Exploratory analysis using discrete event simulation modelling of the wait times and service costs associated with the maximum wait time guarantee policy applied in a rheumatology central intake clinic

References

• Arora, S., Rafiq, A., & Jolly, M. (2016). Management of rheumatoid arthritis: Review of current guidelines. Journal of Arthroscopy and Joint Surgery, 3(2), 45–50. https://doi.org/10.1016/j.jajs.2016.07.002
   Google Scholar
• Barber, C. E. H., Lacaille, D., Faris, P., Mosher, D., Katz, S., Patel, J. N., Zhang, S., Yee, K., Barnabe, C., Hazlewood, G. S., Bykerk, V., Shiff, N. J., Twilt, M., Burt, J., Benseler, S. M., Homik, J., & Marshall, D. A. (2020). Evaluating quality of care for rheumatoid arthritis for the population of Alberta using system-level performance measures. The Journal of Rheumatology, 48(4), 482–485. https://doi.org/10.3899/jrheum.200420
   PubMed Web of Science ®Google Scholar
• Barber, C. E. , Marshall, D. A. , Mosher, D. P. , Akhavan, P. , Tucker, L. , Houghton, K., & Lacaille, D. (2016). Development of System-level Performance Measures for Evaluation of Models of Care for Inflammatory Arthritis in Canada. The Journal of Rheumatology, 43(3), 530–540. https://doi.org/10.3899/jrheum.150839
   PubMed Web of Science ®Google Scholar
• Barber, C. E., Patel, J. N., Woodhouse, L., Smith, C., Weiss, S., Homik, J., LeClercq, S., Mosher, D., Christiansen, T., Howden, J. S., Wasylak, T., Greenwood Lee, J., Emrick, A., Suter, E., Kathol, B., Khodyakov, D., Grant, S., Campbell-Scherer, D., Phillips, L., & Marshall, D. A. (2015). Development of key performance indicators to evaluate centralized intake for patients with osteoarthritis and rheumatoid arthritis. Arthritis Research & Therapy, 17, 322. https://doi.org/10.1186/s13075-015-0843-7
   PubMed Web of Science ®Google Scholar
• Baril, C., Gascon, V., & Cartier, S. (2014). Design and analysis of an outpatient orthopaedic clinic performance with discrete event simulation and design of experiments. Computers & Industrial Engineering, 78, 285–298. https://doi.org/10.1016/j.cie.2014.05.006
   Web of Science ®Google Scholar
• Baril, C., Gascon, V., Miller, J., & Côté, N. (2016). Use of a discrete-event simulation in a Kaizen event: A case study in healthcare. European Journal of Operational Research, 249(1), 327–339. https://doi.org/10.1016/j.ejor.2015.08.036
   Web of Science ®Google Scholar
• Brailsford, S. C., & Hilton, N. A. (2001). A comparison of discrete event simulation and system dynamics for modelling health care systems. In J, Riley (Ed.), Planning for the Future: Health Service Quality and Emergency Accessibility. Operational Research Applied to Health Services (ORAHS),Glasgow Caledonian University (2001). Retrieved October 15, 2022, from https://eprints.soton.ac.uk/35689/1/glasgow_paper.pdf
   Google Scholar
• Carpenter, T., & Katz, S. J. (2014). Review of a rheumatology triage system: Simple, accurate, and effective. Clinical Rheumatology, 33(2), 247–252. https://doi.org/10.1007/s10067-013-2413-1
   PubMed Web of Science ®Google Scholar
• Damani, Z., Bohm, E., Quan, H., Noseworthy, T., MacKean, G., Loucks, L., & Marshall, D. A. (2019). Improving the quality of care with a single-entry model of referral for total joint replacement: A preimplementation/postimplementation evaluation. British Medical Journal Open, 9(12), e028373. https://doi.org/10.1136/bmjopen-2018-028373
   PubMed Web of Science ®Google Scholar
• Damani, Z., Conner-Spady, B., Nash, T., Tom Stelfox, H., Noseworthy, T. W., & Marshall, D. A. (2017). What is the influence of single-entry models on access to elective surgical procedures? A systematic review. British Medical Journal Open, 7(2), e012225. https://doi.org/10.1136/bmjopen-2016-012225
   PubMed Web of Science ®Google Scholar
• Djanatliev, A., & German, R. (2013). Prospective healthcare decision-making by combined system dynamics, discrete-event and agent-based simulation. 2013 Winter Simulations Conference (WSC), 2013, Washington, DC, USA, (pp. 270–281). https://doi.org/10.1109/WSC.2013.6721426
   Google Scholar
• Eriksson, J. K., Johansson, K., Askling, J., & Neovius, M. (2015). Costs for hospital care, drugs and lost work days in incident and prevalent rheumatoid arthritis: How large, and how are they distributed? Annals of the Rheumatic Diseases, 74(4), 648–654. https://doi.org/10.1136/annrheumdis-2013-204080
   PubMed Web of Science ®Google Scholar
