Top

Published in:

01-01-2016 | Systems-Level Quality Improvement

Assessing the Queuing Process Using Data Envelopment Analysis: an Application in Health Centres

Authors: Komal A. Safdar, Ali Emrouznejad, Prasanta K. Dey

Published in: Journal of Medical Systems | Issue 1/2016

Abstract

Queuing is one of the very important criteria for assessing the performance and efficiency of any service industry, including healthcare. Data Envelopment Analysis (DEA) is one of the most widely-used techniques for performance measurement in healthcare. However, no queue management application has been reported in the health-related DEA literature. Most of the studies regarding patient flow systems had the objective of improving an already existing Appointment System. The current study presents a novel application of DEA for assessing the queuing process at an Outpatients’ department of a large public hospital in a developing country where appointment systems do not exist. The main aim of the current study is to demonstrate the usefulness of DEA modelling in the evaluation of a queue system. The patient flow pathway considered for this study consists of two stages; consultation with a doctor and pharmacy. The DEA results indicated that waiting times and other related queuing variables included need considerable minimisation at both stages.

ww. DEAsoftware.co.uk

Adeleke, R. A., Ogunwale, O. D., and Halid, O. Y., Application of queuing theory to waiting time of out-patients in hospitals. Pac. J. Sci. Technol. 10(2):270–274, 2009.

Ramanathan, R., Operations assessment of hospitals in the Sultanate of Oman. Int. J. Prod. Oper. Manag. 25(1):39–54, 2005.CrossRef

Matta, M. E., and Patterson, S. S., Evaluating multiple performance measures across several dimensions at a multi-facility outpatient center. Healthc. Manag. Sci. 10(2):173–194, 2007.CrossRef

Mehandiritta, R., Applications of queuing theory in healthcare. Int. J. Comput. Bus. Res. 2(2):2229–6166, 2011.

Chuang, C. L., Chang, P. C., and Lin, R. H., An Efficiency data envelopment analysis model reinforced by classification and regression tree for hospital performance evaluation. J. Med. Syst. 35(5):1075–1083, 2011.PubMedCrossRef

Biju, M.K. and Naeema, K., Application of queuing theory in human resource management in healthcare, In: ICOQM-10, pp. 1019–1027, 2001.

Jun, J. B., Jacobson, S. H., and Swisher, J. R., Application of discrete-event simulation in healthcare clinics: A survey. J. Oper. Res. Soc. 50(2):109–123, 1999.CrossRef

Silva, F., and Serra, D., Locating emergency services with different priorities: The priority queuing covering location problem. J. Oper. Res. Soc. 59(9):1229–1238, 2008.CrossRef

Yeboah, E.K. and Thomas, M.E., A cost-effective way of reducing outpatient clinic waiting times: How we did it. Int. J. Healthc. Admin. 7(1), 2010.

10.

Babes, M., and Sarma, G. V., Outpatient queues at the Ibn-Rochd health centre. J. Oper. Res. Soc. 42(10):845–855, 1991.CrossRef

11.

Manzi, A, Magge H, Hedt-Gauthier, B.L., Michaelis, A.P., Cyamatare, F.R., Nyirazinyoye, L, Hirschhorn, L.R. and Ntaganira, J., Clinical mentorship to improve pediatric quality of care at the health centers of rural rwanda: A qualitative study of perceptions and acceptability of health workers. BMC Health Serv. Res. 14(275), 2014. Available at: http://www.biomedcentral.com/1472-6963/14/275 [Accessed: 27 Sept 2015].

12.

Mensah, J., Asamoah, J., and Tawiah, A. A., Optimizing patient flow and resource utilization in outpatient clinic: A comparative study of Nkawie government hospital and Aniwaa health center. J. Appl. Bus. Econ. 16(3):181–188, 2015.

13.

Bhattacharjee, P., and Ray, P. K., Patient flow modelling and performance analysis of healthcare delivery processes in hospitals: A review and reflections. Comput. Ind. Eng. 78:299–312, 2014.CrossRef

14.

