Avaliação de desempenho do atendimento em uma emergência hospitalar do Rio de Janeiro: uma abordagem híbrida por meio de Simulação de Eventos Discretos e Análise Envoltória de Dados

Oliveira, Luís Filipe Azevedo de; Peres, Igor Tona; Araujo, Bianca Menezes

Luís Filipe Azevedo de Oliveira

theoretical review

data analysis

writing and final review of the manuscript

Pontifícia Universidade Católica do Rio de Janeiro – PUC-Rio, Departamento de Engenharia Industrial, Rio de Janeiro, RJ, Brasil. E-mail: lfilipeao@gmail.com; igor.peres@puc-rio.br; biancamearaujo@gmail.com

https://orcid.org/0009-0005-8867-1777

Igor Tona Peres

conceptualization

theoretical-methodological approach

theoretical review

data analysis

writing and final review of the manuscript

Pontifícia Universidade Católica do Rio de Janeiro – PUC-Rio, Departamento de Engenharia Industrial, Rio de Janeiro, RJ, Brasil. E-mail: lfilipeao@gmail.com; igor.peres@puc-rio.br; biancamearaujo@gmail.com

https://orcid.org/0000-0001-9863-4690

Bianca Menezes Araujo

Data collection

data analysis

writing and final review of the manuscript

Pontifícia Universidade Católica do Rio de Janeiro – PUC-Rio, Departamento de Engenharia Industrial, Rio de Janeiro, RJ, Brasil. E-mail: lfilipeao@gmail.com; igor.peres@puc-rio.br; biancamearaujo@gmail.com

