Skip to main content
Log in

A participative and facilitative conceptual modelling framework for discrete event simulation studies in healthcare

  • General Paper
  • Published:
Journal of the Operational Research Society

Abstract

Existing approaches to conceptual modelling (CM) in discrete-event simulation do not formally support the participation of a group of stakeholders. Simulation in healthcare can benefit from stakeholder participation as it makes possible to share multiple views and tacit knowledge from different parts of the system. We put forward a framework tailored to healthcare that supports the interaction of simulation modellers with a group of stakeholders to arrive at a common conceptual model. The framework incorporates two facilitated workshops. It consists of a package including: three key stages and sub-stages; activities and guidance; tools and prescribed outputs. The CM framework is tested in a real case study of an obesity system. The benefits of using this framework in healthcare studies and more widely in simulation are discussed. The paper also considers how the framework meets the CM requirements.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5

Similar content being viewed by others

References

  • Ackoff R (1979). Resurrecting the future of operational research. The Journal of the Operational Research Society 30 (3): 189–199.

    Article  Google Scholar 

  • Andersen DF, Richardson GP and Vennix JAM (1997). Group model building: Adding more science to the craft. System Dynamics Review 13 (2): 187–201.

    Article  Google Scholar 

  • Balci O (2010). Golden rules of verification, validation, testing, and certification of modeling and simulation applications. SCS M&S Magazine, Issue 4. The Society for Modeling and Simulation International (SCS): Vista, CA.

  • Balci O (2011). How to successfully conduct large-scale modeling and simulation projects. In: Jain S, Creasey RR, Himmelspach J, White KP and Fu M (eds) Proceedings of the 2011 Winter Simulation Conference. December, IEEE: Phoenix, AZ.

    Google Scholar 

  • Balci O and Ormsby WF (2007). Conceptual modelling for designing large-scale simulations. Journal of Simulation 1 (3): 175–186.

    Article  Google Scholar 

  • Balci O, Arthur JD and Nance RE (2008). Accomplishing reuse with a simulation conceptual model. In: Mason SJ, Hill RR, Mönch L, Rose O, Jefferson T, and Fowler JW (eds). Proceedings of the 2008 Winter Simulation Conference. IEEE: Miami, FL.

    Google Scholar 

  • Banks J, Carson J, Nelson BL and Nicol D (2005). Discrete-Event System Simulation. 4th edn, Prentice-Hall: Englewood Cliffs, NJ.

    Google Scholar 

  • Blackett PMS (1950). Operational research. Operational Research Quarterly (1950–1952) 1 (1): 3–6.

    Article  Google Scholar 

  • Brade D (2004). A generalized process for the verification and validation of models and simulation results. PhD Thesis at the University of the Federal Armed Forces of Germany, Munich.

  • Brailsford SC, Bolt T, Connell C, Klein JH and Patel B (2009). Stakeholder engagement in health care simulation. In: Rossetti MD, Hill RR, Johansson B, Dunkin A and Ingalls RG (eds.) Winter Simulation Conference, IEEE: Austin, TX, pp 1840–1849.

  • Brooks RJ and Tobias AM (1996). Choosing the best model: Level of detail, complexity and model performance. Mathematical and Computer Modelling 24 (4): 1–14.

    Article  Google Scholar 

  • Butland B et al (2007). Foresight. Tackling obesities: Future choices. Project Report. 2nd edn, accessed 15 April 2010. Government Office for Science. UK.

  • Büyükdamgaci G (2003). Process of organizational problem definition: How to evaluate and how to improve. Omega 31 (4): 327–338.

    Article  Google Scholar 

  • Checkland P (1999). Systems Thinking Systems Practice. Wiley: Chichester, UK.

    Google Scholar 

  • Checkland P and Scholes J (1999). Soft Systems Methodology in Action. Wiley: Chichester, UK.

    Google Scholar 

  • Department Of Health (2004). The NHS Improvement Plan: Putting People at the Heart of Public Services. TSO: London, Cm 6268 Series.

  • Eldabi T, Paul RJ and Young T (2007). Simulation modelling in healthcare: Reviewing legacies and investigating futures. Journal of the Operational Research Society 58 (2): 262–270.

    Article  Google Scholar 

  • Fone D et al (2003). Systematic review of the use and value of computer simulation modelling in population health and health care delivery. Journal of Public Health 25 (4): 325–335.

    Article  Google Scholar 

  • Franco LA and Montibeller G (2010). Facilitated modelling in operational research. European Journal of Operational Research 205 (3): 489–500.

    Article  Google Scholar 

  • Grinyer PH (2000). A cognitive approach to group strategic decision taking: A discussion of evolved practice in the light of received research results. Journal of the Operational Research Society 51 (1): 21–35.

