Improving emergency department performance by revising the patient–physician assignment process

Abstract

Emergency departments (EDs) are continuously exploring opportunities to improve their efficiency. A new opportunity lies in revising the patient–physician assignment process by limiting the number of patients simultaneously assigned to a single physician, which is defined as the application of a case manager approach with limited caseloads. The potential of introducing a case manager approach with limited caseloads as a way to improve physician productivity, and consequently ED performance, is investigated by use of a discrete-event simulation model based on a real-life case study. In addition, as the case manager system is characterised by three parameters that can be customised and optimised (i.e. caseload limit, pre-assignment queueing discipline and internal queueing discipline), the impact of these parameters on the effectiveness to improve ED performance in terms of length-of-stay and door-to-doctor time is evaluated. To the best of our knowledge, this paper is the first to examine the potential of a case manager system with limited caseloads in a complex service system like a real-life ED, and to investigate the impact of the three system parameters on the results. The outcomes of the study show that performance can be improved significantly by introducing a case manager system, and that the system parameters have an impact on the effect size.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Notes

  1. 1.

    Shortened as case manager approach in the remainder of this paper.

  2. 2.

    The technical report can be obtained from the corresponding author upon request or on the website https://www.uhasselt.be/Research-group-Logistics.

References

  1. Abo-Hamad W, Arisha A (2013) Simulation-based framework to improve patient experience in an emergency department. Eur J Oper Res 224(1):154–166. https://doi.org/10.1016/j.ejor.2012.07.028

    Article  Google Scholar 

  2. Aral S, Brynjolfsson E, Van Alstyne M (2012) Information, technology, and information worker productivity. Inf Syst Res 23(3–part–2):849–867. https://doi.org/10.1287/isre.1110.0408

    Article  Google Scholar 

  3. Batt RJ, Terwiesch C (2012) Doctors under load: an empirical study of state-dependent service times in emergency care. The Wharton School, The University of Pennsylvania, Philadelphia

    Google Scholar 

  4. Batt RJ, Terwiesch C (2016) Early task initiation and other load-adaptive mechanisms in the emergency department. Manag Sci 63(11):3531–3551. https://doi.org/10.1287/mnsc.2016.2516

    Article  Google Scholar 

  5. Bhattacharjee P, Ray PK (2014) Patient flow modelling and performance analysis of healthcare delivery processes in hospitals: a review and reflections. Comput Ind Eng 78:299–312. https://doi.org/10.1016/j.cie.2014.04.016

    Article  Google Scholar 

  6. Brailsford SC, Hilton NA (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. Glasgow Caledonian University, Glasgow

    Google Scholar 

  7. Campello F, Ingolfsson A, Shumsky RA (2017) Queueing models of case managers. Manag Sci 63(3):882–900. https://doi.org/10.1287/mnsc.2015.2368

    Article  Google Scholar 

  8. Carmen R, Defraeye M, Van Nieuwenhuyse I (2015) A decision support system for capacity planning in emergency departments. Int J Simul Model 14(2):299–312. https://doi.org/10.2507/IJSIMM14(2)10.308

    Article  Google Scholar 

  9. Chisholm CD, Collison EK, Nelson DR, Cordell WH (2000) Emergency department workplace interruptions are emergency physicians “interrupt-driven” and “multitasking”? Acad Emerg Med 7(11):1239–1243. https://doi.org/10.1111/j.1553-2712.2000.tb00469.x

    Article  Google Scholar 

  10. Cildoz M, Mallor F, Ibarra A (2018) Analysing the ED patient flow management problem by using accumulating priority queues and simulation-based optimization. In: 2018 winter simulation conference (WSC). IEEE, Gothenburg, Sweden, pp 2107–2118

  11. Delasay M, Ingolfsson A, Kolfal B, Schultz KL (2015) Load effect on service times. SSRN Electron J. https://doi.org/10.2139/ssrn.2647201

    Article  MATH  Google Scholar 

  12. Dobson G, Tezcan T, Tilson V (2013) Optimal workflow decisions for investigators in systems with interruptions. Manag Sci 59(5):1125–1141. https://doi.org/10.1287/mnsc.1120.1632

    Article  Google Scholar 

  13. Duguay C, Chetouane F (2007) Modeling and improving emergency department systems using discrete event simulation. Simulation 83(4):311–320. https://doi.org/10.1177/0037549707083111

    Article  Google Scholar 

  14. Duma D, Aringhieri R (2018) An ad hoc process mining approach to discover patient paths of an emergency department. Flex Serv Manuf J 32:1–29