Proudlove, N. C., Black, S., and Fletcher, A., OR and the challenge to improve the NHS: Modeling for insight and improvement in in-patient flows. J. Oper. Res. Soc. 58(2):145–158, 2007.

15.

Cayirli, T., and Veral, E., Outpatient scheduling in healthcare: A review of literature. J. Med. Syst. 35(5):1075–1083, 2003.

16.

Gul, M., and Guneri, A. F., A comprehensive review of emergency department simulation applications for normal and disaster conditions. Comput. Ind. Eng. 83:327–344, 2015.CrossRef

17.

May, J. H., Spangler, W. E., and Strum, D. P., The surgical scheduling problem: Current research and future opportunities. Prod. Oper. Manag. 20(3):392–405, 2011.CrossRef

18.

Ashton, R., Hague, L., Brandreth, M., Worthington, D., and Cropper, S., A simulation-based study of a NHS walk-in centre. J. Oper. Res. Soc. 56(2):153–161, 2005.CrossRef

19.

Cayirli, T., and Gunes, E. D., Outpatient appointment scheduling in presence of seasonal walk-ins. J. Oper. Res. Soc. 65:512–531, 2014.

20.

Fetter, R. B., and Thompson, J. D., Patients’ waiting time and doctors’ idle time in the outpatient setting. Health Serv. Res. 1(1):66–90, 1966.PubMedPubMedCentral

21.

Rising, E., Baron, R., and Averill, B., A system analysis of a university health service outpatient clinic. Oper. Res. 21(5):1030–1047, 1973.CrossRef

22.

Fomundam, S., and Hermann, J.W., A survey of queuing theory applications in healthcare, Digital Repository at the University of Maryland, 2007. Available at: http://hdl.handle.net/1903/7222. [Accessed: 19 July 2013].

23.

Lakshmi, C., and Sivakumar, A. I., Application of queuing theory in healthcare: A literature review. Oper. Res. Healthc. 2(1–2):25–39, 2013.

24.

Mayhew, L., and Smith, D., Using queuing theory to analyse the government’s 4-h completion time target in accident and emergency departments. Healthc. Manag. Sci. 11(1):11–21, 2008.CrossRef

25.

Gunal, M., and Pidd, M., Discrete-event simulation for performance modeling in healthcare: A review of the literature. J. Simul. 4:42–51, 2010.CrossRef

26.

Konrad, R., DeSotto, K., Grocela, A., McAuley, P., Wang, J., Lyons, J., and Bruin, M., Modelling the impact of changing patient flow processes in an emergency department: insights from a computer simulation study. Oper. Res. Healthc. 2(4):66–74, 2013.

27.

Brailsford, S. C., Lattimer, V. A., Tarnaras, P., and Turnbull, J. C., Emergency and on-demand healthcare: Modeling a large complex system. J. Oper. Res. Soc. 55:34–42, 2004. Case-Oriented Paper.CrossRef

28.

Gunal, M. M., A guide for building hospital simulation models. Health Syst. 1:17–25, 2012.CrossRef

29.

Lane, D. C., Monefeldt, C., and Rosenhead, J. V., Looking in the wrong place for healthcare improvements: A system dynamics study of an accident and emergency department. J. Oper. Res. Soc. 51(5):518–531, 2000.CrossRef

30.

Pelone, F., Kringos, D.S., Romaniello, A., Archibugi, M., Salsiri, C., and Ricciardi, W., Primary care efficiency measurement using data envelopment analysis: A systematic review. J. Med. Syst. 39(1): 156, 2015.

31.

O’Neill, L., Rauner, M., Heidenberger, K., and Kraus, M., A cross-national comparison and taxonomy of DEA-based hospital efficiency studies. Socio Econ. Plan. Sci. 42(3):158–189, 2008.CrossRef

32.

Liu, J. S., Lu, L. Y. Y., Lu, W., and Lin, B. J. Y., A survey of DEA applications. Omega 41(5):893–902, 2013.CrossRef

33.