    Article  Google Scholar 

  • Gunal MM and Pidd M (2005). Simulation modelling for performance measurement in healthcare. In: Kuhl ME, Steiger NM, Armstrong FB and Joines JA (eds). Proceedings of the 2005 Winter Simulation Conference. IEEE: Orlando, FL, 4–7 December 2005. ACM, pp 2663–2667.

    Google Scholar 

  • Henriksen JO (1988). One system, several perspectives, many models. In: Abrams M, Haigh P, Comfort J (eds) Proceedings of the 1988 Winter Simulation Conference. IEEE: Piscataway, NJ.

    Google Scholar 

  • Jun JB, Jacobson SH and Swisher JR (1999). Application of discrete-event simulation in health care clinics: A survey. Journal of the Operational Research Society 50 (2): 109–123.

    Article  Google Scholar 

  • Kotiadis K (2007). Using soft systems methodology to determine the simulation study objectives. Journal of Simulation 1 (1): 215–222.

    Article  Google Scholar 

  • Kotiadis K and Robinson S (2008). Conceptual modelling: Knowledge acquisition and model abstraction. In: Mason SJ, Hill RR, Mönch L, Rose O, Jefferson T and Fowler JW (eds). Proceedings of the 2008 Winter Simulation Conference. IEEE: Miami, FL.

    Google Scholar 

  • Law AM (2007). Simulation Modeling and Analysis. 4th edn, McGraw-Hill: Boston, MA.

    Google Scholar 

  • Lehaney B and Hlupic V (1995). Simulation modelling for resource allocation and planning in the health sector. Perspectives in Public Health 115 (6): 382–385.

    Google Scholar 

  • Lehaney B and Paul RJ (1994). Using soft systems methodology to develop a simulation of outpatient services. Journal of the Royal Society for the Promotion of Health 114 (5): 248–251.

    Article  Google Scholar 

  • Lehaney B and Paul RJ (1996). The use of soft systems methodology in the development of a simulation of out-patients services at Watford General Hospital. Journal of the Operational Research Society 47 (7): 864–870.

    Article  Google Scholar 

  • Lehaney B, Clarke SA and Paul RJ (1999). A case of an intervention in an outpatients department. Journal of the Operational Research Society 50 (9): 877–891.

    Article  Google Scholar 

  • Lowery JC (1994). Barriers to implementing simulation in health care. In: Tew JD, Mannivannan S, Sadowski DA and Seila AF (eds) Proceedings of the 1994 Winter Simulation Conference. 11–14 December, ACM: Lake Buena Vista, FL, pp 868–875.

    Google Scholar 

  • Mingers J and Rosenhead J (2004). Problem structuring methods in action. European Journal of Operational Research 152 (3): 530–555.

    Article  Google Scholar 

  • Nance RE (1994). The conical methodology and the evolution of simulation model development. Annals of Operations Research 53 (1): 1–45.

    Article  Google Scholar 

  • National Institute for Health and Clinical Excellence (2006). Obesity: Guidance on the Prevention, Identification, Assessment and Management of Overweight and Obesity in Adults and Children. NICE clinical guideline 43 re-issued 29 January 2010 National Institute for Health and Clinical Excellence: London, UK.

  • NHS Information Centre (2008). Statistics on Obesity, Physical Activity and Diet: England, 2008. The Information Centre: Leeds, UK, 31 January.

  • Nutt P (1986). Tactics of implementation. The Academy of Management Journal 29 (2): 230–261.

    Article  Google Scholar 

  • Pace DK (1999). Development and documentation of a simulation conceptual model. In: Proceedings of the 1999 Fall Simulation Interoperability Workshop. (Accessed August 2005).

  • Pace DK (2000). Ideas about simulation conceptual model development. Johns Hopkins APL Technical Digest 21 (3): 327–336.

    Google Scholar 

  • Papamichail KN, Alves G, French S, Yang JB and Snowdon R (2007). Facilitation practices in decision workshops. Journal of the Operational Research Society 58 (5): 614–632.

    Article  Google Scholar 

  • Phillips LD and Phillips MC (1993). Facilitated work groups—Theory and practice. Journal of the Operational Research Society 44 (6): 533–549.

    Article  Google Scholar 

  • Pidd M (1999). Just modeling through: A rough guide to modeling. Interfaces 29 (2): 118–132.

    Article  Google Scholar 

  • Pidd M (2007). Making sure you tackle the right problem: Linking hard and soft methods in simulation practice. Proceedings of the 2007 Winter Simulation Conference, Institute of Electrical and Electronic Engineers, Inc: Washington DC.

  • Pritsker AAB (1986). Model evolution: A rotary table case history. In: Wilson J, Henriksen J and Roberts S (eds) Proceedings of the 1986 Winter Simulation Conference. IEEE: Piscataway, NJ.

    Google Scholar 

  • Robinson S (2004). Simulation: The Practice of Model Development and Use. John Wiley & Sons: Chichester, UK.

