Emergency department resource optimisation for improved performance: a review

Subject Areas : Mathematical Optimization

Kazi Badrul Ahsan ¹ (School of Chemistry, Physics and Mechanical Engineering, Science and Engineering Faculty, Queensland University of Technology, 2 George Street, Brisbane, QLD, 4000, Australia)
M. R. Alam ² (School of Chemistry, Physics and Mechanical Engineering, Science and Engineering Faculty, Queensland University of Technology, 2 George Street, Brisbane, QLD, 4000, Australia)
Doug Gordon Morel ³ (School of Chemistry, Physics and Mechanical Engineering, Science and Engineering Faculty, Queensland University of Technology, 2 George Street, Brisbane, QLD, 4000, Australia)
M. A . Karim ⁴ (School of Chemistry, Physics and Mechanical Engineering, Science and Engineering Faculty, Queensland University of Technology, 2 George Street, Brisbane, QLD, 4000, Australia)

Received: 2020-10-09 Accepted : 2020-10-09 Published : 2020-06-01

Keywords: Simulation, Optimisation, Emergency departments, Patient flow, DES, ABMS,

Abstract :

Emergency departments (EDs) have been becoming increasingly congested due to the combined impacts of growing demand, access block and increased clinical capability of the EDs. This congestion has known to have adverse impacts on the performance of the healthcare services. Attempts to overcome with this challenge have focussed largely on the demand management and the application of system wide process targets such as the “four-hour rule” intended to deal with access blocks. In addition, EDs have introduced various strategies such as “fast tracking”, “enhanced triage” and new models of care such as introducing nurse practitioners aimed at improving throughput. However, most of these practices require additional resources. Some researchers attempted to optimise the resources using various optimisation models to ensure best utilisation of resources to improve patient flow. However, not all modelling approaches are suitable for all situations and there is no critical review of optimisation models used in hospital EDs. The aim of this article is to review various analytical models utilised to optimise ED resources for improved patient flow and highlight benefits and limitations of these models. A range of modelling techniques including agent-based modelling and simulation, discrete-event simulation, queuing models, simulation optimisation and mathematical modelling have been reviewed. The analysis revealed that every modelling approach and optimisation technique has some advantages and disadvantages and their application is also guided by the objectives. The complexity, interrelationships and variability of ED-related variables make the application of standard modelling techniques difficult. However, these models can be used to identify sources of flow obstruction and to identify areas where investments in additional resources are likely to have most benefit.

References:

Abo-Hamad W (2011) An optimisation-based framework for complex business process: healthcare application. Dublin Institute of Technology, Dublin 
Abo-Hamad W, Arisha A (2013) Simulation-based framework to improve patient experience in an emergency department. Eur J Oper Res 224:154–166
Ahalt V, Argon NT, Ziya S, Strickler J, Mehrotra A (2018) Comparison of emergency department crowding scores: a discrete-event simulation approach. Health Care Manag Sci 21(1):144–155
Ahmed MA, Alkhamis TM (2009) Simulation optimization for an emergency department healthcare unit in Kuwait. Eur J Oper Res 198:936–942
Ahsan KB, Karim M, FitzGerald GJ, Morel DG, Burke JA (2019) Development of relationship between triaging of patients and emergency department performance. Procedia Manuf 30:200–207
Ajmi F, Zgaya H, Othman SB, Hammadi S (2019) Agent-based dynamic optimization for managing the workfow of the patient’s pathway. Simul Model Pract Theory 31:101935