Liu, J. S., Lu, L. Y. Y., Lu, W., and Lin, B. J. Y., Data envelopment analysis 1978–2010: A citation-based literature survey. Omega 41(1):3–15, 2013.CrossRef

34.

Akazili, J., Adjuik, M., Appiah, C. J., and Zere, E., Using data envelopment analysis to measure the extent of technical efficiency of public health centers in Ghana. Bio. Med. Central Ltd. 20(2):232–248, 2008.

35.

Flokou, A., Kontodimopoulos, N., and Niakas, D., Employing post-DEA cross-evaluation and cluster analysis in a sample of Greek NHS hospitals. J. Med. Syst. 35(5):1001–1014, 2011.PubMedCrossRef

36.

Kawaguchi, H., Tone, K., and Tsutsui, M., Estimation of the efficiency of Japanese hospitals using a dynamic and network data envelopment analysis model. Healthc. Manag. Sci. 17:101–112, 2014.CrossRef

37.

Nunamaker, T. R., Measuring routine nursing service efficiency: A comparison of cost per day and data envelopment analysis models. Health Serv. Res. 18(2):183–208, 1983.PubMedPubMedCentral

38.

Ouellette, P., and 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(3):755–763, 2004.CrossRef

39.

Parkin, D., and Hollingsworth, B., Measuring production efficiency of acute hospitals in Scotland, 1991–94: Validity issues in data envelopment analysis. Appl. Econ. 29(11):1425–1433, 1997.CrossRef

40.

Hollingsworth, B., The measurement of efficiency and productivity of healthcare delivery. Health Econ. 17(10):1107–1128, 2008.PubMedCrossRef

41.

Sherman, H. D., Improving the Productivity of Service Businesses. Sloan. Manage. Rev. 25(3):11–23, 1984.PubMed

42.

Hollingsworth, B., Revolution, evolution or status-quo? Guidelines for efficiency measurement in healthcare. J. Prod. Anal. 37(1):1–5, 2012.CrossRef

43.

Worthington, A. C., Frontier efficiency measurement in healthcare: A review of empirical techniques and selected applications. Med. Care Res. Rev. 61(2):135–170, 2004.PubMedCrossRef

44.

Chang, H., Cheng, M. A., and Das, S., Hospital ownership and operating efficiency: Evidence from Taiwan. Eur. J. Oper. Res. 159(2):513–527, 2004.CrossRef

45.

Jehu-Appiah, C., Sekidde, S., Adjuik, M., Akazili, J., Almeida, S.D., Nyonator, F., Baltussen, R., Asbu, E.Z. and Kirigia, J.M., Ownership and technical efficiency of hospitals: Evidence from Ghana using data envelopment analysis. Cost Effect. Res. Allocation, 12:9, 2015. Available at: http://www.resource-allocation.com/content/12/1/9 [Accessed: 8 June 2015].

46.

Ramirez-Valdivia, M. T., Maturana, S., and Salvo-Garrido, S., A multiple-stage approach for performance improvement of primary healthcare practice. J. Med. Syst. 35(5):1015–1028, 2011.PubMedCrossRef

47.

Ersoy, K., Kavuncubasi, S., Ozcan, Y. A., and Harris, J. M., II, Technical efficiencies of Turkish hospitals: DEA approach. J. Med. Syst. 21(2):67–74, 1997.PubMedCrossRef

48.

Hollingsworth, B., and Parkin, D., The efficiency of Scottish acute hospitals: An application of data envelopment analysis. IMA J. Math. Appl. Med. Biol. 12(3–4):161–173, 1995.PubMedCrossRef

49.

Puig-Junoy, J., Partitioning input cost efficiency into its allocative and technical components: An Empirical DEA application to hospitals. Socio Econ. Plan. Sci. 34(3):199–218, 2000.CrossRef

50.

Tsai, P. F., and Molinero, C. M., A variable returns to scale data envelopment analysis model for the joint determination of efficiencies with an example of the UK health service. Eur. J. Oper. Res. 141(1):21–38, 2002.CrossRef

51.

