DISCRETE EVENT SIMULATION TO ANALYZE PATIENT PRIORIZATION IN THE CONTEXT OF AN EMERGENCY DEPARTMENT

Blanco, Bruno Baptista; Pinheiro, Ellen Barreto Alcântara; dos Santos, Igor Leão

doi:10.1590/0101-7438.2023.043.00274435

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ARTICLES • Pesqui. Oper. 44 • 2024 • https://doi.org/10.1590/0101-7438.2023.043.00274435 copy

DISCRETE EVENT SIMULATION TO ANALYZE PATIENT PRIORIZATION IN THE CONTEXT OF AN EMERGENCY DEPARTMENT

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

With the growth of the demand flow and its variability at an emergency department, the complexity to deal with the prioritization of assistance also increases, seeking to offer a quality service. In this sense, several systematized risk triage protocols were created to organize the queues at the emergency, establishing an order of service to ensure that patients do not wait more than a safe time to receive their first medical care. Therefore, the objective of this work is to analyze and evaluate the behavior of an emergency department using a discrete event simulation model. For the results, it was possible to better understand the patient flow, identify problems and difficulties in the system, propose alternatives to deal with patient prioritization, and evaluate the performance of these alternatives through the Goal Question Metric methodology.

Keywords:
emergency department; patient prioritization; discrete event simulation

Components	Factors
Input	Medical/surgical emergencies; urgent visits; non-urgent visits; ambulance arrivals; national “safety net”.
Throughput	Patient acuity; triage and bed placement process; ED bed availability; staffing; diagnostic testing usage; degree of staff training and experience; consultant and ancillary services availability; degree of boarding.
Output	Hospital occupancy; inpatient bed shortages; inpatient team “capping”; staffing ratios; inefficient processes of transferring care to inpatient team; skilled rehabilitation and nursing facility capacity; inefficient inpatient discharge planning; inpatient acuity; need to transfer to higher level of care.

Domain	Effects
Operational metrics	Longer ED length of stay and longer inpatient length of stay.
Patient outcomes and adverse events	Increased mortality and medication errors.
Quality measures	Worse performance on core Centers for Medicare and Medicaid Services measures. An increased time to pain management for long bone fracture, longer time to critical therapies for patients with severe sepsis, delays in assessment and treatment of pain, delays in definitive therapy for acute myocardial infarction, and decreased rates of hand hygiene.
Access to care	Higher left without being seen, leave before treatment completion, and ambulance diversion.
Patient experience	Worse Press Ganey and Health Care Providers and System scores.
Educational experience	Fewer patients and procedures for emergency medicine residents.
Financial health	Significant financial losses for EDs and hospitals.

Priority	Name	Color	Maximum time _[minutes]
5	Immediate	Red	0
4	Very urgent	Orange	10
3	Urgent	Yellow	60
2	Standard	Green	120
1	Non-urgent	Blue	240

Process	Resource	Resource Availability	Distribution	Essential parameters	Parameters (values)
Arrival	-	-	Exponential	β is expected value; interval time between consecutive patient arrivals	β = 2.0
Register	Secretary	2	Weibull	α =scale parameter; δ =shape parameter	α = 2.3847; β = 3.0524
Triage	Nurse	2	Triangular	a=lower limit; b=mode; c=upper limit	TRIA (2,3,4)
Medical care	Physician	3	Gamma	α =shape parameter; β =scale parameter	α = 3.5956; β = 1.7646

Priority rating	Manchester risk classification system
Red	0.92
Orange	15.71
Yellow	38.19
Green	43.69
Blue	1.49

Data collected	Bibliography source
Resources availability; Distributions; Parameters values	Ferreira; Santos; Santos; Reis (2020FERREIRA LAF, SANTOS ILD, SANTOS ACDSGD & REIS ADC. 2020. Discrete event simulation for problem solving in the context of an emergency department. Independent Journal of Management & Production, 11(5): 1515. Available at: http://www.ijmp.jor.br/index.php/ijmp/article/view/1286. http://www.ijmp.jor.br/index.php/ijmp/ar... )
MTS categories and time information Probability distribution by MTS risk classification	Mackway-Jones; Marsden; Windle (2014) Cordeiro; Torres; Rausch (2014CORDEIRO W, TORRES BLDB & RAUSCH MDCP. 2014. Sistema Manchester de classificação de risco: comparando modelos. Belo Horizonte: GBCR.)