Al Owad A, Karim M, Ma L (2014) Integrated lean six sigma approach for patient fow improvement in hospital emergency department. In: Advanced materials research. Trans Tech Publications, Durnten-Zurich, pp 1893–1902
Al Owad A, Samaranayake P, Karim A, Ahsan KB (2018) An integrated lean methodology for improving patient fow in an emergency department—case study of a Saudi Arabian hospital. Prod Plan Control 29:1058–1081
Arkun A, Briggs WM, Patel S, Datillo PA, Bove J, Birkhahn RH (2010) Emergency department crowding: factors infuencing
fow. West J Emerg Med 11(1):10–15
Asplin BR, Magid DJ, Rhodes KV, Solberg LI, Lurie N, Camargo CA Jr (2003) A conceptual model of emergency department
crowding. Ann Emerg Med 42:173–180
Cabrera E, Taboada M, Iglesias ML, Epelde F, Luque E (2011) Optimization of healthcare emergency departments by agent-based simulation. Procedia Comput Sci 4:1880–1889
Cabrera E, Luque E, Taboada M, Epelde F, Iglesias ML (2012a) ABMS optimization for emergency departments. In: Proceedings of the winter simulation conference. Winter simulation conference
Cabrera E, Taboada M, Iglesias ML, Epelde F, Luque E (2012b) Simulation optimization for healthcare emergency departments. Procedia Comput Sci 9:1464–1473
Ceglowski R, Churilov L, Wasserthiel J (2007) Combining data mining and discrete event simulation for a value-added view of a hospital emergency department. J Oper Res Soc 58:246–254
Chen T, Wang C (2016) Multi-objective simulation optimization for medical capacity allocation in emergency department. J Simul 10:50–68
Daldoul D, Nouaouri I, Bouchriha H, Allaoui H (2015) Optimization on human and material resources in emergency department. In: 2015 international conference on industrial engineering and systems management (IESM). IEEE, pp 633–638
De Freitas L, Goodacre S, O’Hara R, Thokala P, Hariharan S (2018) Interventions to improve patient fow in emergency departments: an umbrella review. Emerg Med J. 35(10):626–637
Derlet RW (2002) Overcrowding in emergency departments: increased demand and decreased capacity. Ann Emerg Med 39:430–432
Dickson EW, Singh S, Cheung DS, Wyatt CC, Nugent AS (2009) Application of lean manufacturing techniques in the emergency department. J Emerg Med 37:177–182
Diefenbach ML (2010) Application of operations research techniques to improve efciency in the emergency department
Diefenbach M, Kozan E (2011) Efects of bed confgurations at a hospital emergency department. J Simul 5:44–57
Eskandari H, Riyahifard M, Khosravi S, Geiger CD (2011) Improving the emergency department performance using simulation and MCDM methods. In: Proceedings of the 2011 winter simulation conference (WSC). IEEE, pp 1211–1222
Feng Y-Y, Wu I-C, Chen T-L (2015) Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm. Health Care Manag Sci 20(1):55–75
Fishman J, McLaferty S, Galanter W (2018) Does spatial access to primary care afect emergency department utilization for nonemergent conditions? Health Serv Res 53(1):489–508
FitzGerald G, Jelinek GA, Scott D, Gerdtz MF (2010) Emergency department triage revisited. Emerg Med J 27:86–92
Ghanes K, Jouini O, Jemai Z, Wargon M, Hellmann R, Thomas V, Koole G (2014) A comprehensive simulation modeling of an emergency department: a case study for simulation optimization of stafng levels. In: 2014 winter simulation conference (WSC). IEEE, pp 1421–1432
Hsieh MC, Wang EM, Lee WC, Li LW, Hsieh CY, Tsai W, Wang CP, Huang JL, Liu TC (2018) Application of HFACS, fuzzy TOPSIS, and AHP for identifying important human error factors in emergency departments in Taiwan. Int J Ind Ergon 1(67):171–179
Hu X, Barnes S, Golden B (2018) Applying queueing theory to the study of emergency department operations: a survey and a discussion of comparable simulation studies. Int Trans Oper Res 25(1):7–49
Ibrahim IM et al (2015) A proposed simulation optimization model framework for emergency department problems in public hospital. In: AIP conference proceedings, vol 1. AIP Publishing
Jafari H, Salmasi N (2015) Maximizing the nurses’ preferences in nurse scheduling problem: mathematical modeling and a meta-heuristic algorithm. J Ind Eng Int 11(3):439–458
Joubert E, Espinasse M-A, Nakhla M (2015) Patients fow optimization in ED: an operational research on the impacts of physician triage. In: 2015 international conference on industrial engineering and systems management (IESM). IEEE, pp 602–608
Kuo Y-H, Rado O, Lupia B, Leung JM, Graham CA (2016) Improving the efciency of a hospital emergency department: a simulation study with indirectly imputed service-time distributions. Flex Serv Manuf J 28:120–147
Kwak N, Lee C (1997) A linear goal programming model for human resource allocation in a health-care organization. J Med Syst 21:129–140
Kyriacou DN, Ricketts V, Dyne PL, McCollough MD, Talan DA (1999) A 5-year time study analysis of emergency department patient care efciency. Ann Emerg Med 34:326–335
Lim ME, Nye T, Bowen JM, Hurley J, Goeree R, Tarride J-E (2012) Mathematical modeling: the case of emergency department waiting times. Int J Technol Assess Health Care 28:93–109
