The application of Discrete Event Simulation (DES) to enhance wait times and space utilization of a Multi-department Clinic

Authors

  • Zahra Zamani Director of Research, BSA LifeStructures, Raleigh, NC, United States https://orcid.org/0000-0003-0536-245X
  • Timothy J. Spence President, BSA Life Structures, Raleigh, NC, United States

DOI:

https://doi.org/10.26418/ijeas.2023.3.01.1-16

Keywords:

capacity planning, clinic design, discrete event simulation modeling, healthcare design, predictive modeling

Abstract

There has been an increasing demand for outpatient services that support patient experience and quality of care. Consequently, healthcare systems aspire to reduce operational costs and improve resource utilization through different strategies. Given the complexity of multi-department outpatient healthcare facility operations, DES has been recognized as an effective tool for evaluating performance outcomes and design decisions. The purpose of this study was to introduce Discrete Event Simulation (DES) as an effective tool in the architecture planning of a multi-department outpatient clinic. The DES integrated patient flow, schedules, proposed layout, and staffing information to identify space and staff utilization, wait for the provider (WFP), and wait for the room (WFR). The study explored the effects of pooling decentralized registration desks into a centralized desk and staffing with additional kiosks for the primary care departments. We also converted underutilized exam rooms into telehealth rooms, flexible consult-exam rooms, and dedicated pediatric waiting areas. registration pooling and kiosks improved staff resource utilization. Allocating additional providers and exam rooms to departments reduced their WFP and WFR. DES indicated minimal operational effects of transitioning underutilized exam rooms to alternate functions. This finding enabled us to determine if space allocation modifications negatively affected operations. The findings suggest the effectiveness of DES in analyzing the impact of alternative design strategies on operational outcomes through a data-driven approach. This understanding is valuable, as improved resource allocation and streamlined operation are associated with cost reduction and enhanced patient and staff satisfaction.

Downloads

Download data is not yet available.

References

al Hroub, A., Obaid, A., Yaseen, R., El-Aqoul, A., Zghool, N., Abu-Khudair, H., al Kakani, D., & Alloubani, A. (2019). Improving the workflow efficiency of an outpatient pain clinic at a specialized oncology center by implementing lean principles. Asia-Pacific Journal of Oncology Nursing, 6(4), 381–388.

American Medical Association. (2020). Telehealth Implementation Playbook. https://www.ama-assn.org/system/files/2020-04/ama-telehealth-implementation-playbook.pdf

Arnolds, I. V., & Gartner, D. (2018). Improving hospital layout planning through clinical pathway mining. Annals of Operations Research, 263(1), 453–477.

Avis. (2020). Planning facilities for telehealth: Design experts lay out planning considerations for successful adoption of new technology infrastructure. HFM Magazine. https://www.hfmmagazine.com/articles/4036-planning-facilities-for-telehealth

Belt, K. (2015). How self-service check-in works in the real world. Baptist Health CFO Katrina Belt gives the in-sider scoop on kiosk adoption and use by staff and patients. Health Management Technology, 36(4), 18–19.

Betancourt, J. A., Rosenberg, M. A., Zevallos, A., Brown, J. R., & Mileski, M. (2020). The impact of COVID-19 on telemedicine utilization across multiple service lines in the United States. Healthcare, 8(4), 380:1-21.

Brailsford, S.C. & Hilton, N.A. (2001) A comparison of discrete event simulation and system dynamics for modelling health care systems. In, Riley, J. (ed.) Planning for the Future: Health Service Quality and Emergency Accessibility. Operational Research Applied to Health Services (ORAHS) (01/01/01) Glasgow Caledonian University

Brambilla, A., Sun, T., Elshazly, W., Ghazy, A., Barach, P., Lindahl, G., & Capolongo, S. (2021). Flexibility during the COVID-19 pandemic response: Healthcare facility assessment tools for resilient evaluation. International Journal of Environmental Research and Public Health, 18(21), 11478.

Brotman, J. J., & Kotloff, R. M. (2021). Providing outpatient telehealth services in the United States: before and during coronavirus disease 2019. Chest, 159(4), 1548–1558.

Buffoli, M., Nachiero, D., & Capolongo, S. (2012). Flexible healthcare structures: analysis and evaluation of possible strategies and technologies. Ann Ig, 24(6), 543–552.

Cai, H., & Jia, J. (2019). Using discrete event simulation (DES) to support performance-driven healthcare design. HERD: Health Environments Research & Design Journal, 12(3), 89–106.