Name	Description
Goal 1 (G1)	To analyze the performance of the computer simulation model of the ED to understand the quality of patient care.
Question 1 (Q1)	What is the perceived level of quality of patient care in each scenario?
Question 2 (Q2)	What is the bottleneck resource in the simulation model?
Question 3 (Q3)	How well does the simulation model adhere to the Manchester model?
Question 4 (Q4)	Which scenario has the best quality of patient care?
Metric 1 (M1)	Proportion of patients attended. Calculated for each risk category of the Manchester model, averaged across replications, and for each scenario. It represents the percentage of patients attended in each risk category about the total number of patients in each scenario.
Metric 2 (M2)	Average waiting time for the patient, from arrival at the hospital (the hospital is aware of the patient’s arrival when he/she enters the registration queue), until being seen by a physician. Calculated for each risk category of the Manchester model, averaged across replications, and for each scenario.
Metric 3 (M3)	Maximum waiting time for the patient, from arrival at the hospital (the hospital is aware of the patient’s arrival when he/she enters the registration queue), until being seen by a physician. Calculated for each risk category of the Manchester model, averaged across replications, and for each scenario.
Metric 4 (M4)	Percentage difference (deviation) between M2 and M3.
Metric 5 (M5)	Occupancy rate of each resource (percentage of the total simulation time when the resource is occupied). Calculated on average between replications, and for each scenario.
Metric 6 (M6)	Percentage difference (deviation) between M3 and the maximum care time allowed by the Manchester model.
Metric 7 (M7)	Difference in minutes (deviation) between the M2 values calculated for each scenario.

Resource	Scenario 1	Scenario 2	Scenario 3
Physician 1	96.93%	96.93%	99.18%
Physician 2	96.84%	96.84%	98.67%
Physician 3	96.67%	96.67%	98.54%

	Scenario 1			Scenario 2			Scenario 3
Patient	Number in	Number out	%	Number in	Number out	%	Number in	Number out	%
Red	2.2	2.2	100.00%	2.2	2.2	100.00%	3.33	3	90.09%
Orange	57.3	56.6	98.78%	57.3	56.6	98.78%	60.33	58.33	96.68%
Yellow	139.3	137	98.35%	139.3	124.2	98.16%	135.67	111.67	82.31%
Green	164.7	133.8	81.24%	164.7	144.5	87.74%	145	60.33	41.61%
Blue	4.1	1.9	46.34%	4.1	4	97.56%	3.67	0	0%
Sum	367.6	331.5	90.18%	367.6	331.5	90.18%	347.9967	233.33	67.05%

	Scenario 1					Scenario 2					Scenario 3
Patient	1	2	3	4	5	1	2	3	4	5	1	2	3	4	5
Red	3.15	6.1556	95.42%	0	-	3.15	6.1556	95.42%	0	-	17.291	24.5611	42.05%	0	-
Orange	4.6702	8.0403	72.16%	10	-20%	4.681	8.0403	71.76%	10	-20%	14.8767	17.6317	18.52%	10	760%
Yellow	6.0584	9.3188	53.82%	60	-84%	33.2282	59.145	78.00%	60	-1%	43.4014	47.1579	8.66%	60	-21%
Green	61.5619	112.11	82.11%	120	-7%	46.0615	80.3134	74.36%	120	-33%	137.27	165.8	20.78%	120	38%
Blue	109.52	384	250.62%	240	60%	50.8661	95.9204	88.57%	240	-60%	-	-	-	240	-

Patient	Scenario 1	Scenario 2	Scenario 3	Difference between 2 and 1 _[2-1]	Difference between 3 and 1 _[3-1]	Difference between 2 and 3 _[2-3]
Red	3.15	3.15	17.291	0	14.141	-14.141
Orange	4.6702	4.681	14.8767	0.0108	10.2065	-10.1957
Yellow	6.0584	33.2282	43.4014	27.1698	37.343	-10.1732
Green	61.5619	46.0615	137.27	-15.5004	75.7081	-91.2085
Blue	109.52	50.8661	-	-58.6539	-	-
Average	36.9921	27.59736	53.209775	-9.39474	27.47972	-25.14368

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