This study is aimed to measure the efficiency of the regional and district hospitals of Burkina Faso and to find the benchmarks for inefficient hospital centres.

Data envelopment analysis was used to evaluate efficiency and identification of the benchmarks for the inefficient hospitals. The input variables employed within the scope of data envelopment analysis are total nursing workforce; total ward boys or ward girls; total of midwives and state midwives; total of physicians (specialists and generalists). The study’s output variables were the average stay of observation in emergency service; the rate of lethality of malaria for children under five years and the percentage of births performed with partograms.

According to data envelopment analysis results, we have an average efficiency score of 0.943. This means that the set under consideration of works at 94.30% compared to what it could have done better. 60% of these hospitals are considered technically efficient and 40% may be peer referrals for hospitals considered inefficient.

The results obtained show that the benchmarks of inefficient hospitals are not necessarily close, in terms of distance between localities (cities) or in terms of similarities of populations.

data envelopment analysis, technical efficiency, benchmarking, hospital performance indicators, performance evaluation, undesirable output.

Received: May 5, 2023; Accepted: July 1, 2023; Published: January 31, 2024

How to cite this article: Wendpanga Jacob Yougbaré, Moumouni Zongo and Boureima Sory, DEA - benchmarking to assess the performance of regional hospitals in Burkina Faso, JP Journal of Biostatistics 24(1) (2024), 141-160. http://dx.doi.org/10.17654/0973514324010

This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License

References:
[1] A. Charnes, W. W. Cooper and E. Rhodes, Measuring the efficiency of decision making units, European J. Oper. Res. 2 (1978), 429-444.
[2] W. W. Cooper, L. M. Seiford and J. Zhu, Handbook on Data Envelopment Analysis, Kluwer Academic Publishers, 2004.
[3] Negar Darabi, Alireza Ebrahimvandi, Niyousha Hosseinichimeh and Konstantinos Triantis, A DEA evaluation of U.S. states’ healthcare systems in terms of their birth outcomes, Expert Systems with Applications 182 (2021), 115278.
[4] Burcay Erus and Ozan Hatipoglu, Physician payment schemes and physician productivity: analysis of Turkish healthcare reforms, Health Policy 121(5) (2017), 553-557.
[5] A. Ettorchi-Tardy, M. Levif and P. Michel, Le benchmarking : une méthode d’amélioration continue de la qualité en santé, Pratiques et Organisation des Soins 42 (2011), 35-46.
[6] Diogo Cunha Ferreira, Alexandre Morais Nunes and Rui Cunha Marques, Doctors, nurses, and the optimal scale size in the Portuguese public hospitals, Health Policy 122(10) (2018), 1093-1100.
[7] Bruce Hollingsworth, The measurement of efficiency and productivity of healthcare delivery, Health Econ 17(10) (2008), 1107-1128.
[8] Bruce Hollingsworth and Stuart J. Peacock, Efficiency Measurement in Health and Health Care, Taylor & Francis, 2008, pp. 1-157.
[9] Pyb Mané, Analyse de l’efficience des hôpitaux du Sénégal : application de la méthode d’enveloppement des données, Pratiques et Organisation des Soins 43(4) (2012), 277-283.
[10] Y. A. Ozcan, Health care benchmarking and performance evaluation an assessment using data envelopment analysis (DEA), Journal of Operations Research and Management Science 2 (2014), 141-289.
[11] M. G. M. Peixoto, M. A. Musetti and M. C. A. de Mendonça, Performance management in hospital organizations from the perspective of principal component analysis and data envelopment analysis: the case of federal university hospitals in Brazil, Computers and Industrial Engineering 150 (2020), 1-13.
[12] Subramania Raju Rajasulochana and Po-Chi Chen, Efficiency and productivity changes in the presence of undesirable outcomes in emergency obstetric and newborn care, Journal of Policy Modeling 41(5) (2019), 905-925.
[13] M. Top, M. Konca and B. Sapaz, Technical efficiency of healthcare systems in African countries: an application based on data envelopment analysis, Health Policy and Technology 9(1) (2020), 62-68.
[14] Sun Yaya, Chen Xi, Zhou Xiaoyang and Zhang Meixia, Evaluating the efficiency of China’s healthcare service: a weighted DEA-game theory in a competitive environment, Journal of Cleaner Production 270 (2020), 122431.
[15] Jacob Wendpanga Yougbaré, Méthodes d’Aide à la Décision Appliquées pour le développement au Burkina Faso, La méthodologie Data Envelopment Analysis (DEA), etudes africaines ed., L’harmattan, 2014.
[16] Jacob Wendpanga Yougbaré, Analysis of the performances of the health system in Burkina Faso, International Journal of Scientific and Engineering Research 9(12) (2018), 882-889.
[17] Jacob Wendpanga Yougbaré, Analysis of agricultural performance in Burkina Faso using data envelopment analysis, European Journal of Pure and Applied Mathematics 14(2) (2021), 366-379.
[18] Jacob Wendpanga Yougbaré and Jacques Teghem, Relationships between Pareto optimality in multi-objective 0-1 linear programming and DEA efficiency, European J. Oper. Res. 183(3) (2007), 608-617.