Authors	Area/Objective	Simulation	DEA
Azadeh et al. (2011)Azadeh, A., Moghaddam, M., Asadzadeh, S. M., & Negahban, A. (2011). An integrated fuzzy simulation-fuzzy data envelopment analysis algorithm for job-shop layout optimization: the case of injection process with ambiguous data. European Journal of Operational Research, 214(3), 768-779. http://doi.org/10.1016/j.ejor.2011.05.015. http://doi.org/10.1016/j.ejor.2011.05.01...	Manufacture of plastic artifacts - Evaluate the performance of different layouts in a new manufacturing plant arrangement	Simulator: Visual SLAM Specifics: Simulator used to model the injection manufacturing process	Model: Non-radial Fuzzy-DEA Orientation: Output Inputs: - Outputs: average machine utilization (desirable output), average wait time (undesirable output) and average system time (undesirable output)
Weng et al. (2011)Weng, S. J., Tsai, B. S., Wang, L. M., Chang, C. Y., & Gotcher, D. (2011). Using simulation and Data Envelopment Analysis in optimal healthcare efficiency allocations. In Proceedings of the 2011 Winter Simulation Conference (WSC) (pp. 1295-1305), Phoenix, AZ, USA. New York: IEEE. http://doi.org/10.1109/WSC.2011.6147850. http://doi.org/10.1109/WSC.2011.6147850...	Emergency Sector - Assess potential bottlenecks, maximize production flows, and identify solutions to reduce patient time in the emergency department (ED) while increasing patient satisfaction	Simulator: Arena Specificities: The model simulated and followed patients in the emergency department throughout their stay in the ED, from presentation to discharge	Model: DEA-VRS Orientation: Input Inputs: doctors, nurses, beds Outputs: number of patients seen (desired output)
van den Bergh et al. (2013)van den Bergh, J. V., De Bruecker, P., Beliën, J., De Boeck, L., & Demeulemeester, E. (2013). A three-stage approach for aircraft line maintenance personnel rostering using MIP, discrete event simulation and DEA. Expert Systems with Applications, 40(7), 2659-2668. http://doi.org/10.1016/j.eswa.2012.11.009. http://doi.org/10.1016/j.eswa.2012.11.00...	Aircraft Line Maintenance - The work focuses on the scheduling of allocated human resources to minimize cost and maximize maintenance service and attendance at an airport	Simulator: Microsoft Visual Studio (C++) Specifics: Real-time flight arrival data for the winter season was used to identify the appropriate distributions	Model No.: CRS Orientation: Input Inputs: Labor costs and allocated labor per shift Outputs: success rate (desired), average number of solutions served (desired) and average delay time (unwanted)
Azadeh et al. (2014)Azadeh, A., Motevali Haghighi, S., & Asadzadeh, S. M. (2014). A novel algorithm for layout optimization of injection process with random demands and sequence dependent setup times. Journal of Manufacturing Systems, 33(2), 287-302. http://doi.org/10.1016/j.jmsy.2013.12.008. http://doi.org/10.1016/j.jmsy.2013.12.00...	Injection Molding Production Line - Reduce bottlenecks in the production line by injection molding	Simulator: Visual SLAM Specifics: The performance of different priority rules for task dispatch was evaluated, comparing them by queue performance indicators	Model: Stochastic-DEA Orientation: Output Inputs:- Outputs: queue size (unwanted), average machine utilization (desired) and cycle time (unwanted)
Dev et al. (2014)Dev, N. K., Shankar, R., & Debnath, R. M. (2014). Supply chain efficiency: a simulation cum DEA approach. International Journal of Advanced Manufacturing Technology, 72(9-12), 1537-1549. http://doi.org/10.1007/s00170-014-5779-6. http://doi.org/10.1007/s00170-014-5779-6...	Supply Chain - Analyze the efficiency of the total supply chain in the context of average supply rate performance, considering the possibility of time delays due to changes in lead time and inventory review period	Simulator: Arena Specifics: Uses to obtain supply time in a model that includes multiple levels and links of a hypothetical supply chain	Model No.: VRS Orientation: Input Inputs: delay due to lead time and inventory review period Outputs: Average Supply Rate
Marlin & Sohn (2016)Marlin, B., & Sohn, H. (2016). Using DEA in conjunction with designs of experiments: an approach to assess simulated futures in the Afghan educational system. Journal of Simulation, 10(4), 272-282. http://doi.org/10.1057/jos.2015.14. http://doi.org/10.1057/jos.2015.14...	National education system - Determine the critical relationships, potential futures, and unforeseen consequences of potential policies regarding primary and secondary education in Afghanistan	Simulator: NOLH Specifics: In the simulation model, entities such as students and teachers are created, which then transit through some part of the system and then are terminated, in which attributes of the entities contain decision rules (containing records about demographic and socioeconomic information), making them analogous to agents	Model No.: CRS Orientation: Input Inputs: student problem-solving ability, teacher-solving ability, teacher training capacity, community-based education capacity; Student Demand and Required Funds Outputs: dropout, literate, graduates, parity between provinces and quality of education
