of Operational Research Society 61:574–584, 2010.CrossRef
10.
Cordero-Ferrera, J. M., Crespo-Cebada, E., and Murillo-Zamorano, L. R., Measuring technical efficiency in primary health care: the effect of exogenous variables on results. J Med Syst 35:545–54, 2011.CrossRef
11.
Defelice, L. C., and Bradford, W. D., Relative inefficiencies in production between solo and group practice physicians. Health Economics 6:455–465, 1997.CrossRef
12.
Devaraj, S., and Kohli, R., Information technology payoff in thehealth-care industry: a longitudinal study. Journal of Management Information Systems 16:41–67, 2000.
13.
Estelle, S. M., Johnson, A., and Ruggiero, J., Three-stage DEA models for incorporating exogenous inputs. Computers and Operations Research 37(6):1087–1090, 2010.MathSciNetMATHCrossRef
14.
Filipe Amado, C. A., and Dyson, R. G., On comparing the performance of primary care providers. European Journal of Operational Research 185:915–932, 2008.MATHCrossRef
15.
Fried, H. O. & Lovell, C. A. K. 1996. Fried and Lovell (1996) “searching for the zeds”, paper presented at II georgia productivity workshop. II Georgia Productivity Workshop.
16.
Fried, H. O., Schmidt, S. S., and Yaisawarng, S., Incorporating the operating environment into a nonparametric measure of technical efficiency. Journal of Productivity Analysis 12:249–267, 1999.CrossRef
17.
Fried, H. O., Lovell, C. A. K., Schmidt, S. S., and Yaisawarng, S., Accounting for environmental effects and statistical noise in data envelopment analysis. Journal of Productivity Analysis 17:157–174, 2002.CrossRef
18.
García, F., Marcuello, C., Serrano, D., and Urbina, O., Evaluation of efficiency in primary health care centres: an application of data envelopment analysis. Financial Accountability & Management 15:67–83, 1999.CrossRef
19.
Gaynor, M., and Pauly, M. V., Compensation and productive efficiency in partnerships: evidence form medical groups practice. The Journal of Political Economy 98(3):544–573, 1990.CrossRef
20.
Giuffrida, A., and Gravelle, H., Measuring performances in primary care: econometric analysis and DEA. Applied Economics 33:163–175, 2001.CrossRef
21.
Golany, B., and Roll, Y., Some extensions of techniques to handle Non-discretionary factors in data envelopment analysis. Journal of Productivity Analysis 4:419–432, 1993.CrossRef
22.
Hollingsworth, B., Non-parametric and parametric applications measuring efficiency in health care. Health Care Manag Sci 6:203–18, 2003.CrossRef
23.
Hollingsworth, B., The measurement of efficiency and productivity of health care delivery. Health Econ 17:1107–28, 2008.CrossRef
24.
Hollingsworth, B., and Street, A., The market for efficiency analysis of health care organisations. Health Econ 15:1055–9, 2006.CrossRef
25.
Hollingsworth, B., Dawson, P. J., and Maniadakis, N., Efficiency measurement of health care: a review of non-parametric methods and applications. Health Care Manag Sci 2:161–72, 1999.CrossRef
26.
Huang, Y. G., and Mclaughlin, C. P., Relative efficiency in rural primary health care: an application of data envelopment analysis. Health Serv Res 24:143–58, 1989.
27.
Hussey, P. S., De Vries, H., Romley, J., Wang, M. C., Chen, S. S., Shekelle, P. G., and Mcglynn, E. A., A systematic review of health care efficiency measures. Health Serv Res 44:784–805, 2009.CrossRef
28.
29.
Ko, M., and Osei-Bryson, K. M., The productivity impact of information technology in the healthcare industry: an empirical study using a regression spline-based approach. Information and Software Technology 46:65–73, 2004.CrossRef
30.
Ko, M., and Osei-Bryson, K. M., Using regression splines to assess the impact of information technology investments on productivity in the health care industry. Information Systems Journal 14:43–63, 2004.CrossRef
31.