Capolongo, S., Rebecchi, A., Buffoli, M., Appolloni, L., Signorelli, C., Fara, G. M., & D’Alessandro, D. (2020). COVID-19 and cities: From urban health strategies to the pandemic challenge. A decalogue of public health opportunities. Acta Bio Medica: Atenei Parmensis, 91(2), 13.

Centers for Medicare and Medicaid Services. (2022). Hospital Outpatient Quality Reporting Program. CMS.GOV. https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/HospitalQualityInits/HospitalOutpatientQualityReportingProgram#:~:text=Outpatient%20Department%20Measures&text=Outpatient%20often%20refers%20to%20a,an%20order%20for%20inpatient%20admission.

Chahal, K., Eldabi, T., & Young, T. (2013). A conceptual framework for hybrid system dynamics and discrete event simulation for healthcare. Journal of Enterprise Information Management, 26(1/2), 50–74.

Ciulla, T. A., Tatikonda, M. v, ElMaraghi, Y. A., Hussain, R. M., Hill, A. L., Clary, J. M., & Hattab, E. (2018). Lean six sigma techniques to improve ophthalmology clinic efficiency. Retina, 38(9), 1688–1698.

Culver, E., Sznycer-Taub, M., & Donovan, P. (2022). The unexpected ways regulatory changes can impact outpatient shifts. Advisory Board. https://www.advisory.com/sponsored/outpatient-shifts

Fone, D., Hollinghurst, S., Temple, M., Round, A., Lester, N., Weightman, A., Roberts, K., Coyle, E., Bevan, G., & Palmer, S. (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.

Greiwe, J. (2022). Telemedicine lessons learned during the COVID-19 pandemic. Current Allergy and Asthma Reports, 22(1), 1–5.

Guilford-Blake, R. (2022). Key considerations when designing patient rooms for the future. https://insights.samsung.com/2022/01/19/key-considerations-when-designing-patient-rooms-for-the-future/

Hargraves, J., & Reiff, J. (2019). Shifting Care from Office to Outpatient Settings: Services are Increasingly Performed in Outpatient Settings with Higher Prices. Health Care Cost Insitute. https://healthcostinstitute.org/in-the-news/shifting-care-office-to-outpatient

Hong, T. S., Shang, P. P., Arumugam, M., & Yusuff, R. M. (2013). Use of simulation to solve outpatient clinic problems: A review of the literature. South African Journal of Industrial Engineering, 24(3), 27–42.

Khorram-Manesh, A. (2020). Flexible surge capacity–public health, public education, and disaster management. Health Promotion Perspectives, 10(3), 175–179.

Leong, Z. A., Horn, M. S., Thaniel, L., & Meier, E. (2018). Inspiring AWE: Transforming clinic waiting rooms into informal learning environments with active waiting education. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 1–12.

Łukasik, M., & Porębska, A. (2022). Responsiveness and adaptability of healthcare facilities in emergency scenarios: COVID-19 experience. International Journal of Environmental Research and Public Health, 19(2), 675.

McLaughlan, R. (2018). Psychosocially supportive design: The case for greater attention to social space within the pediatric hospital. HERD: Health Environments Research & Design Journal, 11(2), 151–162.

McLaughlan, R., Sadek, A., & Willis, J. (2019). Attractions to fuel the imagination: Reframing understandings of the role of distraction relative to well-being in the pediatric hospital. HERD: Health Environments Research & Design Journal, 12(2), 130–146.

Medpac. (2019). Hospital inpatient and outpatient services: Assessing payment adequacy and updating payments. https://www.medpac.gov/wp-content/uploads/import_data/scrape_files/docs/default-source/reports/mar19_medpac_ch3_sec.pdf

Mielczarek, B., & Uziałko-Mydlikowska, J. (2012). Application of computer simulation modeling in the health care sector: a survey. Simulation, 88(2), 197–216.

Mosher, Z. A., Hudson, P. W., Lee, S. R., Perez, J. L., Arguello, A. M., McGwin Jr, G., Theiss, S. M., & Ponce, B. A. (2020). Check-in kiosks in the outpatient clinical setting: fad or the future. South Med J, 113(3), 134–139.

Norouzzadeh, S., Riebling, N., Carter, L., Conigliaro, J., & Doerfler, M. E. (2015). Simulation modeling to optimize healthcare delivery in an outpatient clinic. 2015 Winter Simulation Conference (WSC), 1355–1366.

OECD Health Statistics. (2023). Health expenditure and financing. Organization for Economic Co-Operation and Development. https://stats.oecd.org/Index.aspx?DataSetCode=SHA

Patel, S. Y., Mehrotra, A., Huskamp, H. A., Uscher-Pines, L., Ganguli, I., & Barnett, M. L. (2021). Variation in telemedicine use and outpatient care during the COVID-19 pandemic in the United States: study examines variation in total US outpatient visits and telemedicine use across patient demographics, specialties, and conditions during the COVID-19 pandemic. Health Affairs, 40(2), 349–358.

