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FRAMEWORK DEVELOPMENT FOR DYNAMIC SYSTEM VALIDATION

Year 2020, Volume: 6 Issue: 2, 140 - 149, 31.12.2020
https://doi.org/10.22531/muglajsci.785381

Abstract

Validation is one of the most important stages of modeling, and it is a necessity before utilizing the model for further analyses. Especially for the studies that start with conceptual models, the validation process is more complex than others. In this study, we used a multi-methodology approach to develop a framework to support the conceptual model development and validation in System Dynamics (SD) modeling. In the proposed framework, we integrated methods from SD methodology with methods from the Analytic Network Process (ANP). The proposed framework was then used for clinical laboratory performance analysis. The purpose was to use the proposed framework to conduct structural validity of the SD model, to prioritize Clinical Laboratory (CL) performance indicators, and capture their relations. Results indicate that the proposed framework can be used to generate an enriched and validated conceptual model for a CL performance system that can be useful for healthcare decision-makers. Also, the proposed multi-methodology framework can be applied to any complex systems to validate the conceptual models.

References

  • Sargent, R. G. “Verification and validation of simulation models”, Journal of Simulation, 7(1), 12-24. 2013.
  • Barlas, Y. “System dynamics: systemic feedback modeling for policy analysis”, System, 1, 59, 2007.
  • Balci, O., and Ormsby, W. F. “Conceptual modeling for designing large-scale simulations”, Journal of Simulation, 1(3), 175-186, 2007.
  • Groesser, S.N., and Schaffernicht, M. “Mental Models of Dynamic Systems:Taking Stock and Looking Ahead”, System Dynamics Review, 28(1), 46-68, 2012.
  • Maani K.E., and Cavana. R.Y., Systems thinking. System Dynamics: Managing Change and Complexity. Pearson Education: North Shore City, New Zealand, 2007.
  • Chick, S.E., “Six ways to improve a simulation analysis”, Journal of Simulation, 1(1), 21-28, 2006.
  • Kunc, M., “Using systems thinking to enhance strategy maps”, Management Decision, 46(5), 761-778, 2008.
  • Mingers, J., “Variety is the spice of life: Combining soft and hard OR/MS methods”, International Transactions in Operations Research, 7(6), 673-691, 2000.
  • Henao, F., and Franco, L. A., “Unpacking multimethodology: Impacts of a community development intervention”, European Journal of Operational Research, 253(3), 681-696, 2016.
  • Sterman, J.D., Business Dynamics: Systems Thinking and Modeling for a Complex World, Irwin McGraw-Hill, Boston, 2000.
  • Barlas, Y., “Model validation in system dynamics”, In Proceedings of the International System Dynamics Conference, 1994, pp. 1-10.
  • Kumar, S., and Nigmatullin, A., “A system dynamics analysis of food supply chains–Case study with non-perishable products”, Simulation Modeling Practice and Theory, 19(10), 2151-2168, 2011.
  • Chwif, L., Muniz, P. S., and Shimada, L. M., “A prescriptive technique for V&V of simulation models when no real-life data are available: Results rom a real-life project”, Journal of Simulation, 2(2), 81-89, 2008.
  • Karpak, B., and Topcu, I., “Small medium manufacturing enterprises in Turkey: an analytic network process framework for prioritizing factors affecting success”, Int. J. Production Economics, Vol. 125, No. 1, pp.60–70, 2010.
  • Saaty, T.L., and Peniwati, K., Group decision making: drawing out and reconciling differences, RWS Publications, 4922 Ellsworth Avenue, Pittsburgh, PA 15213, 2008.
  • Saaty, T.L., “Analytic network process”, in S.I. Gass and C.M. Harris (Eds.), Encyclopedia of Operations Research and Management (100th ed.). USA: Boston, pp.28–35, 2001.
  • Fottler, M. D., “Health care organizational performance: Present and future research”, Journal of Management, 13(2), 367-391, 1987.
  • Arah, O. A., Klazinga, N. S., Delnoij, D. M. J., Ten Asbroek, A. H. A., and Custers, T., “Conceptual frameworks for health systems performance: a quest for effectiveness, quality, and improvement”, International Journal for Quality in Health Care, 15(5), 377-398, 2003.
  • Veillard, J., Champagne, F., Klazinga, N., Kazandjian, V., Arah, O. A., and Guisset, A. L., “A performance assessment framework for hospitals: the WHO regional office for Europe PATH project”, International journal for quality in Health Care, 17(6), 487-496, 2005.
  • Organisation for Economic Co-Operation and Development (OECD): In Towards High-Performing Health Systems, Edited by Organisation for Economic Co-Operation and Development. Paris. ISBN 9789264015555, 2004.
  • Snozek, C., Kaleta, E., and Hernandez, J. S., “Management structure: Establishing a laboratory utilization program and tools for utilization management”, Clinica Chimica Acta, 427, 118-122, 2014.
  • Zinn, J., Zalokowski, A., and Hunter, L., “Identifying indicators of laboratory management performance: a multiple constituency approach”, Health Care Management Review, 26(1), 40-53, 2001.
  • Arah, O. A., Westert, G. P., Hurst, J., and Klazinga, N. S., “A conceptual framework for the OECD health care quality indicators project”, International Journal for Quality in Health Care, 18(suppl 1), 5-13, 2006.
  • Scinto, L.D., Product cost analysis in the clinical laboratory. Issues in cost accounting for health care organizations, Finkler SA (ED): Aspen Publishers INC Gaithersburg., 1994.
  • Medical laboratories – requirements for quality and competence. Geneva, Switzerland: International Organization for Standardization, ISO 15189, 2012.
  • Plebani, M., Astion, M. L., Barth, J. H., Chen, W., de Oliveira Galoro, C. A., Escuer, M. I., and Shcolnik, W., “Harmonization of quality indicators in laboratory medicine. A preliminary consensus”, Clinical Chemistry and Laboratory Medicine (CCLM), 52(7), 951-958, 2014.
  • Yenice, S., “Implementing a resource management program for accreditation process at the medical laboratory”, Clinical biochemistry, 42(4), 266-273, 2008.
  • Lundberg, G. D., “Acting on significant laboratory results”, Jama, 245(17), 1762-1763, 1981.
  • Azadmanjir, Z., Torabi, M., Safdari, R., Bayat, M., and Golmahi, F. A., “Map for Clinical Laboratories Management Indicators in the Intelligent Dashboard”, Acta Informatica Medica, 23(4), 210, 2015.
  • Hawkins, R. C., “Laboratory turnaround time”, The Clinical Biochemist Reviews, 28(4), 179, 2007.
  • http://www.superdecisions.com/super-decisions-download-page/ the SuperDecisions software.
  • Tosun, O. K., Gungor, A., and Topcu, Y. I., “ANP application for evaluating Turkish mobile communication operators”, Journal of Global Optimization, 42(2), 313-324, 2008.
  • Saaty, T. L., and William, A., Super decisions software, Pittsburgh, PA: RWS Publication, 2004.
  • Forrester, J. W., and Senge, P. M., “Tests for building confidence in system dynamics models”, System dynamics, TIMS studies in management sciences, 14, 209-228, 1980.
  • Qudrat-Ullah, H., and Seong, B.S., “How to do structural validity of a system dynamics type simulation model: the case of an energy policy model”, Energy Policy, 38(5), 2216-2224, 2010.
Year 2020, Volume: 6 Issue: 2, 140 - 149, 31.12.2020
https://doi.org/10.22531/muglajsci.785381