• Farrer, C., Abraham, L., Jerome, D., Hochman, J., & Gakhal, N. (2016). Triage of rheumatology referrals facilitates wait time benchmarks. The Journal of Rheumatology, 43(11), 2064–2067. https://doi.org/10.3899/jrheum.151235
   PubMed Web of Science ®Google Scholar
• Günal, M. M., & Pidd, M. (2010). Discrete event simulation for performance modelling in health care: A review of the literature. Journal of Simulation, 4(1), 42–51. https://doi.org/10.1057/jos.2009.25
   Web of Science ®Google Scholar
• Hamrock, E., Paige, K., Parks, J., Scheulen, J., & Levin, S. (2013). Discrete event simulation for healthcare organizations: a tool for decision making. Journal of Healthcare Management, 58(2), 110–124. https://doi.org/10.1097/00115514-201303000-00007
   PubMed Web of Science ®Google Scholar
• Hurst, J., & Siciliani, L. (2003). Tackling excessive waiting times for elective surgery: a comparison of policies in twelve OECD Countries. https://doi.org/10.1787/108471127058
   Google Scholar
• Husberg, M., Bernfort, L., & Hallert, E. (2018). Costs and disease activity in early rheumatoid arthritis in 1996–2000 and 2006–2011, improved outcome and shift in distribution of costs: A two-year follow-up. Scandinavian Journal of Rheumatology, 47(5), 378–383. https://doi.org/10.1080/03009742.2017.1420224
   PubMed Web of Science ®Google Scholar
• Karlberg, H. I., & Brinkmo, B.-M. (2009). The unethical focus on access: A study of medical ethics and the waiting-time guarantee. Scandinavian Journal of Public Health, 37(2), 117–121. https://doi.org/10.1177/1403494808101359
   PubMed Web of Science ®Google Scholar
• Kelton, W. D., Sadowski, R. P., & Swets, N. B. (2010). Simulation with arena. McGraw-Hill.
   Google Scholar
• Kozlowski, D., & Worthington, D. (2015). Use of queue modelling in the analysis of elective patient treatment governed by a maximum waiting time policy. European Journal of Operational Research, 244(1), 331–338. https://doi.org/10.1016/j.ejor.2015.01.024
   Web of Science ®Google Scholar
• Kreindler, S. A. (2008). Watching your wait: evidence-informed strategies for reducing health care wait times. Quality Management in Health Care, 17(2), 128–135. https://doi.org/10.1097/01.QMH.0000316990.48673.9f
   PubMedGoogle Scholar
• Lo, T., Parkinson, L., Cunich, M., & Byles, J. (2016). Cost of arthritis: A systematic review of methodologies used for direct costs. Expert Review of Pharmacoeconomics & Outcomes Research, 16(1), 51–65. https://doi.org/10.1586/14737167.2016.1126513
   PubMed Web of Science ®Google Scholar
• Monks, T., Currie, C. S., Onggo, B. S., Robinson, S., Kunc, M., & Taylor, S. J. (2019). Strengthening the reporting of empirical simulation studies: Introducing the STRESS guidelines. Journal of Simulation, 13(1), 55–67. https://doi.org/10.1080/17477778.2018.1442155
   Web of Science ®Google Scholar
• Moscelli, G., Siciliani, L., & Tonei, V. (2016). Do waiting times affect health outcomes? Evidence from coronary bypass. Social Science & Medicine, 161, 151–159. https://doi.org/10.1016/j.socscimed.2016.05.043
   PubMed Web of Science ®Google Scholar
• Moura, C. S., Abrahamowicz, M., Beauchamp, M.-E., Lacaille, D., Wang, Y., Boire, G., Fortin, P. R., Bessette, L., Bombardier, C., Widdifield, J., Hanly, J. G., Feldman, D., Maksymowych, W., Peschken, C., Barnabe, C., Edworthy, S., & Bernatsky, S. (2015). Early medication use in new-onset rheumatoid arthritis may delay joint replacement: Results of a large population-based study. Arthritis Research & Therapy, 17(1), 197. https://doi.org/10.1186/s13075-015-0713-3
   PubMedGoogle Scholar
• Naiker, U., FitzGerald, G., Dulhunty, J. M., & Rosemann, M. (2018). Time to wait: A systematic review of strategies that affect outpatient waiting times. Australian Health Review, 42(3), 286. https://doi.org/10.1071/AH16275
   PubMed Web of Science ®Google Scholar
• Neovius, M., Simard, J. F., Askling, J., & for the ARTIS Study Group. (2011). How large are the productivity losses in contemporary patients with RA, and how soon in relation to diagnosis do they develop? Annals of the Rheumatic Diseases, 70(6), 1010–1015. https://doi.org/10.1136/ard.2010.136812
   PubMed Web of Science ®Google Scholar
• OECD. (2013). Waiting time policies in the health sector: what works? (Siciliani, L., Borowitz, M., & Moran, V. Eds.). https://doi.org/10.1787/9789264179080-en
   Google Scholar
