Measuring an efficiency aggregation of medical diagnostic laboratories: A window NDEA approach
Article ID: 3138
Vol 6, Issue 1, 2023
Vol 6, Issue 1, 2023
VIEWS - 1431 (Abstract)
Abstract
The efficiency evaluation of laboratories, as one of the most significant areas of healthcare, plays a key role in the quality of laboratory management. The classic data envelopment analysis (DEA) models have overlooked intermediate products, internal interactions and dealt with analyzing the network within the “Black Box” mode. This results in the loss of important information, and at times, a considerable modification occurs in efficiency results. This article evaluated the efficiency of some selected medical diagnostic laboratories in the city of Tehran according to the network data envelopment analysis (NDEA) approach. We considered a four-stage structure with additional inputs and undesirable outputs. We obtain the labs’ performance over a period of 6 months in 2022 by the NDEA window analysis process. To this aim, a four-stage structure model of three chief medical diagnostic laboratory processes as the pre-test, the test, and the post-test is designed. We considered sustainability criteria (economic, social, and environmental) to appraise the performance of laboratories, thus helping to improve the social, economic, and environmental problems of medical diagnostic laboratories. By using the Delphi viewpoint, the criteria for efficiency evaluation are achieved. The results showed that laboratory unit No. 22 maintained the highest average overall efficiency, since the high accuracy of this unit’s laboratory results had led to many physicians recommending this unit to their patients. We found that the only laboratory unit No. 20 had a decreasing trend, as it is located in an area that abounds with administrative and educational centers. At the beginning of the exam period, then the summer holidays, and finally the wave of end-of-summer trips, a decline occurs in efficiency over the period of six months.
Keywords
network data envelopment analysis; four stage process; sustainability criteria; medical diagnostic laboratories; window analysis
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Vaezi E, Najafi SE, Hajimolana SM, et al. Production planning and efficiency evaluation of a three-stage network. Journal of Industrial and Systems Engineering 2020; 13(2): 155–178.
Lee T, Zhang Y, Jeong B H. A multi-period output DEA model with consistent time lag effects. Computers & Industrial Engineering 2016; 93: 267–274. doi: 10.1016/j.cie.2016.01.003
Farrell MJ. The measurement of productive efficiency. Journal of the Royal Statistical Society Series A: (General) 1957; 120(3): 253–281. doi: 10.2307/2343100
Vaezi E, Najafi SE, Haji Molana M, et al. Measuring performance of a three-stage structure using data envelopment analysis and Stackelberg game. Journal of Industrial and Systems Engineering 2019; 12(2): 151–173.
Vaezi E, Najafi SE, Haji Molana SM, et al. Measuring performance of a hybrid system based on imprecise data: Modeling and solution approaches. Journal of Industrial Engineering and Management Studies 2019; 6(2): 214–241. doi: 10.22116/jiems.2019.94192
Avilés-Sacoto SV, Cook WD, Güemes-Castorena D, et al. Evaluating the efficiencies of academic research groups: A problem of shared outputs. Asia-Pacific Journal of Operational Research 2018; 35(6): 1850042. doi: 10.1142/S0217595918500422
Kritikos MN. A full ranking methodology in data envelopment analysis based on a set of dummy decision making units. Expert Systems with Applications 2017; 77(77): 211–225. doi: 10.1016/j.eswa.2017.01.042