    Google Scholar 

  • Robinson S (2008a). Conceptual modelling for simulation part I: Definition and requirements. Journal of the Operational Research Society 59 (3): 278–290.

    Article  Google Scholar 

  • Robinson S (2008b). Conceptual modelling for simulation Part II: A framework for conceptual modelling. Journal of the Operational Research Society 59 (3): 291–304.

    Article  Google Scholar 

  • Robinson S (2011). A framework for simulation conceptual modelling. In: Robinson S, Brooks R, Kotiadis K and Van Der Zee DJ (eds) Conceptual Modelling for Discrete Event Simulation. CRC Press: Boca Raton, FL, pp 73–101.

    Google Scholar 

  • Rosenhead J and Mingers J (2001). Rational Analysis for a Problematic World Revisited. Wiley: Chichester, UK.

    Google Scholar 

  • Rouwette EAJA, Vennix JAM and Mullekom TV (2002). Group model building effectiveness: A review of assessment studies. System Dynamics Review 18 (1): 5–45.

    Article  Google Scholar 

  • Sargent RG (2008). Verification and validation of simulation models. In: Mason SJ, Hill RR, Mönch L, Rose O, Jefferson T and Fowler JW (eds) Proceedings of the 2008 Winter Simulation Conference. December, IEEE: Miami, FL.

    Google Scholar 

  • Shannon RE (1975). Systems Simulation: The Art and Science. Prentice-Hall: Englewood Cliffs, NJ.

    Google Scholar 

  • Tako AA, Kotiadis K, Vasilakis C, Miras A and le Roux CW (2012). Modeling patient waiting times for an obesity service: A computer simulation study. Available from Loughborough University Institutional Repository, http://hdl.handle.net/2134/9330.

  • van Der Zee DJ (2007). Developing participative simulation models: Framing decomposition principles for joint understanding. Journal of Simulation 1: 187–202.

    Article  Google Scholar 

  • van Der Zee DJ (2011). Developing participative simulation models: Framing decomposition principles for joint understanding. In: Robinson S, Brooks R, Kotiadis K, and Van Der Zee DJ (eds) Conceptual Modelling for Discrete Event Simulation. CRC Press: Boca Raton, FL, pp 103–132.

    Google Scholar 

  • van Der Zee DJ and van Der Vorst JGAJ (2005). A modelling framework for supply chain simulation: Opportunities for improved decision making. Decision Science 36: 65–95.

    Article  Google Scholar 

  • van Der Zee DJ et al (2010). Panel discussion: Education on conceptual modelling for simulation - challenging the art. In: Johansson B, Jain S, Montoya-Torres J, Hugan J and Yücesan E (eds) Proceedings of the 2010 Winter Simulation Conference. IEEE: Piscataway, NJ, pp 290–304.

    Chapter  Google Scholar 

  • van Der Zee DJ, Tolk A, Pidd M, Kotiadis K and Tako A (2011). Education on conceptual modeling for simulation—Beyond the craft: A summary of a recent expert panel discussion, 2010, panel discussion: Education on conceptual modelling for simulation—Challenging the art. SCS Magazine 2 (2): 93–102.

    Google Scholar 

  • Vennix JAM (1999). Group model-building: Tackling messy problems. System Dynamics Review 15 (4): 379.

    Article  Google Scholar 

  • Vennix JAM (1996). Group Model-building: Facilitating Team Learning Using System Dynamics. Wiley: Chichester, UK.

    Google Scholar 

  • Vennix JAM and Gubbels JW (1992). Knowledge elicitation in conceptual model building: A case study in modeling a regional Dutch health care system. European Journal of Operational Research 59 (1): 85–101.

    Article  Google Scholar 

  • Vennix JAM, Gubbels JW, Post D and Poppen HJ (1990). A structured approach to knowledge elicitation in conceptual model building. System Dynamics Review 6 (2): 194–208.

    Article  Google Scholar 

  • Willemain TR (1994). Insights on modelling from a dozen experts. Operations Research 42: 413–222.

    Article  Google Scholar 

  • Wilson JCT (1981). Implementation of computer simulation projects in health care. Journal of the Operational Research Society 32 (9): 162–164.

    Article  Google Scholar 

  • Zeigler BP (1976). Theory of Modeling and Simulation. Wiley: New York.

    Google Scholar 

Download references

Acknowledgements

This study was funded by the UK Engineering and Physical Sciences Research Council (EPRSC) grant EP/E045871/1.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to K Kotiadis.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kotiadis, K., Tako, A. & Vasilakis, C. A participative and facilitative conceptual modelling framework for discrete event simulation studies in healthcare. J Oper Res Soc 65, 197–213 (2014). https://doi.org/10.1057/jors.2012.176

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/jors.2012.176

Keywords

Navigation