Abstract

Geçerlilik, modellemenin en önemli aşamalarından biridir ve her model için gereklidir. Bu süreç, özellikle kavramsal modeller ile başlanılan çalışmalarda diğerlerine nazaran daha karmaşıktır. Kavramsal model geliştirmenin sistem dinamiği (SD) modellerinin doğrulanması ve geçerlilik analizlerinin sonuçları üzerinde büyük etkisi olduğu öne sürülmektedir. Bu çalışmada, SD modellemesinde kavramsal model geliştirme ve geçerlilik sürecini destekleyen bir çerçeve geliştirmek için çoklu metodoloji yaklaşımı kullanılmıştır. Önerilen çerçevede, SD metodolojisindeki yöntemler Analitik Ağ Süreci (ANP) yöntemleriyle entegre edilmiştir. Önerilen çerçeve daha sonra klinik laboratuvarda performans analizi için kurulacak bir SD modelinin onaylanması için kullanılmıştır. Amacımız, SD modelinin yapısal geçerliliğini gerçekleştirmek, klinik laboratuvar performans göstergelerinin hangilerine önceliklendirmek ve bunların ilişkilerini yakalamak için önerilen çerçeveyi kullanmaktır. Sonuçlar, önerilen çerçevenin, sağlık hizmeti karar vericileri için yararlı olabilecek bir klinik laboratuvar performans sistemi için zenginleştirilmiş ve doğrulanmış bir kavramsal model oluşturmak için kullanılabileceğini göstermektedir. Ayrıca, önerilen çoklu metodoloji çerçevesi, kavramsal modellerin doğrulama ve onaylanması için başka sistemlere de uygulanabilir.