Salinas-Jimenez, J., and Smith, P., Data envelopment analysis applied to quality in primary healthcare. Ann. Oper. Res. 67(1):141–161, 1996.CrossRef

52.

Thanassoulis, E., Boussofiane, A., and Dyson, R. G., A comparison of DEA and ratio analysis as tools for performance measurement. Omega 24(3):229–244, 1996.CrossRef

53.

Chilingerian, J. A., and Sherman, H. D., DEA and primary care physician report cards: Deriving preferred practice cones from managed care service concepts and operating strategies. Ann. Oper. Res. 73:35–66, 1997.CrossRef

54.

Wagner, J. M., Shimshak, D. G., and Novak, M. A., Advances in physician profiling: The use of DEA. Socio Econ. Plan. Sci. 37(2):141–163, 2003.CrossRef

55.

Osman, I. H., Berbary, L. N., Sidani, Y., Al-Ayoubi, B., and Emrouznejad, A., Data envelopment analysis model for the appraisal and relative performance evaluation of nurses at an intensive care unit. J. Med. Syst. 35(5):1039–1062, 2011.PubMedCrossRef

56.

Lewis, H. F., Sexton, T. R., and Dolan, M. A., An efficiency-based multicriteria strategic planning model for ambulatory surgery centers. J. Med. Syst. 35(5):1029–1037, 2011.PubMedCrossRef

57.

Rouse, P., Harrison, J., and Turner, N., Cost and performance: Complements for improvement. J. Med. Syst. 35(5):1063–1074, 2011.PubMedCrossRef

58.

Charnes, A., Cooper, W. W., and Rhodes, E., Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2(6):429–444, 1978.CrossRef

59.

Banker, R. D., Charnes, A., and Cooper, W. W., Some models for estimating technical and scale efficiencies in data envelopment analysis. Manag. Sci. 30(9):1078–1092, 1984.CrossRef

60.

Farrell, M. J., The measurement of productive efficiency. J. R. Stat. Soc. Ser. A (Gen.) 120(3):253–290, 1957.CrossRef

61.

Lee, H., and Kim, C., Benchmarking of service quality with data envelopment analysis. Exp. Syst. Appl. 41(8):3761–3768, 2014.CrossRef

62.

Charnes, A., and Cooper, W. W., Programming with linear fractional functionals. Nav. Res. Logist. Q. 9(3–4):181–185, 1962.CrossRef

63.

Emrouznejad, A., and Cabanda, E., Managing service productivity: uses of frontier efficiency methodologies and MCDM for improving service performance. In: the series of “International Series in Operations Research & Management Science”, Springer-Verlag, ISBN 978-3-662-43436-9, 2014.

64.

Cooper, W. W., Seiford, L. M., and Zhu, J., Data envelopment analysis: models and interpretations, chapter 1:1–39. In: Cooper, W. W., Seiford, L. M., and Zhu, J. (Eds.), Handbook on data envelopment analysis. Kluwer Academic Publisher, Boston, pp. 3–4, 2004.

65.

Cooper, W. W., Seiford, L. M., and Tone, K., Data envelopment analysis: a comprehensive text with models, applications, references and DEA-solver software, 2nd edition. Springer, New York, 2007.

66.

Ridge, J. C., Jones, S. K., Nielsen, M. S., and Shahani, A. K., Capacity planning for intensive care units. Eur. J. Oper. Res. 105(2):346–355, 1998.CrossRef

67.

Thanassoulis, E., Introduction to the theory and application of data envelopment analysis: a foundation text with integrated software. Kluwer Academic Publishers, USA, 2001.CrossRef

68.

Harrison, J. P., Coppola, M. N., and Wakefield, M., Efficiency of federal hospitals in the United States. J. Med. Syst. 28(5):411–422, 2004.PubMedCrossRef

69.

Bwana, K. M., Measuring technical efficiency of faith based hospitals in Tanzania: An application of data envelopment analysis (DEA). Res. Appl. Econ. 7(1):1–12, 2015.CrossRef

70.