Lewis HF, Sexton TR. Network DEA: Efficiency analysis of organizations with complex internal structure. Computers & Operations Research 2004; 31(9): 1365–1410. doi: 10.1016/S0305-0548(03)00095-9
Fare R, Grosskopf S. Network DEA. Socio Economics Planning Science 2000; 34(1): 35–49. doi: 10.1016/S0038-0121(99)00012-9
Kou M, Chen K, Wang S, Shao Y. Measuring efficiencies of multi-period and multi-division systems associated with DEA: An application to OECD countries’ national innovation systems. Expert Systems with Applications 2016; 46(2): 494–510. doi: 10.1016/j.eswa.2015.10.032
Wanke P, Barros C. Two-stage DEA: An application to major Brazilian banks. Expert Systems with Applications 2014; 41(5): 2337–2344. doi: 10.1016/j.eswa.2013.09.031
Afzalinejad M, Abbasi Z. A slacks-based model for dynamic data envelopment analysis. Journal of Industrial and Management Optimization 2019; 15(1): 275–291. doi: 10.3934/jimo.2018043
Kao C, Hwang SN. Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research 2008; 185(1): 418–429. doi: 10.1016/j.ejor.2006.11.041
Cook WD, Zhu J. Data Envelopment Analysis: A Handbook of Modeling. Internal Structure and Network. Springer; 2014. doi: 10.1007/978-1-4899-8068-7
Kao C. Efficiency decomposition in network data envelopment analysis: A relational model. European Journal of Operational Research 2009; 192(3): 949–962. doi: 10.1016/j.ejor.2007.10.008
Yu MM, Lin ETJ. Efficiency and effectiveness in railway performance using a multi-activity network DEA model. Omega 2008; 36(6): 1005–1017. doi: 10.1016/j.omega.2007.06.003
Fare R, Grosskopf S, Lovell CAK, Pasurka C. Multilateral productivity comparisons when some outputs are undesirable: A nonparametric approach. The Review of Economics and Statistics 1989; 71(1): 90–98. doi: 10.2307/1928055
Seiford LM, Zhu J. Modeling undesirable factors in efficiency evaluation. European Journal of Operational Research 2002; 142(1): 16–20. doi: 10.1016/S0377-2217(01)00293-4
Badiezadeh T, Farzipoor Saen R. Efficiency evaluation of production lines using maximal balance index. International Journal of Management and Decision Making 2014; 13(3): 302–317. doi: 10.1504/IJMDM.2014.063573
Jahanshahloo GR, Lotfi FH, Shoja N, Tohidi G. Undesirable inputs and outputs in DEA models. Applied Mathematics and Computation 2005; 169(2): 917–925. doi: 10.1016/j.amc.2004.09.069
Lu WM, Lo SF. A closer look at the economic-environmental disparities for regional development in China. European Journal of Operational Research 2007; 183(2): 882–894. doi: 10.1016/j.ejor.2006.10.027
Golany B, Phillips FY, Rousseau JJ. Few-wave vs. continuous consumer panels: Some issues of attrition, bias and variance. International Journal of Research in Marketing 1991; 8(3): 273–280. doi: 10.1016/0167-8116(91)90016-Z
Flokou A, Aletras V, Niakas D. A window-DEA based efficiency evaluation of the public hospital sector in Greece during the 5-year economic crisis. PloS One 2017; 12(5): e0177946. doi: 10.1371/journal.pone.0177946
Martins AA, Mata TM, Costa CAV, Sikdar SK. Framework for sustainability metrics. Industrial & Engineering Chemistry Research 2007; 46(10): 2962–2973. doi: 10.1021/ie060692l
Chen C, Zhu J, Yu JY, Noori H. A new methodology for evaluating sustainable product design performance with two-stage network data envelopment analysis. European Journal of Operational Research 2012; 221(2): 348–359. doi: 10.1016/j.ejor.2012.03.043
Pope J, Annandale D, Morrison-Saunders A. Conceptualising sustainability assessment. Environmental Impact Assessment Review 2004; 24(6): 595–616. doi: 10.1016/j.eiar.2004.03.001
Gerdessen JC, Pascucci S. Data Envelopment Analysis of sustainability indicators of European agricultural systems at regional level. Agricultural Systems 2013; 118: 78–90. doi: 10.1016/j.agsy.2013.03.004