Lowthian JA, Curtis AJ, Cameron PA, Stoelwinder JU, Cooke MW, McNeil JJ (2011) Systematic review of trends in emergency department attendances: an Australian perspective. Emerg Med J 28:373–377
McHugh M, Van Dyke K, McClelland M, Moss D (2011) Improving patient fow and reducing emergency department crowding: a guide for hospitals. AHRQ Publication No. 11(12)-0094. Rockville, MD
Memari H, Rahimi S, Gupta B, Sinha K, Debnath N (2016) Towards patient fow optimization in emergency departments using genetic algorithms. In: 2016 IEEE 14th international conference on industrial informatics (INDIN). IEEE, pp 843–850
Mohammadian M, Babaei M, Amin Jarrahi M, Anjomrouz E (2019) Scheduling nurse shifts using goal programming based on nurse preferences: a case study in an emergency department. Int J Eng 32(7):954–963
Moskop JC, Sklar DP, Geiderman JM, Schears RM, Bookman KJ (2009) Emergency department crowding, part 1—concept, causes, and moral consequences. Ann Emerg Med 53:605–611
Oddoye JP, Jones DF, Tamiz M, Schmidt P (2009) Combining simulation and goal programming for healthcare planning in a medical assessment unit. Eur J Oper Res 193:250–261
Omar E-R, Garaix T, Augusto V, Xie X (2015) A stochastic optimization model for shift scheduling in emergency departments. Health Care Manag Sci 18:289–302
Oyedokun T, Donauer A, Stempien J, McKay S (2019) Self-documentary in the emergency department: perspectives on patients recording their own procedures. Can J Emerg Med 21(3):384–390
Peck JS, Kim S-G (2010) Improving patient fow through axiomatic design of hospital emergency departments. CIRP J Manuf Sci Technol 2:255–260
Pines JM, Iyer S, Disbot M, Hollander JE, Shofer FS, Datner EM (2008) The efect of emergency department crowding on patient satisfaction for admitted patients. Acad Emerg Med 15:825–831
Rabbani M, Farshbaf-Geranmayeh A, Yazdanparast R (2018) A simulation optimization approach for integrated resource allocation in an emergency department, pharmacy, and lab. Intell Decis Technol 12(2):187–212
Ramirez-Nafarrate A, Fowler JW, Wu T (2011) Design of centralized ambulance diversion policies using simulation–optimization. In: Proceedings of the 2011 winter simulation conference (WSC). IEEE, pp 1251–1262
Rutman L, Stone K, Reid J, Woodward GAT, Migita R (2015) Improving patient fow using lean methodology: an emergency medicine experience. Curr Treat Options Pediatr 1:359–371
Salvi F, Morichi V, Grilli A, Giorgi R, De Tommaso G, DessiFulgheri P (2007) The elderly in the emergency department: a
critical review of problems and solutions. Intern Emerg Med 2(4):292–301
Selker HP, Grifth JL, Patil S, Long WJ, d’Agostino R (1995) A comparison of performance of mathematical predictive methods for medical diagnosis: identifying acute cardiac ischemia among emergency department patients. J Investig Med 43:468–476
Stefanini A, Aloini D, Benevento E, Dulmin R, Mininno V (2018) Performance analysis in emergency departments: a data-driven approach. Meas Bus Excell 22(2):130–145
Sundaramoorthi D, Chen VC, Rosenberger JM, Kim SB, BuckleyBehan DF (2010) A data-integrated simulation-based optimization for assigning nurses to patient admissions. Health Care Manag Sci 13:210–221
Svirsko AC, Norman BA, Rausch D, Woodring J (2019) Using mathematical modeling to improve the emergency department nursescheduling process. J Emerg Nurs 45(4):425–432
Toloo S, FitzGerald G, Aitken P, Ting J, Tippett V, Chu K (2011) Emergency health services: demand and service delivery models. Monograph 1: Literature review and activity trends. Queensland University of Technology
Uriarte AG, Zúñiga ER, Moris MU, Ng AH (2015) System design and improvement of an emergency department using simulation-based multi-objective optimization. In: Journal of physics: conference series, vol 1. IOP Publishing
Vass H, Szabo ZK (2015) Application of queuing model to patient fow in emergency department. Case study. Procedia Econ Finance 32:479–487
Weng S-J, Cheng B-C, Kwong ST, Wang L-M, Chang C-Y (2011) Simulation optimization for emergency department resources allocation. In: Proceedings of the 2011 winter simulation conference (WSC). IEEE, pp 1231–1238
Wiler JL, Bolandifar E, Grifey RT, Poirier RF, Olsen T (2013) An emergency department patient fow model based on queueing theory principles. Acad Emerg Med 20:939–946
Wong TC, Xu M, Chin KS (2014) A two-stage heuristic approach for nurse scheduling problem: a case study in an emergency department. Comput Oper Res 51:99–110. https://doi.org/10.1016/j.cor.2014.05.018
Yeh J-Y, Lin W-S (2007) Using simulation technique and genetic algorithm to improve the quality care of a hospital emergency department. Expert Syst Appl 32:1073–1083 
Yousefi M, Yousefi M (2019) Human resource allocation in an emergency department. Kybernetes. https://doi.org/10.110
8/K-12-2018-0675
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
Zhang A, Zhu X, Lu Q, Zhang R (2019) Impact of prioritization on the outpatient queuing system in the emergency department with limited medical resources. Symmetry 11(6):796
Zhao Y, Peng Q, Strome T, Weldon E, Zhang M, Chochinov A (2015) Bottleneck detection for improvement of Emergency Department efciency. Bus Process Manag J 21:564–585