Masiye, F., Kirigia, J. M., Emrouznejad, A., Sambo, L. G., Mounkaila, A., Chimfwembe, D., and Okello, D., Efficient management of health centres human resources in Zambia. J. Med. Syst. 30(6):473–481, 2006.PubMedCrossRef

71.

Zuckerman, S., Hadley, J., and Iezzoni, L., Measuring hospital efficiency with frontier cost functions. J. Health Econ. 13(3):255–280, 1994.PubMedCrossRef

72.

Al-Shammari, M., A multi-criteria data envelopment analysis model for measuring the productive efficiency of hospitals. Int. J. Oper. Prod. Manag. 19(9):879–891, 1999.CrossRef

73.

Kose, T., Uckun, N., and Girginer, N., An efficiency analysis of the clinical departments of a public hospital in Eskisehir by using DEA. Glob. J. Adv. Pure Appl. Sci. 4:252–258, 2014.

74.

Magnussen, J., Efficiency measurement and the operationalization of hospital production. Health Serv. Res. 31(1):21–37, 1996.PubMedPubMedCentral

75.

Weng, S. J., Wu, T., Blackhurst, J., and Mackulak, G., An extended DEA model for hospital performance evaluation and improvement. Health Serv Outcome Res. Methodol. 9(1):39–53, 2009.CrossRef

76.

Banker, R. D., Conrad, R.F. and Strauss, R.P., A comparative application of data envelopment analysis and translog methods: An illustrative study of hospital production. 1986.

77.

Butler, T. W., and Li, L., The utility of returns to scale in DEA programming: An analysis of Michigan rural hospitals. Eur. J. Oper. Res. 161(2):469–477, 2005.CrossRef

78.

Zere, E., McIntyre, D., and Addison, T., Technical efficiency and productivity of public sector hospitals in three South African provinces. S. Afr. J. Econ. 69(2):336–358, 2001.CrossRef

79.

Dotoli, M., Epicoco, N., Falagario, M., and Sciancalepore, F., A cross-efficiency fuzzy data envelopment analysis technique for performance evaluation of decision making units under uncertainty. Comput. Ind. Eng. 79:103–114, 2015.CrossRef

80.

Grosskopf, S., and Valdmanis, V., Measuring hospital performance: A nonparametric approach. J. Health Econ. 6(1):89–107, 1987.PubMedCrossRef

81.

Huang, Y. G., and McLaughlin, C. P., Relative efficiency in rural primary health care: An application of data envelopment analysis. Health Serv. Res. 24(2):143–158, 1989.PubMedPubMedCentral

82.

Kirigia, J. M., Emrouznejad, A., Cassoma, B., Asbu, E. Z., and Barry, S., A performance assessment method for hospitals: The case of municipal hospitals in Angola. J. Med. Syst. 32(6):509–519, 2008.PubMedCrossRef

83.

Prior, D., Efficiency and total quality management in healthcare organizations: A dynamic frontier approach. Ann. Oper. Res. 145(1):281–299, 2006.CrossRef

84.

Gerdtham, U. G., Löthgren, M., Tambour, M., and Rehnberg, M., Internal markets and health care efficiency: A multiple-output stochastic frontier analysis. Health Econ. 8(2):151–164, 1999.PubMedCrossRef

85.

Parkin, D., and Hollingsworth, B., Measuring productivity efficiency of acute hospitals in Scotland, 1991-94: Validity issues in data envelopment analysis, Applied Economics, 29(11): 1425-1433, 1997.

86.

Byrnes, P., and Valdmanis, V., Analyzing technical and allocative efficiency of hospitals. In: Charnes, A., Cooper, W. W., Lewin, A. Y., and Seiford, L. M. (Eds.), Data envelopment analysis: theory, methodology and applications. Kluwer, Boston, 1993.

87.

Kang, H., Nembhard, H.B. and DeFlitch, C., identifying emergency department efficiency frontiers and the factors associated with their efficiency performance. In: Guan, Y., and Liao, H., (Eds), Proceedings of the 2014 Industrial and Systems Engineering Research Conference, 2014.

88.