References

  • Sargent, R. G. “Verification and validation of simulation models”, Journal of Simulation, 7(1), 12-24. 2013.
  • Barlas, Y. “System dynamics: systemic feedback modeling for policy analysis”, System, 1, 59, 2007.
  • Balci, O., and Ormsby, W. F. “Conceptual modeling for designing large-scale simulations”, Journal of Simulation, 1(3), 175-186, 2007.
  • Groesser, S.N., and Schaffernicht, M. “Mental Models of Dynamic Systems:Taking Stock and Looking Ahead”, System Dynamics Review, 28(1), 46-68, 2012.
  • Maani K.E., and Cavana. R.Y., Systems thinking. System Dynamics: Managing Change and Complexity. Pearson Education: North Shore City, New Zealand, 2007.
  • Chick, S.E., “Six ways to improve a simulation analysis”, Journal of Simulation, 1(1), 21-28, 2006.
  • Kunc, M., “Using systems thinking to enhance strategy maps”, Management Decision, 46(5), 761-778, 2008.
  • Mingers, J., “Variety is the spice of life: Combining soft and hard OR/MS methods”, International Transactions in Operations Research, 7(6), 673-691, 2000.
  • Henao, F., and Franco, L. A., “Unpacking multimethodology: Impacts of a community development intervention”, European Journal of Operational Research, 253(3), 681-696, 2016.
  • Sterman, J.D., Business Dynamics: Systems Thinking and Modeling for a Complex World, Irwin McGraw-Hill, Boston, 2000.
  • Barlas, Y., “Model validation in system dynamics”, In Proceedings of the International System Dynamics Conference, 1994, pp. 1-10.
  • Kumar, S., and Nigmatullin, A., “A system dynamics analysis of food supply chains–Case study with non-perishable products”, Simulation Modeling Practice and Theory, 19(10), 2151-2168, 2011.
  • Chwif, L., Muniz, P. S., and Shimada, L. M., “A prescriptive technique for V&V of simulation models when no real-life data are available: Results rom a real-life project”, Journal of Simulation, 2(2), 81-89, 2008.
  • Karpak, B., and Topcu, I., “Small medium manufacturing enterprises in Turkey: an analytic network process framework for prioritizing factors affecting success”, Int. J. Production Economics, Vol. 125, No. 1, pp.60–70, 2010.
  • Saaty, T.L., and Peniwati, K., Group decision making: drawing out and reconciling differences, RWS Publications, 4922 Ellsworth Avenue, Pittsburgh, PA 15213, 2008.
  • Saaty, T.L., “Analytic network process”, in S.I. Gass and C.M. Harris (Eds.), Encyclopedia of Operations Research and Management (100th ed.). USA: Boston, pp.28–35, 2001.
  • Fottler, M. D., “Health care organizational performance: Present and future research”, Journal of Management, 13(2), 367-391, 1987.
  • Arah, O. A., Klazinga, N. S., Delnoij, D. M. J., Ten Asbroek, A. H. A., and Custers, T., “Conceptual frameworks for health systems performance: a quest for effectiveness, quality, and improvement”, International Journal for Quality in Health Care, 15(5), 377-398, 2003.
  • Veillard, J., Champagne, F., Klazinga, N., Kazandjian, V., Arah, O. A., and Guisset, A. L., “A performance assessment framework for hospitals: the WHO regional office for Europe PATH project”, International journal for quality in Health Care, 17(6), 487-496, 2005.
  • Organisation for Economic Co-Operation and Development (OECD): In Towards High-Performing Health Systems, Edited by Organisation for Economic Co-Operation and Development. Paris. ISBN 9789264015555, 2004.
  • Snozek, C., Kaleta, E., and Hernandez, J. S., “Management structure: Establishing a laboratory utilization program and tools for utilization management”, Clinica Chimica Acta, 427, 118-122, 2014.
  • Zinn, J., Zalokowski, A., and Hunter, L., “Identifying indicators of laboratory management performance: a multiple constituency approach”, Health Care Management Review, 26(1), 40-53, 2001.
  • Arah, O. A., Westert, G. P., Hurst, J., and Klazinga, N. S., “A conceptual framework for the OECD health care quality indicators project”, International Journal for Quality in Health Care, 18(suppl 1), 5-13, 2006.
  • Scinto, L.D., Product cost analysis in the clinical laboratory. Issues in cost accounting for health care organizations, Finkler SA (ED): Aspen Publishers INC Gaithersburg., 1994.
  • Medical laboratories – requirements for quality and competence. Geneva, Switzerland: International Organization for Standardization, ISO 15189, 2012.
  • Plebani, M., Astion, M. L., Barth, J. H., Chen, W., de Oliveira Galoro, C. A., Escuer, M. I., and Shcolnik, W., “Harmonization of quality indicators in laboratory medicine. A preliminary consensus”, Clinical Chemistry and Laboratory Medicine (CCLM), 52(7), 951-958, 2014.
  • Yenice, S., “Implementing a resource management program for accreditation process at the medical laboratory”, Clinical biochemistry, 42(4), 266-273, 2008.
  • Lundberg, G. D., “Acting on significant laboratory results”, Jama, 245(17), 1762-1763, 1981.
  • Azadmanjir, Z., Torabi, M., Safdari, R., Bayat, M., and Golmahi, F. A., “Map for Clinical Laboratories Management Indicators in the Intelligent Dashboard”, Acta Informatica Medica, 23(4), 210, 2015.
  • Hawkins, R. C., “Laboratory turnaround time”, The Clinical Biochemist Reviews, 28(4), 179, 2007.
  • http://www.superdecisions.com/super-decisions-download-page/ the SuperDecisions software.
  • Tosun, O. K., Gungor, A., and Topcu, Y. I., “ANP application for evaluating Turkish mobile communication operators”, Journal of Global Optimization, 42(2), 313-324, 2008.
  • Saaty, T. L., and William, A., Super decisions software, Pittsburgh, PA: RWS Publication, 2004.
  • Forrester, J. W., and Senge, P. M., “Tests for building confidence in system dynamics models”, System dynamics, TIMS studies in management sciences, 14, 209-228, 1980.
  • Qudrat-Ullah, H., and Seong, B.S., “How to do structural validity of a system dynamics type simulation model: the case of an energy policy model”, Energy Policy, 38(5), 2216-2224, 2010.
There are 35 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Journals
Authors

