FRAMEWORK DEVELOPMENT FOR DYNAMIC SYSTEM VALIDATION

Yıl 2020, Cilt: 6 Sayı: 2, 140 - 149, 31.12.2020

Eylül Damla Gönül Sezer Zeynep Ocak

https://doi.org/10.22531/muglajsci.785381

Öz

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.

Anahtar Kelimeler

system dynamics, conceptual model development, analytic network process, validation

Kaynakça

• 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.