    Google Scholar 

  15. Ferrand YB, Magazine MJ, Rao US, Glass TF (2018) Managing responsiveness in the emergency department: comparing dynamic priority queue with fast track. J Oper Manag 58–59(1):15–26. https://doi.org/10.1016/j.jom.2018.03.001

    Article  Google Scholar 

  16. Field A (2013) Discovering statistics using IBM SPSS statistics. Sage, London

    Google Scholar 

  17. Forster AJ (2003) The effect of hospital occupancy on emergency department length of stay and patient disposition. Acad Emerg Med 10(2):127–133. https://doi.org/10.1197/aemj.10.2.127

    Article  Google Scholar 

  18. Ghanes K, Wargon M, Jouini O, Jemai Z, Diakogiannis A, Hellmann R, Thomas V, Koole G (2015) Simulation-based optimization of staffing levels in an emergency department. Simulation 91(10):942–953. https://doi.org/10.1177/0037549715606808

    Article  Google Scholar 

  19. Graff LG, Wolf S, Dinwoodie R, Buono D, Mucci D (1993) Emergency physician workload: a time study. Ann Emerg Med 22(7):1156–1163. https://doi.org/10.1016/S0196-0644(05)80982-5

    Article  Google Scholar 

  20. Gul M, Guneri AF (2015) A comprehensive review of emergency department simulation applications for normal and disaster conditions. Comput Ind Eng 83:327–344. https://doi.org/10.1016/j.cie.2015.02.018

    Article  Google Scholar 

  21. Gunal MM, Pidd M (2006) Understanding accident and emergency department performance using simulation. In: Proceedings of the 38th conference on winter simulation, winter simulation conference, pp 446–452

  22. Hoot NR, Aronsky D (2008) Systematic review of emergency department crowding: causes, effects, and solutions. Ann Emerg Med 52(2):126–136.e1. https://doi.org/10.1016/j.annemergmed.2008.03.014

    Article  Google Scholar 

  23. Kang H, Nembhard HB, Rafferty C, DeFlitch CJ (2014) Patient flow in the emergency department: a classification and analysis of admission process policies. Ann Emerg Med 64(4):335–342.e8. https://doi.org/10.1016/j.annemergmed.2014.04.011

    Article  Google Scholar 

  24. Kc DS (2014) Does multitasking improve performance? Evidence from the emergency department. Manuf Serv Oper Manag 16(2):168–183. https://doi.org/10.1287/msom.2013.0464

    Article  Google Scholar 

  25. Kc DS, Terwiesch C (2009) Impact of workload on service time and patient safety: an econometric analysis of hospital operations. Manag Sci 55(9):1486–1498. https://doi.org/10.1287/mnsc.1090.1037

    Article  Google Scholar 

  26. Kelton WD, Sadowski RP, Zupick NB (2015) Simulation with arena, 6th edn. McGraw-Hill Education, New York

    Google Scholar 

  27. Kuo YH, Rado O, Lupia B, Leung JMY, Graham CA (2016) Improving the efficiency of a hospital emergency department: a simulation study with indirectly imputed service-time distributions. Flex Serv Manuf J 28(1–2):120–147. https://doi.org/10.1007/s10696-014-9198-7

    Article  Google Scholar 

  28. Levin S, Aronsky D, Hemphill R, Han J, Slagle J, France DJ (2007) Shifting toward balance: measuring the distribution of workload among emergency physician teams. Ann Emerg Med 50(4):419–423. https://doi.org/10.1016/j.annemergmed.2007.04.007

    Article  Google Scholar 

  29. Li N, Stanford DA (2016) Multi-server accumulating priority queues with heterogeneous servers. Eur J Oper Res 252(3):866–878. https://doi.org/10.1016/j.ejor.2016.02.010

    MathSciNet  Article  MATH  Google Scholar 

  30. Li N, Stanford DA, Sharif AB, Caron RJ, Pardhan A (2019) Optimising key performance indicator adherence with application to emergency department congestion. Eur J Oper Res 272(1):313–323. https://doi.org/10.1016/j.ejor.2018.06.048

    MathSciNet  Article  MATH  Google Scholar 

  31. Maidstone R (2012) Discrete event simulation, system dynamics and agent based simulation: discussion and comparison. System 1(6):1–6

    Google Scholar 

  32. McKay KN, Engels JE, Jain S, Chudleigh L, Shilton D, Sharma A (2013) Emergency departments: “repairs while you wait, no appointment necessary”. In: Denton B (ed) Handbook of healthcare operations management. Springer, Berlin, pp 349–385