Hosseini et al. (2019)Hosseini, Z., Navazi, F., Siadat, A., Memari, P., & Tavakkoli-Moghaddam, R. (2019). A tailored fuzzy simulation integrated with a fuzzy DEA method for a resilient facility layout problem: a case study of a refrigerator injection process. IFAC-PapersOnLine, 52(13), 541-546. http://doi.org/10.1016/j.ifacol.2019.11.214. http://doi.org/10.1016/j.ifacol.2019.11....	Refrigerator injection process of a home appliance company - Improve the resiliency performance of the production line	Simulator: Visual SLAM Specifics: 24 different scenarios were generated to be evaluated in the simulation in terms of queue time and utilization rate	Model No.: Fuzzy DEA Orientation: Output Inputs: - Outputs: Wait time (unwanted), average utilization of flexible devices (desired), average utilization of other devices (desired), and average fault tolerance time (unwanted)
Keshtkar et al. (2020)Keshtkar, L., Rashwan, W., Abo-Hamad, W., & Arisha, A. (2020). A hybrid system dynamics, discrete event simulation and data envelopment analysis to investigate boarding patients in acute hospitals. Operations Research for Health Care, 26, 100266. http://doi.org/10.1016/j.orhc.2020.100266. http://doi.org/10.1016/j.orhc.2020.10026...	Hospitalization of patients in emergencies - Investigate the impact of the inpatient problem on patients' ability to access other units in the hospital	Simulator: AnyLogic Specificities: the integration between DES and DS makes it possible to monitor the patient flow at the micro and macro level	Model No.: VRS Orientation: Output Inputs: number of porters, number of stretchers for transport and number of consultants Outputs: length of stay (unwanted) and patients seen (desired)
Navazi et al. (2019) Navazi, F., Tavakkoli-Moghaddam, R., & Memari, P. (2019). Layout optimization of injection process by considering integrated resilience engineering: a fuzzy-DEA approach. International Journal of Modelling and Simulation, 41(1), 52–66. https://doi.org/10.1080/02286203.2019.1670325. https://doi.org/10.1080/02286203.2019.16...	Refrigerator injection process of a home appliance company - Evaluate the layout of a high-volume production line of single-queue installations and multiple products that requires many downtimes per setup	Simulator: Visual SLAM Specifics: To improve the facility layout of the injection modelling process, this study investigates the performance of each layout against the three factors of Resilience Engineering, which includes flexibility, teamwork, and fault tolerance	Model: Fuzzy DEA-VRS Orientation: Output Inputs: - Outputs: Average Wait Time (Unwanted), Average Device Utilization (Desired), Average Flexible Device Utilization (Desired), and Average Fault Tolerance Time (Unwanted)
Monazzam et al. (2022)Monazzam, N., Alinezhad, A., & Adibi, M. A. (2022). Simulation-based optimization using DEA and DOE in production systems. Scientia Iranica, 29(6), 3470-3488. http://doi.org/10.24200/sci.2021.55499.4253. http://doi.org/10.24200/sci.2021.55499.4...	Manufacturing - Increase efficiency and determine methods to evaluate and optimize the performance of different parts of production systems	Simulator: Enterprise Dynamics Specificities: It models different sectors of the production system and integrates them with results on productivity	Model: Ratio Efficiency Dominant Orientation: - Inputs: production planning, non-mechanized inventories, and assembly shop cycle time Outputs: total production profit, production volume, average productivity of production halls, number of trucks transporting the body and number of semi-finished products during the process
Tavakoli et al. (2022)Tavakoli, M., Tavakkoli-Moghaddam, R., Mesbahi, R., Ghanavati-Nejad, M., & Tajally, A. (2022). Simulation of the COVID-19 patient flow and investigation of the future patient arrival using a time-series prediction model: a real-case study. Medical & Biological Engineering & Computing, 60(4), 969-990. http://doi.org/10.1007/s11517-022-02525-z. PMid:35152366. http://doi.org/10.1007/s11517-022-02525-...	Health (COVID-19) - Propose a decision support system for hospital managers, who are willing to be more efficient and who rely on data to support decision-making	Simulator: Arena Specifics: Simulate the process of patient flow and then predict the future entry of patients into a hospital as a case study and suggest some policies based on different likely scenarios	Model No.: CRS Orientation: Input Inputs: number of doctors on the COVID-19 emergency hotline, number of nurses on the COVID-19 emergency hotline, number of doctors in the ICU, number of nurses in the ICU, number of doctors in the ICU, number of nurses in the ICU, number of radiologists in a CT scan unit, Number of service providers in the laboratory, number of beds in the ICU, number of beds in the ICU and number of beds in the special COVID-19 emergency line Outputs: total time, average patient waiting time, rate of patients discharged, and cost of inputs
Taleb et al. (2023)Taleb, M., Khalid, R., Ramli, R., & Nawawi, M. K. M. (2023). An integrated approach of discrete event simulation and a non-radial super efficiency data envelopment analysis for performance evaluation of an emergency department. Expert Systems with Applications, 220, 119653. http://doi.org/10.1016/j.eswa.2023.119653. http://doi.org/10.1016/j.eswa.2023.11965...	Emergency Sector - Investigate the impact of the allocation of human resources (receptionists, nurses, and doctors) in terms of the queue performance indicators	Simulator: Arena Specificities: Performs the simulation from arrival at the emergency room until discharge	Model No.: Super Efficiency SBM-VRS Guideline: Not applicable Inputs: number of receptionists, number of nurses and number of doctors Outputs: average length of stay in the emergency room (unwanted) and average length of stay in queues (unwanted)

