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Improving the performance of surgery-based clinical pathways: a simulation-optimization approach

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Abstract

This paper aims to improve the performance of clinical processes using clinical pathways (CPs). The specific goal of this research is to develop a decision support tool, based on a simulation-optimization approach, which identify the proper adjustment and alignment of resources to achieve better performance for both the patients and the health-care facility. When multiple perspectives are present in a decision problem, critical issues arise and often require the balancing of goals. In our approach, meeting patients’ clinical needs in a timely manner, and to avoid worsening of clinical conditions, we assess the level of appropriate resources. The simulation-optimization model seeks and evaluates alternative resource configurations aimed at balancing the two main objectives—meeting patient needs and optimal utilization of beds and operating rooms.

Using primary data collected at a Department of Surgery of a public hospital located in Genoa, Italy. The simulation-optimization modelling approach in this study has been applied to evaluate the thyroid surgical treatment together with the other surgery-based CPs. The low rate of bed utilization and the long elective waiting lists of the specialty under study indicates that the wards were oversized while the operating room capacity was the bottleneck of the system. The model enables hospital managers determine which objective has to be given priority, as well as the corresponding opportunity costs.

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References

  1. Porter ME (2010) What is value in health care? N Engl J Med 363:2477–2481

    Article  Google Scholar 

  2. De Blaser L, Depreitere R, De Waele K, Vanhaecht K, Vlayen J, Sermeus W (2006) Defining pathways. J Nurs Manag 14:553–563

    Article  Google Scholar 

  3. Panella M, Marchisio S, Stanislao F (2003) Reducing clinical variations with clinical pathways: do pathways work? Int J Qual Health Care 15:509–521

    Article  Google Scholar 

  4. Aringhieri R, Tànfani E, Testi A (2013) Operations research for health care delivery. Comput Oper Res 40(9):2165–2166

    Article  Google Scholar 

  5. Ozcan YA (2009) Quantitative methods in health care management: Techniques and applications, 2nd edn. Jossey-Bass/Wiley, San Francisco

    Google Scholar 

  6. Vissers J, Beech R (2005) Health operations management patient flow logistics in health care. Routledge, UK

    Google Scholar 

  7. H.H.R C, Bradshaw N, Porteous M (1998) Integrated care pathways. Br Med J 316:133–144

    Article  Google Scholar 

  8. Rotter T, Kinsman L, James E, Machotta A, Gothe H, Willis J, Snow P, Kugler J (2010) Clinical pathways: Effects on professional practice, patient outcomes, length of stay and hospital costs (review). In: The Cochrane library 2010, issue 7. Sons, John Wiley &

    Google Scholar 

  9. Zander K (2002) Integrated care pathways: eleven international trends. Journal of Integrated Care Pathways 6:101–107

    Google Scholar 

  10. Cardoen B, Demeulemeester E (2008) Capacity of clinical pathways - a strategic multi-level evaluation tool. J Med Syst 32(6):443–452

    Article  Google Scholar 

  11. Tanfani E, Testi A (2012) A decision support tool to analyze clinical pathways in hospital. In: Tànfani E, Testi A (eds) Advanced decision making methods applied to health services, series: International series on operations research and management science, 173. Springer-Verlang, Milano, pp. 191–211

    Chapter  Google Scholar 

  12. Swisher J, Jacobson S, Jun J, Balci O (2001) Modeling and analyzing a physician clinic environment using discrete event-event (visual) simulation. Comput Oper Res 28:105–125

    Article  Google Scholar 

  13. Vanberkel PT, Boucherie RJ, Hans EW, Hurink JL, Litvak N (2012) Efficiency evaluation for pooling resources in health care. OR Spectr 34(2):371–390

    Article  Google Scholar 

  14. Cote MJ, Stein WE (2007) A stochastic model for visit to the doctor's office. Math Comput Model 45:309–323

    Article  Google Scholar 

  15. Maliapen M, Dangerfield BC (2010) A system dynamics-based simulation study for managing clinical governance and pathways in a hospital. J Oper Res Soc 61(2):255–264

    Article  Google Scholar 

  16. Xu D. (2010). Simulating on CIS based on petri-net. In Proceedings of the 2009 I.E. International Symposium on IT in Medicine and Education, pp. 1132–1135.

  17. Michalowski W, Wilk S, Thijssen A, Li M (2006) Using a bayesian belief network model to categorize length of stay for radical prostatectomy patients. Health Care Manag Sci 9(2):341–348

    Article  Google Scholar 

  18. Li W, Liu K, Li S, Yang H (2010) A semiotic multi-agent modeling approach for clinical pathway management. J Comput 5(2):266–273

    Google Scholar 

  19. Zhang Y, Liu K, Cui G (2008) Consensus forming in clinical pathway development: Norm based modeling and simulation. In: In: Proceedings of the 2008 international conference on computational intelligence for modelling control and automation, CIMCA, pp. 931–936

    Chapter  Google Scholar 

  20. Wolf A (2008) Constraint-based modeling and scheduling of clinical pathways”, recent advances in constraints. Lect Notes Comput Sci 63:122–138

    Google Scholar 

  21. Vissers J, Adan I, Bekkers J (2005) Patient mix optimization in tactical cardiothoracic surgery planning: a case study. IMA J Manag Math 16(3):281–304