• Parks, J. K., Engblom, P., Hamrock, E., Satjapot, S., & Levin, S. (2011). Designed to fail: how computer simulation can detect fundamental flaws in clinic flow. Journal of Healthcare Management, 56(2), 135–146. https://doi.org/10.1097/00115514-201103000-00009
   PubMed Web of Science ®Google Scholar
• Puolakka, K. (2005). Monetary value of lost productivity over a five year follow up in early rheumatoid arthritis estimated on the basis of official register data on patients’ sickness absence and gross income: Experience from the FIN-RACo trial. Annals of the Rheumatic Diseases, 65(7), 899–904. https://doi.org/10.1136/ard.2005.045807
   PubMed Web of Science ®Google Scholar
• Rohleder, T. R., Lewkonia, P., Bischak, D. P., Duffy, P., & Hendijani, R. (2011). Using simulation modelling to improve patient flow at an outpatient orthopedic clinic. Health Care Management Science, 14(2), 135–145. https://doi.org/10.1007/s10729-010-9145-4
   PubMed Web of Science ®Google Scholar
• Smolen, J. S., Breedveld, F. C., Burmester, G. R., Bykerk, V., Dougados, M., Emery, P., Kvien, T. K., Navarro-Compán, M. V., Oliver, S., Schoels, M., Scholte-Voshaar, M., Stamm, T., Stoffer, M., Takeuchi, T., Aletaha, D., Andreu, J. L., Aringer, M., Bergman, M., Betteridge, N., & van der Heijde, D. (2016). Treating rheumatoid arthritis to target: 2014 update of the recommendations of an international task force. Annals of the Rheumatic Diseases, 75(1), 3–15. https://doi.org/10.1136/annrheumdis-2015-207524
   PubMed Web of Science ®Google Scholar
• Suter, E., Birney, A., Charland, P., Misfeldt, R., Weiss, S., Howden, J. S., Hendricks, J., Lupton, T., & Marshall, D. (2015). Optimizing the interprofessional workforce for centralized intake of patients with osteoarthritis and rheumatoid disease: Case study. Human Resources for Health, 13(1), 41. https://doi.org/10.1186/s12960-015-0033-3
   PubMedGoogle Scholar
• Systems Modelling Corporation. (2000) . Arena. Rockwell Automation.
   Google Scholar
• Tagimacruz, T., Bischak, D. P., & Marshall, D. A. (2020). Alternative care providers in rheumatoid arthritis patient care: A queueing and simulation analysis. Health Systems, 10(4), 249–267. https://doi.org/10.1080/20476965.2020.1771619
   Web of Science ®Google Scholar
• Tuncay, R., Eksioglu, E., Cakir, B., Gurcay, E., & Cakci, A. (2007). Factors affecting drug treatment compliance in patients with rheumatoid arthritis. Rheumatology International, 27(8), 743–746. https://doi.org/10.1007/s00296-006-0299-9
   PubMed Web of Science ®Google Scholar
• Viana, J., Brailsford, S. C., Harindra, V., & Harper, P. R. (2014). Combining discrete-event simulation and system dynamics in a healthcare setting: A composite model for Chlamydia infection. European Journal of Operational Research, 237(1), 196–206. https://doi.org/10.1016/j.ejor.2014.02.052
   Web of Science ®Google Scholar
• Viberg, N., Forsberg, B. C., Borowitz, M., & Molin, R. (2013). International comparisons of waiting times in health care – Limitations and prospects. Health Policy, 112(1–2), 53–61. https://doi.org/10.1016/j.healthpol.2013.06.013
   PubMed Web of Science ®Google Scholar
• Visser, H. (2005). Early diagnosis of rheumatoid arthritis. Best Practice & Research Clinical Rheumatology, 19(1), 55–72. https://doi.org/10.1016/j.berh.2004.08.005
   PubMed Web of Science ®Google Scholar
• Vrangbæk, K., Østergren, K., Birk, H. O., & Winblad, U. (2007). Patient reactions to hospital choice in Norway, Denmark, and Sweden. Health Economics, Policy, and Law, 2(2), 125–152. https://doi.org/10.1017/S174413310700401X
   PubMed Web of Science ®Google Scholar
• Wen, J., Geng, N., & Xie, X. (2020). Real-time scheduling of semi-urgent patients under waiting time targets. International Journal of Production Research, 58(4), 1127–1143. https://doi.org/10.1080/00207543.2019.1612965
   Web of Science ®Google Scholar
• Widdifield, J., Bernatsky, S., Pope, J., Kuriya, B., Barber, C., Eder, L., Ahluwalia, V., Ling, V., Gozdyra, P., Hofstetter, C., Lyddiatt, A., Paterson, J. M., & Thorne, C. (2021). Evaluation of rheumatology workforce supply changes in Ontario, Canada, from 2000 to 2030. Healthcare Policy | Politiques de Santé, 16(3), 119–135. https://doi.org/10.12927/hcpol.2021.26428
   Google Scholar
• Zhang, X. (2018). Application of discrete event simulation in health care: A systematic review. BMC Health Services Research, 18(1), 687. https://doi.org/10.1186/s12913-018-3456-4
   PubMedGoogle Scholar