Ko, M., and Osei-Bryson, K. M., Reexamining the impact of information technology investment on productivity using regression tree and multivariate adaptive regression splines (MARS). Journal Information Technology and Management 9:285–299, 2008.CrossRef
32.
Lapointe, L., Mignerat, M., and Vedel, I., The IT productivity paradox in health: a stakeholder’s perspective. Int J Med Inform 80:102–15, 2011.CrossRef
33.
Lau, F., Kuziemsky, C., Price, M., and Gardner, J., A review on systematic reviews of health information system studies. J Am Med Inform Assoc 17:637–45, 2010.CrossRef
34.
Menon, N., and Lee, B., Cost control and production performance enhancement by IT investment and regulation changes: evidence from the healthcare industry. Decision Support Systems 30:153–169, 2000.CrossRef
35.
Meyer, R., and Degoulet, P., Assessing the capital efficiency of healthcare information technologies investments: an econometric perspective. Yearb Med Inform 114–27, 2008.
36.
Meyer, R., and Degoulet, P., Choosing the right amount of healthcare information technologies investments. Int J Med Inform 79:225–31, 2010.CrossRef
37.
Meyer, R., Degoulet, P., and Omnes, L., Impact of health care information technology on hospital productivity growth: a survey in 17 acute university hospitals. Stud Health Technol Inform 129:203–7, 2007.
38.
Muñiz, M., Separating managerial inefficiency and external conditions in data. European Journal of Operational Research 143(3):625–643, 2002.MATHCrossRef
39.
Murillo, L. R., and Petraglia, C., Technical efficiency in primary health care: does quality matter?”, Working paper #10725, MPRA, University Library of Munich, 2008.
40.
Nunamaker, T. R., Using data envelopment analysis to measure the efficiency of non-profit organisations: a critical evaluation. Managerial and Decision Economics 6:50–58, 1985.CrossRef
41.
Nuno-Solinis, R., Orueta, J. F., and Mateos, M., An answer to chronicity in the basque country: primary care-based population health management. J Ambul Care Manage 35:167–73, 2012.CrossRef
42.
OECD, Improving health sector efficiency : the role of information and communication technologies. OECD, Paris, 2010.
43.
Puig-Junoy, J., and Ortun, V., Cost efficiency in primary care contracting: a stochastic frontier cost function approach. Health Econ 13:1149–1165, 2004.CrossRef
44.
Ruggiero, J., Non-discretionary inputs in data envelopment analysis. European Journal of Operational Research 111:461–469, 1998.MATHCrossRef
45.
Simar, L. & Wilson, P. 2003. Estimation and inference in twostage, semi-parametric models of production processes. Discussion paper no. 0307 Institut de Statistique, Université Catholique de Louvain.
46.
47.
Smith, P. C., and Häkkinen, U., Information strategies for decentralisation. In: Saltman, R. B., Bankauskaite, V., and Vrangbaek, K. (Eds.), Decentralisation in health care. Strategies and outcomes. World Health Organization, Geneva, 2007.
48.
Street, A., The contribution of ICT to health care system productivity and efficiency: what do we know? OECD, Paris, 2007.
49.
Whitten, P. S., Mair, F. S., Haycox, A., May, C. R., Williams, T. L., and Hellmich, S., Systematic review of cost effectiveness studies of telemedicine interventions. BMJ 324:1434–7, 2002.CrossRef
50.
Xue, M., and Patrick, T. H., Overcoming the inherent dependency of DEA efficiency scores: a bootstrap approach. Unpublished Working Paper, Wharton Financial Institutions Center, University of Pennsylvania, 1999.
Metadata
Title
Using Data Envelopment Analysis to Analyse the Efficiency of Primary Care Units
Authors
Manuela Deidda
Francisco Lupiáñez-Villanueva
Cristiano Codagnone
Ioannis Maghiros
Publication date
01-10-2014
Publisher
Springer US
Published in
Journal of Medical Systems / Issue 10/2014
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-014-0122-1

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