    Google Scholar 

  33. Mohiuddin S, Busby J, Savović J, Richards A, Northstone K, Hollingworth W, Donovan JL, Vasilakis C (2017) Patient flow within UK emergency departments: a systematic review of the use of computer simulation modelling methods. BMJ Open 7(5):e015007. https://doi.org/10.1136/bmjopen-2016-015007

    Article  Google Scholar 

  34. Paul JA, Lin L (2012) Models for improving patient throughput and waiting at hospital emergency departments. J Emerg Med 43(6):1119–1126. https://doi.org/10.1016/j.jemermed.2012.01.063

    Article  Google Scholar 

  35. Pines JM, Hilton JA, Weber EJ, Alkemade AJ, Al Shabanah H, Anderson PD, Bernhard M, Bertini A, Gries A, Ferrandiz S, Kumar VA, Harjola VP, Hogan B, Madsen B, Mason S, Öhlén G, Rainer T, Rathlev N, Revue E, Richardson D, Sattarian M, Schull MJ (2011) International perspectives on emergency department crowding. Acad Emerg Med 18(12):1358–1370. https://doi.org/10.1111/j.1553-2712.2011.01235.x

    Article  Google Scholar 

  36. Saghafian S, Hopp WJ, Van Oyen MP, Desmond JS, Kronick SL (2012) Patient streaming as a mechanism for improving responsiveness in emergency departments. Oper Res 60(5):1080–1097. https://doi.org/10.1287/opre.1120.1096

    Article  MATH  Google Scholar 

  37. Saghafian S, Austin G, Traub SJ (2015) Operations research/management contributions to emergency department patient flow optimization: review and research prospects. IIE Trans Healthc Syst Eng 5(2):101–123. https://doi.org/10.1080/19488300.2015.1017676

    Article  Google Scholar 

  38. Tan KW, Wang C, Lau HC (2012) Improving patient flow in emergency department through dynamic priority queue. In: 2012 IEEE international conference on automation science and engineering (CASE). IEEE, Seoul, Korea (South), pp 125–130. https://doi.org/10.1109/CoASE.2012.6386409

  39. Tan TF, Netessine S (2014) When does the devil make work? An empirical study of the impact of workload on worker productivity. Manag Sci 60(6):1574–1593. https://doi.org/10.1287/mnsc.2014.1950

    Article  Google Scholar 

  40. Vanbrabant L, Braekers K, Ramaekers K, Nieuwenhuyse IV (2019a) Simulation of emergency department operations: a comprehensive review of KPIs and operational improvements. Comput Ind Eng. https://doi.org/10.1016/j.cie.2019.03.025

    Article  Google Scholar 

  41. Vanbrabant L, Martin N, Ramaekers K, Braekers K (2019b) Quality of input data in emergency department simulations: framework and assessment techniques. Simul Model Pract Theory 91:83–101. https://doi.org/10.1016/j.simpat.2018.12.002

    Article  Google Scholar 

  42. Yang KK, Lam SSW, Low JM, Ong MEH (2016) Managing emergency department crowding through improved triaging and resource allocation. Oper Res Health Care 10:13–22. https://doi.org/10.1016/j.orhc.2016.05.001

    Article  Google Scholar 

  43. Zeinali F, Mahootchi M, Sepehri MM (2015) Resource planning in the emergency departments: a simulation-based metamodeling approach. Simul Model Pract Theory 53:123–138. https://doi.org/10.1016/j.simpat.2015.02.002

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the Strategic Basic Research project Data-driven logistics (S007318N), funded by the Research Foundation Flanders (FWO). This work is supported by the Special Research Fund (BOF) of Hasselt University (BOF20TT03).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Lien Vanbrabant.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix 1: Electronic health record data

See Table 5, 6, 7 and 8.

Table 5 Timestamp attributes in extracted input file of EHRs (T = time)
Table 6 Numerical attributes in extracted input file of EHRs
Table 7 Categorical attributes in extracted input file of EHRs
Table 8 Free text attributes in extracted input file of EHRs

Appendix 2: Validation

See Figs. 12 and 13.

Fig. 12
figure12

Validation boxplot for the LOS of patients in the non-ambulant and ambulant zone

Fig. 13
figure13

Graphical validation of hourly number of patient arrivals

Appendix 3: Statistical analysis

This online appendix provides the results of Mauchly’s test of sphericity and the repeated-measures full factorial ANOVA. For all main effects and 2-way interactions in the ANOVA, the most appropriate F-statistic is determined by the results of Mauchly’s test of sphericity Tables 9 and 18. In case the results of Mauchly’s test provide evidence for the violation of the sphericity assumption at the 5% significance level (p value < 0.05), the G–G estimate of the F-statistic is used in the ANOVA. Otherwise, the sphericity assumed estimate of the F-statistic is used.