Variable	Description	% Missing Data
a	Timestamp of the withdrawal of the password at the totem	16.86%
b	Timestamp of service at the front desk	0.00%
c	Timestamp of service in triage	52.36
d	Registration of the date and time of the appointment at the doctor's appointment	16.36%
e	Duration of care at triage (in minutes)	55.07%
f	Duration of consultation with a doctor (in minutes)	16.46%

Station	Function	T1	T2
Totem	---	2	1
Reception	Receptionist	4	3
Triage	Nurse	1	1
Consultation	Doctor	6 (Mon and Tue)	3 (Mon and Tue)
Consultation	Doctor	5 (Wed to Sun)	2 (Wed to Sun)

Month	Total	Group	Month	Total	Group
Feb/22	3,380	Group 1	Dec/22	5,072	Group 1
Mar/22	4,856	Group 1	Jan/23	5,554	Group 2
Apr/22	4,967	Group 1	Feb/23	5,530	Group 2
May/22	6,683	Group 3	Mar/23	6,920	Group 3
Jun/22	6,717	Group 3	Apr/23	6,334	Group 2
Jul/22	5,072	Group 1	May/23	6,729	Group 3
Augo/22	5,280	Group 2	Jun/23	5,901	Group 2
Sep/22	5,026	Group 1	Jul/23	5,517	Group 2
Oct/22	5,705	Group 2	Aug/23	-	-
Nov/22	6,656	Group 3	Sep/23	6,622	Group 3

Day week	Interval (α = 5%)	Average
Monday	(229.61; 249.59)	239.60
Tuesday	(205.68; 225.15)	215.41
Wednesday	(194.09; 213.67)	203.88
Thursday	(185.73; 203.39)	194.56
Friday	(170.76; 187.60)	179.18
Saturday	(136.16; 149.82)	142.99
Sunday	(138.49; 152.82)	145.65

Shift	Average length of stay at reception	Average service time at triage	Average service time at emergency consultation
T1	13.90 (13.83; 13.97)	3.81 (3.76; 3.85)	11.38 (11.32; 11.44)
T2	10.35 (10.23; 10.47)	4.20 (4.12; 4.29)	10.99 (10.89; 11.10)
Test U	1,016,617,025	12,716,506	575,068,112
P-value	< 0.05	< 0.05	< 0.05