Linna, M., Measuring hospital cost efficiency with panel data models. Health Econ. 7(5):415–427, 1998.PubMedCrossRef

89.

Kirigia, J. M., Emrouznejad, A., Sambo, L. G., Munguti, N., and Liambila, W., Using data envelopment analysis to measure the technical efficiency of public health centers in Kenya. J. Med. Syst. 28(2):155–166, 2004.PubMedCrossRef

90.

Valdmanis, V., Sensitivity analysis for DEA models: An empirical example using public Vs NFP hospitals. J. Public Econ. 48(2):185–205, 1992.CrossRef

91.

Blank, J. L. T., and Van Hurst, B. L., Governance and performance: The performance of dutch hospitals explained by governance characteristics. J. Med. Syst. 35(5):991–999, 2011.PubMedPubMedCentralCrossRef

92.

Sorup, C.M., Estay, D.S., Jacobsen, P. and Anderson, P.D., Balancing patient flow and returning patients: A system dynamics study on emergency department crowding factors. Healthc. Manag. Sci. 2015. Available at: http://orbit.dtu.dk/ws/files/108550877/Balancing_patient.pdf [Accessed: 8 June 2015].

93.

Harper, P. R., and Gamlin, H. M., Reduced outpatient waiting times with improved appointment scheduling: A simulation modeling approach. OR Spectr. 2(2):207–222, 2003.CrossRef

94.

O’Keefe, R. M., Investigating outpatient departments: Implementable policies and qualitative approaches. J. Oper. Res. Soc. 36(8):705–712, 1998.CrossRef

95.

Zhu, Z., Heng, B. H., and Teow, K. L., Analysis of factors causing long patient waiting time and clinic overtime in outpatient clinics. J. Med. Syst. 36(2):707–713, 2012.PubMedCrossRef

96.

Aboueljinane, L., Sahin, E., and Jemai, Z., A review on simulation models applied to emergency medical service operations. Comput. Ind. Eng. 66(4):734–750, 2013.CrossRef

97.

Brahimi, M., and Worthington, D. J., Queuing models for outpatient appointment systems: A case study. J. Oper. Res. Soc. 42(9):733–746, 1991.

98.

Hill-Smith, I., Mathematical relationship between waiting times and appointment interval for doctors and patients. J. R. Coll. Gen. Pract. 39(329):492–494, 1989.PubMedPubMedCentral

99.

Khori, V., Changizi, S., Biuckians, E., Keshtkar, A., Alizadeh, A. M., Mohaghgheghi, A. M., and Rabie, M. R., Relationship between consultation length and rational prescribing of drugs in Gorgan City, Islamic Republic of Iran. East Mediterr. Health J. 18(5):480–486, 2012.PubMed

100.

Mankowska, D. S., Meisel, F., and Bierwirth, C., The home healthcare routing and scheduling problem with interdependent services. Healthc. Manag. Sci. 17:15–30, 2014.CrossRef

101.

Welch, J. D., Appointment systems in hospitals and general practice: Appointment systems in hospital outpatient departments. J. Oper. Res. Soc. 15(3):224–232, 1964.CrossRef

102.

Griffiths, J. D., Price-Lloyd, N., Smithies, M., and Williams, J. E., Modeling the requirement for supplementary nurses in an intensive care unit. J. Oper. Res. Soc. 56(2):126–133, 2005.CrossRef

103.

Feldman, J., Liu, N., Topaloglu, H., and Ziya, S., Appointment scheduling under patient preference and No-show behavior. Oper. Res. 62(4):794–811, 2014.CrossRef

104.

Hassin, R., and Mendel, S., Scheduling arrivals to queues: A single-server model with No- shows. Manag. Sci. 54(3):565–572, 2008.CrossRef

105.

Klassen, K. J., and Yoogalingham, R., Improving performance in outpatient appointment services with a simulation optimization approach. Prod. Oper. Manag. 18(4):447–458, 2009.CrossRef

106.

Huarng, F., and Lee, M. H., Using simulation in outpatient queues: A case study. Int. J. Healthc. Qual. Assur. 9(6):21–25, 1996.CrossRef

107.

