Healthc Inform Res.  2017 Jan;23(1):35-42. 10.4258/hir.2017.23.1.35.

Application of Queueing Theory to the Analysis of Changes in Outpatients' Waiting Times in Hospitals Introducing EMR

Affiliations
  • 1Department of Healthcare Administration, Kosin University, Busan, Korea.
  • 2Graduate School of Public Health, Yonsei University, Seoul, Korea.
  • 3Division of Business Administration, Yonsei University Wonju Campus, Wonju, Korea. yusong@yonsei.ac.kr

Abstract


OBJECTIVES
This research used queueing theory to analyze changes in outpatients' waiting times before and after the introduction of Electronic Medical Record (EMR) systems.
METHODS
We focused on the exact drawing of two fundamental parameters for queueing analysis, arrival rate (λ) and service rate (µ), from digital data to apply queueing theory to the analysis of outpatients' waiting times. We used outpatients' reception times and consultation finish times to calculate the arrival and service rates, respectively.
RESULTS
Using queueing theory, we could calculate waiting time excluding distorted values from the digital data and distortion factors, such as arrival before the hospital open time, which occurs frequently in the initial stage of a queueing system. We analyzed changes in outpatients' waiting times before and after the introduction of EMR using the methodology proposed in this paper, and found that the outpatients' waiting time decreases after the introduction of EMR. More specifically, the outpatients' waiting times in the target public hospitals have decreased by rates in the range between 44% and 78%.
CONCLUSIONS
It is possible to analyze waiting times while minimizing input errors and limitations influencing consultation procedures if we use digital data and apply the queueing theory. Our results verify that the introduction of EMR contributes to the improvement of patient services by decreasing outpatients' waiting time, or by increasing efficiency. It is also expected that our methodology or its expansion could contribute to the improvement of hospital service by assisting the identification and resolution of bottlenecks in the outpatient consultation process.

Keyword

Electronic Medical Record; Healthcare; Queue; Waiting Time

MeSH Terms

Delivery of Health Care
Electronic Health Records
Hospitals, Public
Humans
Outpatients

Figure

  • Figure 1 Patient consultation paths.


Reference

1. Jeong BH, Choi JT, Park SS. An implementation of medical treatment schedule guidance system for inpatients satisfaction improvement. J Korean Inst Inf Technol. 2012; 10(2):88–93.
2. Chae YM, Cho KW, Kim HS, Park CB. Evaluation of hospital information system based on the performance reference model. Korean J Health Serv Manag. 2011; 5(1):1–13.
Article
3. Cho KW, Bae SK, Ryu JH, Kim KN, An CH, Chae YM. Performance evaluation of public hospital information systems by the information system success model. Healthc Inform Res. 2015; 21(1):43–48.
Article
4. An CH. Study on the economic analysis of hospital information system for regional medical center [dissertation]. Seoul, Korea: Yonsei University;2013.
5. Park SH. Analysis of factors delaying on waiting time for medical examination of outpatient on a hospital. J Korean Soc Qual Assur Health Care. 2001; 8(1):56–72.
6. Hwang JI. Factors influencing consultation time and waiting time of ambulatory patients in a tertiary teaching hospital. Qual Improv Health Care. 2006; 12(1):6–16.
7. Ko YK. The relationships among waiting time, patient's satisfaction, and revisiting intention of outpatients in general hospital. J Korean Acad Nurs Adm. 2010; 16(3):219–228.
Article
8. Yeo H, Bak W, Yoo M, Park S, Lee S. Evaluation of patients' queue environment on medical service using queueing theory. J Korean Soc Qual Manag. 2014; 42(1):71–79.
Article
9. Green LV, Soares J, Giglio JF, Green RA. Using queueing theory to increase the effectiveness of emergency department provider staffing. Acad Emerg Med. 2006; 13(1):61–68.
Article
10. Park CK, Kwag EJ. A case study about managing waiting time for raising customer's satisfaction in the medical service. Korean J Hosp Manag. 2009; 14(3):132–153.
11. HallR. BelsonD. MuraliP. DessoukyM. Modeling patient flows through the health care system. In : Hall R, editor. Patient flow. New York (NY): Springer;2013. p. 3–42.
12. Kim S, Seo H, Lee J, Kwon Y, Kim S, Park I, et al. An application of a queueing network for waiting time reduction at the emergency care center. Proceedings of the Korean Operations and Management Science Society Conference. 2009 Oct 30; Seoul, Korea. p. 298–316.
13. Mandelbaum A, Momcilovic P, Tseytlin Y. On fair routing from emergency departments to hospital wards: QED queues with heterogeneous servers. Manag Sci. 2012; 58(7):1273–1291.
Article
14. Green LV, Savin S. Reducing delays for medical appointments: a queueing approach. Oper Res. 2008; 56(6):1526–1538.
Article
15. Kim S, Son U, Choi J, Roh J, Yang Y. Analysis of factors delaying on waiting time of outpatient in a general hospital. Health Welf. 2008; 10:107–120.
Article
16. Broyles JR, Cochran JK. Estimating business loss to a hospital emergency department from patient reneging by queuing-based regression. Proceedings of the 2007 Industrial Engineering Research Conference. 2007 May 19-23; Nashville, TN. p. 613–618.
17. Roche KT, Cochran JK. Improving patient safety by maximizing fast-track benefits in the emergency department: a queuing network approach. Proceedings of the 2007 Industrial Engineering Research Conference. 2007 May 19-23; Nashville, TN. p. 619–624.
18. Worthington DJ. Queueing models for hospital waiting lists. J Oper Res Soc. 1987; 38(5):413–422.
Article
19. Green L. Queueing analysis in healthcare. In : Hall RW, editor. Patient flow: reducing delay in healthcare delivery. New York (NY): Springer;2006. p. 281–307.
20. Fiems D, Koole G, Nain P. Waiting times of scheduled patients in the presence of emergency requests [Internet]. place unknown: publisher unknown;2015. cited at 2016 Dec 3. Available from: http://www.math.vu.nl/~koole/publications/2005report1/art.pdf.
21. Koizumi N, Kuno E, Smith TE. Modeling patient flows using a queuing network with blocking. Health Care Manag Sci. 2005; 8(1):49–60.
Article
22. de Bruin AM, van Rossum AC, Visser MC, Koole GM. Modeling the emergency cardiac in-patient flow: an application of queuing theory. Health Care Manag Sci. 2007; 10(2):125–137.
Article
23. Gorunescu F, McClean SI, Millard PH. A queueing model for bed-occupancy management and planning hospitals. J Oper Res Soc. 2002; 53(1):19–24.
Article
24. Gorunescu F, McClean SI, Millard PH. Using a queueing model to help plan bed allocation in a department of geriatric medicine. Health Care Manag Sci. 2002; 5(4):307–312.
25. Park CS, Koh SH. A case study on the improvement of general hospital outpatients waiting time using TOC methodology. Korean J Hosp Manag. 2011; 16(1):77–100.
Full Text Links
  • HIR
Actions
Cited
CITED
export Copy
Close
Share
  • Twitter
  • Facebook
Similar articles
Copyright © 2024 by Korean Association of Medical Journal Editors. All rights reserved.     E-mail: koreamed@kamje.or.kr