    Article  Google Scholar 

  22. Gartner D, Kolisch R (2014) Scheduling the hospital-wide flow of elective patients. Eur J Oper Res 233(3):689–699

    Article  Google Scholar 

  23. Fu MC (2002) Optimization for simulation: theory vs. practice. INFORMS J Comput 14(3):192–215

    Article  Google Scholar 

  24. Pasupathy R, Henderson SG (2005) In: Perrone LF, Wieland FP, Liu J, Lawson BG, Nicol DM, Fujimoto RM (eds) A testbed of simulation-optimization problems, vol 2005, pp. 255–263

    Google Scholar 

  25. Fu M.C., Glover F.W., April J. (2005). Simulation optimization: A review, new developments, and applications. In: Kuhl M.E., Steiger N.M., Armstrong F.B.,. Joines J.A (eds.), Proceedings of the 2005 winter simulation conference, Piscataway, New Jersey, pp. 83–95.

    Chapter  Google Scholar 

  26. De Angelis V, Felici G, Impelluso P (2003) Integrating simulation and optimization in health care center management. Eur J Oper Res 150:101–114

    Article  Google Scholar 

  27. Zhang Y, Puterman ML (2013) Developing an adaptive policy for long-term care capacity planning. Health Care Manag Sci 16(3):271–279

    Article  Google Scholar 

  28. Baesler F.F., Sepúlveda J.A. (2001). Multi-objective simulation optimization for a cancer treatment center. In: Proceedings of the 2001 Winter Simulation Conference, pp. 1405–1411.

  29. Landa P, Tanfani E, Testi A (2013) Simulation and optimization for bed Re-organization at a surgery department. In: In: Proceedings of the 3rd international conference on simulation and modeling methodologies, technologies and applications, pp. 584–594

    Google Scholar 

  30. Lee EK, Atallah HY, Wright MD, Post ET, Thomas C IV, Wu DT, Haley LL Jr (2015) Transforming hospital emergency department workflow and patient care. Interfaces 45(1):58–82

  31. Feili HR (2013) Improving the health care systems performance by simulation optimization. J Math Comput Sci 7:73–79

  32. Ahmed MA, Alkhamis TM (2009) Simulation optimization for an emergency department healthcare unit in Kuwait. Eur J Oper Res 198(3):936–942

    Article  Google Scholar 

  33. Ozcan YA, Tànfani E, Testi A (2011) A simulation-based modeling framework to deal with clinical pathways. In: Jain S, Creasey RR, Himmelspach J, White KP, Fu M (eds) Proceedings of the 2011 winter simulation conference. Piscataway, New Jersey, pp. 1190–1201

    Chapter  Google Scholar 

  34. Ozcan YA, Tànfani E, Testi A (2013) Project Management approach to implement clinical pathways: An example for thyroid treatment. In: Zaric G (ed) Operations research and health care policy, series on operations research and management science, 190. Springer, Newton, pp. 91–104

    Chapter  Google Scholar 

  35. Mullen PM (2003) Prioritising waiting lists: how and why? Eur J Oper Res 150(1):32–45

    Article  Google Scholar 

  36. Testi A, Tanfani E, Valente R, Ansaldo G, Torre GC (2008) Prioritising surgical waiting list. J Eval Clin Pract 14(1):59–64

    Article  Google Scholar 

  37. Valente R, Testi A, Tanfani E, Fato M, Porro I, Santori G, Santo M, Ansaldo GL, Torre GC (2009) A model to prioritize access to elective surgery on the base of clinical urgency and waiting time. BMC Health Serv Res 9(1). doi:10.1186/1472-6963-9-1

  38. Witness 2014. Manual guide, Lanner Group.

  39. Sargent RG (2013) Verification and validation of simulation models. J Simul 7:12–24

    Article  Google Scholar 

  40. Kleijnen JPC (1995) Verification and validation of simulation models. Eur J Oper Res 82:145–162

    Article  Google Scholar 

  41. Law M.A. (2007). Simulation Modeling & Analysis, 4th ed., McGraw-Hill.

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Acknowledgments

The authors wish to thank the director and clinical personnel of the General Surgery Department of San Martino Hospital for providing data and helping with model implementation and face validation. All the authors participated in this study and acknowledge the support from the Italian Ministry of Education, University and Research (MIUR) under grant no. RBFR08IKSB - FIRB PROJECT.

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Authors and Affiliations

  1. Department of Health Administration, Virginia Commonwealth University, PO Boxs 980203, 1008 East Clay Street, Richmond, VA, 23298-0203, USA

    Yasar A. Ozcan

  2. Department of Economics, University of Genova, Via Vivaldi 5, 16126, Genoa, Italy

    Elena Tànfani & Angela Testi

Authors
  1. Yasar A. Ozcan
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  2. Elena Tànfani
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  3. Angela Testi
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Correspondence to Yasar A. Ozcan.

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Ozcan, Y.A., Tànfani, E. & Testi, A. Improving the performance of surgery-based clinical pathways: a simulation-optimization approach. Health Care Manag Sci 20, 1–15 (2017). https://doi.org/10.1007/s10729-016-9371-5

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  • DOI: https://doi.org/10.1007/s10729-016-9371-5

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