Galán-Martín Á, Guillén-Gosálbez G, Stamford L, et al. Enhanced data envelopment analysis for sustainability assessment: A novel methodology and application to electricity technologies. Computers & Chemical Engineering 2016; 90: 188–200. doi: 10.1016/j.compchemeng.2016.04.022
Zhang B, Bi J, Fan Z, et al. Eco-efficiency analysis of industrial system in China: A data envelopment analysis approach. Ecological Economics 2008; 68(1–2): 306–316. doi: 10.1016/j.ecolecon.2008.03.009
Zhou P, Ang B W, Poh KL. A survey of data envelopment analysis in energy and environmental studies. European Journal of Operational Research 2008; 189(1): 1–18. doi: 10.1016/j.ejor.2007.04.042
Khanjarpanah H, Pishvaee MS, Seyedhosseini SM. A risk averse cross-efficiency data envelopment analysis model for sustainable switchgrass cultivation location optimization. Industrial Crops and Products 2017; 109: 514–522. doi: 10.1016/j.indcrop.2017.09.005
Tajbakhsh A, Hassini E. Evaluating sustainability performance in fossil-fuel power plants using a two-stage data envelopment analysis. Energy Economics 2018; 74: 154–178. doi: 10.1016/j.eneco.2018.05.032
Khan M, Hussain M, Gunasekaran A, et al. Motivators of social sustainability in healthcare supply chains in the UAE—Stakeholder perspective. Sustainable Production and Consumption 2018; 14: 95–104. doi: 10.1016/j.spc.2018.01.006
Khalafi S, Khalilzadeh M, Razaghi S, Vaezi E. Investigating the impact of the internet of things on the performance of knowledge areas of project management. International Journal of Internet Manufacturing and Services 2021; 8(2): 89–102. doi: 10.1504/IJIMS.2021.122702
Korhonen PJ, Luptacik M. Eco-efficiency analysis of power plants: An extension of data envelopment analysis. European Journal of Operational Research 2004; 154(2): 437–446. doi: 10.1016/S0377-2217(03)00180-2
Chen Y, Cook WD, Li N, Zhu J. Additive efficiency decomposition in two-stage DEA. European Journal of Operational Research 2009; 196(3): 1170–1176. doi: 10.1016/j.ejor.2008.05.011
Chen Y, Liang L, Zhu J. Equivalence in two-stage DEA approaches. European Journal of Operational Research 2009; 193(2): 600–604. doi: 10.1016/j.ejor.2007.11.040
Charnes A, Cooper WW. Programming with linear fractional functionals. Naval Research Logistics Quarterly 1962; 9(3–4): 181–186. doi: 10.1002/nav.3800090303
Vlontzos G, Pardalos PM. Assess and prognosticate green house gas emissions from agricultural production of EU countries, by implementing, DEA Window analysis and artificial neural networks. Renewable and Sustainable Energy Reviews 2017; 76: 155–162. doi: 10.1016/j.rser.2017.03.054
Vaezi E. Performance measurement in the health care sector: A leader-follower network DEA model based on double frontier analysis. Scientia Iranica 2022; 29(3): 1662–1684. doi: 10.24200/SCI.2020.53718.3382
Vaezi E. Measuring the performances of Medical Diagnostic Laboratories based on interval efficiencies. Journal of Optimization in Industrial Engineering 2021; 14(2): 137–154. doi: 10.22094/JOIE.2019.1864217.1635
Vaezi E, Najafi SE, Haji Molana M, et al. Efficiency evaluation in hybrid three-stage network data envelopment analysis from the double-frontier standpoint. International Journal of Supply and Operations Management 2020; 7(3): 222–241. doi: 10.22034/IJSOM.2020.3.2
Vaezi E, Najafi SE, Hajimolana SM, et al. Efficiency evaluation of a three-stage leader-follower model by data envelopment analysis with double-frontier viewpoint. Scientia Iranica 2021; 28(1): 492–515. doi: 10.24200/SCI.2019.51980.2459
DOI: https://doi.org/10.24294/mipt.v6i1.3138
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