Rohleder, T. R., Bischak, D. P., & Baskin, L. B. (2007). Modeling patient service centers with simulation and system dynamics. Health Care Management Science, 10(1), 1–12.

Santibáñez, P., Chow, V. S., French, J., Puterman, M. L., & Tyldesley, S. (2009). Reducing patient wait times and improving resource utilization at British Columbia Cancer Agency’s ambulatory care unit through simulation. Health Care Management Science, 12(4), 392–407.

Song, W., Chen, A. X., Conti, T. F., Greenlee, T. E., Hom, G. L., Rachitskaya, A. v, & Singh, R. P. (2020). Characterization of kiosk usage for ophthalmic outpatient visits. Ophthalmic Surgery, Lasers and Imaging Retina, 51(12), 684–690.

Suhaimi, N., Vahdat, V., & Griffin, J. (2018). Building a flexible simulation model for modeling multiple outpatient orthopedic clinics. 2018 Winter Simulation Conference (WSC), 2612–2623.

Swain, J. J. (2011). Simulation Software Survey-A brief history of discrete-event simulation and the state of simulation tools today. OR/MS Today, 38(5), 56. https://www.informs.org/ORMS-Today/Public-Articles/October-Volume-44-Number-5/Simulation-Software-Survey-Simulation-new-and-improved-reality-show

Torres, C. (2022). Inpatient vs Outpatient Care and Health Services. University of Medicine and Health Sciences . https://www.umhs-sk.org/blog/inpatient-vs-outpatient

Tresenriter, M., Holdaway, J., Killeen, J., Chan, T., & Dameff, C. (2021). The implementation of an emergency medicine telehealth system during a pandemic. The Journal of Emergency Medicine, 60(4), 548–553.

Vahdat, V., Griffin, J., & Stahl, J. E. (2018). Decreasing patient length of stay via new flexible exam room allocation policies in ambulatory care clinics. Health Care Management Science, 21(4), 492–516.

Vahdat, V., Namin, A., Azghandi, R., & Griffin, J. (2019). Improving patient timeliness of care through efficient outpatient clinic layout design using data-driven simulation and optimisation. Health Systems, 8(3), 162–183.

Vanberkel, P. T., Boucherie, R. J., Hans, E. W., Hurink, J. L., & Litvak, N. (2012). Efficiency evaluation for pooling resources in health care. OR Spectrum, 34(2), 371–390.

Vázquez-Serrano, J. I., Peimbert-García, R. E., & Cárdenas-Barrón, L. E. (2021). Discrete-event simulation modeling in healthcare: A comprehensive review. International Journal of Environmental Research and Public Health, 18(22), 12262.

Wang, L., Weiss, J., Ryan, E. B., Waldman, J., Rubin, S., & Griffin, J. L. (2021). Telemedicine increases access to buprenorphine initiation during the COVID-19 pandemic. Journal of Substance Abuse Treatment, 124, 108272.

Wennberg, J. E., Brownlee, S., Fisher, E. S., Skinner, J. S., & Weinstein, J. N. (2008). An Agenda for Change: Improving Quality and Curbing Health Care Spending: Opportunities for the Congress and the Obama Administration. The Dartmouth Institute for Health Policy and Clinical Practice.

Wischik, D., Handley, M., & Braun, M. B. (2008). The resource pooling principle. ACM SIGCOMM Computer Communication Review, 38(5), 47–52.

Wosik, J., Fudim, M., Cameron, B., Gellad, Z. F., Cho, A., Phinney, D., Curtis, S., Roman, M., Poon, E. G., & Ferranti, J. (2020). Telehealth transformation: COVID-19 and the rise of virtual care. Journal of the American Medical Informatics Association, 27(6), 957–962.

Zamani, Z. (2022). Leveraging discrete event simulation modeling to evaluate design and process improvements of an emergency department. Journal of Design for Resilience in Architecture and Planning, 3(3), 397–408.

Zeigler, B. P., Kim, T. G., & Praehofer, H. (2000). Theory of modeling and simulation. Academic press.

Downloads

Published

2023-02-28

How to Cite

Zamani, Z., & Spence, T. J. . (2023). The application of Discrete Event Simulation (DES) to enhance wait times and space utilization of a Multi-department Clinic. International Journal of Environment, Architecture, and Societies, 3(01), 1-16. https://doi.org/10.26418/ijeas.2023.3.01.1-16