Stage	Guy	Specification	Expression (unit)
Arrival	Create	Schedule	Poisson (^{Appendix 1} Appendix 1 Average number of customers arriving per hour on each day of the week according to the group of months. Group 1 Group 2 Group 3 Timetable Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun 0am-1am 0.71 0.84 0.84 1.11 1.00 0.85 1.00 1.50 1.03 0.86 1.31 0.97 1.00 0.78 1.81 1.74 1.79 1.50 1.54 1.44 1.44 1am-2am 0.13 0.28 0.24 0.11 0.32 0.37 0.38 0.34 0.30 0.38 0.10 0.21 0.58 0.66 0.62 0.41 0.54 0.35 0.81 0.80 0.40 2pm-3pm 0.08 0.12 0.16 0.11 0.14 0.04 0.13 0.19 0.13 0.21 0.17 0.07 0.19 0.16 0.19 0.48 0.25 0.38 0.42 0.36 0.60 3am-4am 0.13 0.16 0.04 0.07 0.07 0.04 0.13 0.13 0.07 0.21 0.21 0.03 0.16 0.16 0.27 0.26 0.07 0.31 0.12 0.24 0.16 4am-5am 0.00 0.04 0.04 0.07 0.04 0.04 0.13 0.06 0.20 0.21 0.28 0.10 0.13 0.06 0.23 0.15 0.14 0.31 0.27 0.36 0.36 5am-6am 0.21 0.28 0.16 0.11 0.25 0.00 0.25 0.31 0.63 0.31 0.52 0.38 0.32 0.03 0.88 0.48 0.86 0.85 0.92 0.32 0.60 6am-7am 2.21 1.72 1.32 1.44 1.75 0.81 0.83 2.91 2.50 2.52 1.48 1.79 1.16 1.16 2.69 2.93 2.93 2.65 3.19 1.72 1.72 7am-8am 7.92 6.96 6.00 6.52 6.36 3.59 3.63 8.59 9.77 8.31 7.66 6.90 3.74 3.47 11.65 11.48 11.07 9.58 9.23 5.80 5.12 8am-9am 13.46 11.96 10.04 11.48 9.54 6.22 6.67 14.59 14.00 14.41 12.59 10.62 7.74 6.78 21.38 16.70 17.46 15.73 14.88 9.52 9.20 9am-10am 16.79 14.16 12.64 13.96 12.46 8.56 9.42 20.53 17.73 15.52 16.55 14.83 10.39 9.78 25.12 21.11 20.11 19.88 16.77 14.00 13.32 10am-11am 21.04 17.56 14.52 14.89 13.96 11.11 11.29 20.53 21.00 20.24 18.17 16.83 13.77 13.72 26.04 21.00 22.68 21.77 21.31 16.36 16.48 11am-12pm 15.17 14.84 14.64 14.96 11.57 12.19 11.88 20.69 18.53 18.24 19.03 15.48 13.26 14.16 23.27 19.44 18.54 19.69 18.31 16.76 16.36 12pm-1pm 15.17 12.76 12.48 12.89 10.43 10.11 9.21 17.59 15.37 15.62 14.03 12.14 12.42 11.53 18.42 16.19 16.64 16.62 14.69 14.40 13.64 1pm-2pm 16.25 12.76 11.24 14.00 11.25 8.85 8.00 17.69 17.00 14.86 13.38 13.03 10.32 9.81 19.00 17.74 18.04 16.54 16.58 11.84 12.24 2pm-3pm 15.17 13.32 13.12 12.89 10.79 9.33 8.33 17.69 15.67 16.10 15.24 13.17 10.32 10.50 18.12 18.52 17.82 15.38 16.15 12.32 11.72 3pm-4pm 13.83 12.88 10.80 10.59 10.82 8.04 8.42 16.16 14.47 13.90 13.10 12.14 9.81 10.91 16.92 16.15 15.64 12.15 14.85 12.08 13.08 4pm-5pm 12.50 11.08 10.04 10.26 8.86 8.67 7.38 15.03 13.37 12.59 11.93 10.17 9.19 9.00 16.58 12.93 12.46 11.58 12.50 11.28 10.28 5pm-6pm 11.63 11.60 9.36 7.96 7.64 6.74 6.96 13.13 12.70 10.41 11.34 9.48 8.23 8.25 13.96 14.37 10.50 10.73 10.73 9.28 9.28 6pm-7pm 11.71 9.84 8.96 8.19 8.00 5.70 5.67 13.19 11.90 11.34 9.55 9.14 6.39 7.56 15.04 13.85 12.21 11.31 11.15 8.36 8.08 7pm-8pm 10.42 8.80 9.20 8.85 6.57 5.19 6.75 11.81 10.63 10.14 9.41 8.62 6.58 7.59 14.38 14.07 12.86 9.88 10.69 6.88 9.04 8pm-9pm 8.38 7.52 6.92 7.56 7.75 4.96 5.38 10.16 9.37 8.38 8.62 7.45 4.74 5.47 10.92 10.78 8.86 8.50 8.31 6.52 7.48 9pm-10pm 5.96 4.92 5.72 4.67 4.50 3.00 4.83 6.72 6.00 6.14 5.93 5.21 4.10 5.41 8.50 6.48 7.11 6.54 6.73 6.16 5.84 10pm-11pm 4.25 3.60 3.00 3.63 3.25 2.89 2.83 4.56 3.83 4.03 4.10 3.24 3.65 3.34 5.42 5.11 4.89 4.04 4.38 3.16 3.72 11pm-0am 2.29 1.60 2.24 2.07 1.93 1.85 2.13 2.75 2.30 2.41 2.28 2.28 2.19 2.06 3.15 2.78 2.68 2.73 2.35 2.00 2.84 ) \| hour
Queue Reception	Seize	Based on Schedule	T1: 4
Queue Reception	Seize	Based on Schedule	T2: 3
Reception Shift	Decide	2-way by condition	CalHour(TNOW) >= 7 && CalHour(TNOW) <19
T1 Reception	Process	Delay Release	EXPO(11.52) \| minute
T2 Reception	Process	Delay Release	EXPO(10.29) \| minute
Leave Reception	Leave	Route	Enter Triage
Enter Triage	Enter	-	-
Queue Triage	Seize	Fixed Capacity	1
Shift Triage	Decide	2-way by condition	CalHour(TNOW) >= 7 && CalHour(TNOW) <19
T1 Triage	Process	Delay Release	1.5 + LOGN(1.86, 1.32) \| minute
T2 Triage	Process	Delay Release	1.5 + EXPO(1.7) \| minute
Leave Reception	Leave	Route	Enter Medical
Enter Medical	Enter	-	-
Queue Medical	Seize	Based on Schedule	T1: 6 (2f e 3f), 5 (4f a dom)
Queue Medical	Seize	Based on Schedule	T2: 3 (2f e 3f), 2 (4f a dom)
Medical Appointment	Decide	2-way by condition	CalHour(TNOW) >= 7 && CalHour(TNOW) <19
Medical Care T1	Process	Delay Release	2.5 + GAMM(5.78, 1.29) \| minute
T2 Medical Care	Process	Delay Release	4.5 + EXPO(6.49) \| minute
Denouement	Has	-	-