Scenario without multitasking effect

See Tables 9, 10, 11, 12, 13, 14, 15, 16 and 17.

Table 9 Mauchly’s test results on sphericity—no multitasking effect
Table 10 \(2\times 2\times 3\times 25\) full factorial repeated-measures ANOVA on DTDT TC2—no multitasking effect
Table 11 \(2\times 2\times 3\times 25\) full factorial repeated-measures ANOVA on DTDT TC3—no multitasking effect
Table 12 \(2\times 2\times 3\times 25\) full factorial repeated-measures ANOVA on DTDT TC4—no multitasking effect
Table 13 \(2\times 2\times 3\times 25\) full factorial repeated-measures ANOVA on DTDT TC5—no multitasking effect
Table 14 \(2\times 2\times 3\times 25\) full factorial repeated-measures ANOVA on LOS TC2—no multitasking effect
Table 15 \(2\times 2\times 3\times 25\) full factorial repeated-measures ANOVA on LOS TC3—no multitasking effect
Table 16 \(2\times 2\times 3\times 25\) full factorial repeated-measures ANOVA on LOS TC4—no multitasking effect
Table 17 \(2\times 2\times 3\times 25\) full factorial repeated-measures ANOVA on LOS TC5—no multitasking effect

Scenario with multitasking effect

See Tables 18, 19, 20, 21, 22, 23, 24, 25 and 26.

Table 18 Mauchly’s test results on sphericity—multitasking effect
Table 19 \(2\times 2\times 3\times 25\) full factorial repeated-measures ANOVA on DTDT TC2—multitasking effect
Table 20 \(2\times 2\times 3\times 25\) full factorial repeated-measures ANOVA on DTDT TC3—multitasking effect
Table 21 \(2\times 2\times 3\times 25\) full factorial repeated-measures ANOVA on DTDT TC4—multitasking effect
Table 22 \(2\times 2\times 3\times 25\) full factorial repeated-measures ANOVA on DTDT TC5—multitasking effect
Table 23 \(2\times 2\times 3\times 25\) full factorial repeated-measures ANOVA on LOS TC2—multitasking effect
Table 24 \(2\times 2\times 3\times 25\) full factorial repeated-measures ANOVA on LOS TC3—multitasking effect
Table 25 \(2\times 2\times 3\times 25\) full factorial repeated-measures ANOVA on LOS TC4—multitasking effect
Table 26 \(2\times 2\times 3\times 25\) full factorial repeated-measures ANOVA on LOS TC5—multitasking effect

Appendix 4: Results scenario without multitasking effect

See Figs. 14, 15, 16, 17 and Tables 27 and 28.

Fig. 14
figure14

Mean DTDT per TC as a function of caseload. Note DTDT at caseload 1 is very high because of the large amount of physician idle time, and will never be used in practice. These values are not presented in the figures for clarity purposes. The DTDT at caseload 1 equals (in minutes): TC2: 1855.96, TC3: 1867.53, TC4: 2893.89, TC5: 8738.38

Fig. 15
figure15

Mean LOS per TC as a function of caseload. Note LOS at caseload 1 is very high because of the large amount of physician idle time, and will never be used in practice. These values are not presented in the figures for clarity purposes. The LOS at caseload 1 equals (in minutes): TC2: 2138.78, TC3: 2133.13, TC4: 3021.15, TC5: 8925.27

Fig. 16
figure16

Caseload range resulting in significant DTDT improvement in comparison with no caseload limit for each priority factor combination (per triage code)—no multitasking effect

Fig. 17
figure17

Caseload range resulting in significant LOS improvement in comparison with no caseload limit for each priority factor combination (per triage code)—no multitasking effect

Table 27 Significant potential KPI improvements under the current queueing disciplines (TC–TC-Equal) with corresponding caseload limit—no Multitasking effect
Table 28 Significant KPI improvements under the current queueing disciplines (TC–TC-Equal) for caseload limit 2—no multitasking effect

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Vanbrabant, L., Braekers, K. & Ramaekers, K. Improving emergency department performance by revising the patient–physician assignment process. Flex Serv Manuf J (2020). https://doi.org/10.1007/s10696-020-09388-2

Download citation

Keywords

  • Discrete-event simulation
  • Emergency department
  • Case managers
  • Real-life case study
  • Healthcare operations