Cote, M. J., Patient flow and resource utilization in an outpatient clinic. Socio Econ. Plan. Sci. 33(3):231–245, 1999.CrossRef

108.

Silvester, K., Lendon, R., Bevan, H., Steyn, R. and Walley, P., Reducing waiting times in the NHS: Is lack of capacity the problem? Clin. Manag. 12: Academic Paper, 1–7, 2004.

109.

Klassen, K. J., and Rohleder, T. R., Scheduling outpatient appointments in a dynamic environment. J. Oper. Manag. 14(2):83–101, 1996.CrossRef

110.

Cayirli, T., Veral, E., and Rosen, H., Assessment of patient classification in appointment system design. Prod. Oper. Manag. 17(3):338–353, 2008.

111.

Halme, M., Joro, T., Korhonen, P., Salo, S., and Wallenius, J., A value efficiency approach to incorporating preference information in data envelopment analysis. Manag. Sci. 45(1):103–115, 1999.CrossRef

112.

Golany, B., An interactive MOLP procedure for the extension of DEA to effectiveness analysis. J. Oper. Res. Soc. 39(8):725–734, 1988.CrossRef

113.

Thanassoulis, E., and Dyson, R. G., Estimating preferred target input–output levels using data envelopment analysis. Eur. J. Oper. Res. 56(1):80–97, 1992.CrossRef

114.

Zhu, J., Data envelopment analysis with preference structure. J. Oper. Res. Soc. 47(1):136–150, 1996.CrossRef

115.

Athanassopoulos, A. D., Lambroukos, N., and Seiford, L., Data envelopment scenario analysis for setting targets to electricity generating plants. Eur. J. Oper. Res. 115(3):413–428, 1999.CrossRef

116.

Thanassoulis, E., and Dunstan, P., Guiding schools to improved performance using data envelopment analysis: An illustration with data from a local education authority. J. Oper. Res. Soc. 45(11):1247–1262, 1994.CrossRef

117.

Liu, J., Ding, F. F., and Lall, V., Using data envelopment analysis to compare suppliers for supplier selection and performance improvement. Supply Chain Manag. Int. J. 5(3):143–150, 2000.CrossRef

118.

Martic, M., and Savic, G., An application of DEA for Comparative analysis and ranking of regions in Serbia with regards to social-economic development. Eur. J. Oper. Res. 132(2):343–356, 2001.CrossRef

119.

Thanassoulis, E., Boussofiane, A., and Dyson, R. G., Exploring output quality targets in the provision of perinatal care in England using data envelopment analysis. Eur. J. Oper. Res. 80(3):588–607, 1995.CrossRef

Title: Assessing the Queuing Process Using Data Envelopment Analysis: an Application in Health Centres
Authors: Komal A. Safdar
Ali Emrouznejad
Prasanta K. Dey
Publication date: 01-01-2016
Publisher: Springer US
Published in: Journal of Medical Systems / Issue 1/2016
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI: https://doi.org/10.1007/s10916-015-0393-1

At a glance: The STEP trials

Springer Medicine

Assessing the Queuing Process Using Data Envelopment Analysis: an Application in Health Centres

Abstract

At a glance: The STEP trials

Springer Medicine

Abstract

Please log in to get access to this content

Other articles of this Issue 1/2016

Analysis of Muscle Fatigue Progression using Cyclostationary Property of Surface Electromyography Signals

A Modular Framework for EEG Web Based Binary Brain Computer Interfaces to Recover Communication Abilities in Impaired People

Development of a Multi-Agent m-Health Application Based on Various Protocols for Chronic Disease Self-Management

Utilization of a Pharmacy Clinical Surveillance System for Pharmacist Alerting and Communication at a Tertiary Academic Medical Center

Diagnosis of Brain Metastases from Lung Cancer Using a Modified Electromagnetism like Mechanism Algorithm

A Secure ECC-based RFID Mutual Authentication Protocol to Enhance Patient Medication Safety