Group	Stage	Queue time (in minutes)	Number of customers in the queue	Average resource utilization
1	Reception	2.16 (1.95; 2.37)	0.24 (0.22; 0.26)	32.5%
	Triage	4.63 (4.45; 4.81)	0.51 (0.49; 0.53)	36.6%
	Consultation	0.36 (0.3; 0.42)	0.04 (0.03; 0.05)	26.3%
2	Reception	5.08 (4.78; 5.38)	0.66 (0.62; 0.7)	38.9%
	Triage	9.91 (9.6; 10.22)	1.29 (1.24; 1.34)	43.5%
	Consultation	0.6 (0.52; 0.68)	0.08 (0.07; 0.09)	31.2%
3	Reception	12.13 (10.76; 13.5)	1.85 (1.64; 2.06)	45.6%
	Triage	19.37 (18.32; 20.42)	2.95 (2.78; 3.12)	50.7%
	Consultation	1.85 (1.56; 2.14)	0.28 (0.23; 0.33)	37.7%

Scenario		Current	5%	10%	15%
Average attendances per day		218.77	230.03	243.20	257.26
Average length of stay in the emergency department (minutes)		58.12	68.10	84.05	104.58
Lead Time	Reception	12.13	16.56	24.42	37.42
	Triage	19.37	24.53	31.23	37.00
	Consultation	1.85	2.25	3.65	5.38
Queue size	Reception	1.85	2.66	4.14	6.71
	Triage	2.95	3.93	5.29	6.63
	Consultation	0.28	0.36	0.62	0.96
Average occupancy	Reception	45.57%	47.99%	50.75%	54.07%
	Triage	50.68%	53.21%	56.25%	59.41%
	Consultation	37.65%	39.79%	42.86%	46.32%

Scenario	Mondays and Tuesdays		Wednesday to Sunday		MH
Scenario	T1	T2	T1	T2	MH
C0	12h: 6	12h: 3	12h: 5	12h: 2	636
C1	1h: 4	5h: 3	1h: 3	1h: 4	628
(23f1 + 4d)	1h: 5	6h: 2	1h: 4	1h: 3
	2h: 7	1h: 4	10h: 5	10h: 2
	3h: 8
	3h: 6
	2h: 4
C2	1h: 4	1h: 5	1h: 3	1h: 4	636
(23f2 + 4d)	1h: 6	1h: 3	1h: 4	1h: 3
	4h: 7	4h: 2	10h: 5	10h: 2
	6h: 6	6h: 3