Eylül Damla Gönül Sezer 0000-0002-9237-0468

Zeynep Ocak 0000-0002-2830-7851

Publication Date December 31, 2020
Published in Issue Year 2020 Volume: 6 Issue: 2

Cite

APA Gönül Sezer, E. D., & Ocak, Z. (2020). FRAMEWORK DEVELOPMENT FOR DYNAMIC SYSTEM VALIDATION. Mugla Journal of Science and Technology, 6(2), 140-149. https://doi.org/10.22531/muglajsci.785381
AMA Gönül Sezer ED, Ocak Z. FRAMEWORK DEVELOPMENT FOR DYNAMIC SYSTEM VALIDATION. MJST. December 2020;6(2):140-149. doi:10.22531/muglajsci.785381
Chicago Gönül Sezer, Eylül Damla, and Zeynep Ocak. “FRAMEWORK DEVELOPMENT FOR DYNAMIC SYSTEM VALIDATION”. Mugla Journal of Science and Technology 6, no. 2 (December 2020): 140-49. https://doi.org/10.22531/muglajsci.785381.
EndNote Gönül Sezer ED, Ocak Z (December 1, 2020) FRAMEWORK DEVELOPMENT FOR DYNAMIC SYSTEM VALIDATION. Mugla Journal of Science and Technology 6 2 140–149.
IEEE E. D. Gönül Sezer and Z. Ocak, “FRAMEWORK DEVELOPMENT FOR DYNAMIC SYSTEM VALIDATION”, MJST, vol. 6, no. 2, pp. 140–149, 2020, doi: 10.22531/muglajsci.785381.
ISNAD Gönül Sezer, Eylül Damla - Ocak, Zeynep. “FRAMEWORK DEVELOPMENT FOR DYNAMIC SYSTEM VALIDATION”. Mugla Journal of Science and Technology 6/2 (December 2020), 140-149. https://doi.org/10.22531/muglajsci.785381.
JAMA Gönül Sezer ED, Ocak Z. FRAMEWORK DEVELOPMENT FOR DYNAMIC SYSTEM VALIDATION. MJST. 2020;6:140–149.
MLA Gönül Sezer, Eylül Damla and Zeynep Ocak. “FRAMEWORK DEVELOPMENT FOR DYNAMIC SYSTEM VALIDATION”. Mugla Journal of Science and Technology, vol. 6, no. 2, 2020, pp. 140-9, doi:10.22531/muglajsci.785381.
Vancouver Gönül Sezer ED, Ocak Z. FRAMEWORK DEVELOPMENT FOR DYNAMIC SYSTEM VALIDATION. MJST. 2020;6(2):140-9.

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