Reception	T1	T2	MH
R0	4	3	588
R1	5	2	588
R2	6	2	672
R3	5	3	756

Triage	T1	T2	MH
T0	1	1	168
T1	2	1	252
T2	3	2	420
T3	4	2	504

Scn	Rec	Tri	Med	LoS	Eff	Scn	Rec	Tri	With	LoS	Eff
0	R0	T0	C0	58.12	57.5%	24	R1	T1	C1	44.71	82.4%
1	R1	T0	C0	76.57	0.5%	25	R1	T2	C1	44.38	82.4%
2	R2	T0	C0	75.70	3.2%	26	R1	T3	C1	44.64	81.4%
3	R3	T0	C0	53.15	72.9%	27	R2	T1	C1	41.29	81.1%
4	R0	T1	C0	39.25	96.3%	28	R2	T2	C1	39.58	83.7%
5	R0	T2	C0	38.72	96.8%	29	R2	T3	C1	41.38	79.6%
6	R0	T3	C0	38.71	96.4%	30	R3	T1	C1	29.28	99.0%
7	R1	T1	C0	43.08	86.5%	31	R3	T2	C1	29.21	97.2%
8	R1	T2	C0	41.90	88.7%	32	R3	T3	C1	28.90	97.8%
9	R1	T3	C0	41.02	90.5%	33	R1	T0	C2	76.72	0.0%
10	R2	T1	C0	39.40	85.4%	34	R2	T0	C2	73.93	8.6%
11	R2	T2	C0	38.93	85.2%	35	R3	T0	C2	51.68	77.4%
12	R2	T3	C0	37.51	88.1%	36	R0	T1	C2	37.82	100%
13	R3	T1	C0	29.69	96.9%	37	R0	T2	C2	37.45	100%
14	R3	T2	C0	29.54	95.3%	38	R0	T3	C2	37.28	100%
15	R3	T3	C0	29.37	95.6%	39	R1	T1	C2	42.34	88.4%
16	R0	T0	C1	56.25	63.3%	40	R1	T2	C2	39.95	93.6%
17	R0	T0	C2	56.48	62.6%	41	R1	T3	C2	42.47	86.8%
18	R1	T0	C1	76.16	1.7%	42	R2	T1	C2	38.50	87.4%
19	R2	T0	C1	75.13	4.9%	43	R2	T2	C2	38.00	87.2%
20	R3	T0	C1	52.31	75.5%	44	R2	T3	C2	38.30	86.4%
21	R0	T1	C1	39.04	97.0%	45	R3	T1	C2	28.19	100%
22	R0	T2	C1	38.39	97.7%	46	R3	T2	C2	27.23	100%
23	R0	T3	C1	38.02	98.2%	47	R3	T3	C2	27.19	100%

Scenario	Reception	Triage	Consultation
Current (0)	45.60%	50.70%	37.70%
36	45.49%	29.78%	27.69%
37	45.51%	18.48%	27.66%
38	45.48%	15.03%	27.68%
45	33.87%	29.79%	27.79%
46	33.63%	18.47%	27.70%
47	33.67%	14.96%	27.70%

Sensitivity	LoS (in minutes)	Total attended	Occupation reception	Occupation triage	Occupation consultation
Current (0)	52.12	118,137	45.60%	50.70%	37.70%
Scenario 47	27.19	118,143	33.67%	14.96%	27.7%
Scenario 47 with 15%	31.61	138,948	39.81%	17.59%	32.4%
Scenario 47 with 30%	48.09	168,717	48.44%	21.39%	39.0%

Brasil

Brasil

Avaliação de desempenho do atendimento em uma emergência hospitalar do Rio de Janeiro: uma abordagem híbrida por meio de Simulação de Eventos Discretos e Análise Envoltória de Dados

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