Conferencia UAB llevada a cabo en el marco del Acto de inauguración del curso académico 2012-2013 celebrado el 25 de septiembre de 2012

1. Emilio Luque
Computer Architecture & Operating Systems Department
University Autonoma of Barcelona (UAB)

2. Patients must be
addressed with the best
Emergency Departments (ED) quality.
are complex and
quite dynamic systems.
ED’s are overcrowded and work
with limited budget.

3. Simulation:
Optimization: What if?
The best
solution for?

4. Supported by the MICINN Spain, under contract
TIN2007-64974 and
the MINECO (MICINN) Spain, under contract Emilio Luque
TIN2011-24384 CAOS – HPC4EAS
Manel Taboada
GIMBERNAT
Eduardo Cabrera
CAOS – HPC4EAS
Francisco Epelde
PARC TAULÍ
Ma. Luisa Iglesias
PARC TAULÍ

5. Optimization
Simulation

12. STATE Variables Values Observability
Name/identifier <id> Unique per agent I
Gender, Medical history (cardiology, pulmonology,
neurological,…); Allergies (yes-no);
Personal details Treatments that received (classified into therapeutic groups: I
bronchodilators, vasodilators, etc.);
Origin (national or immigrant)
Entrance, Admissions, Waiting Room, Triage, Treatment
Location E
Zone.
Idle, Requesting information from <id>, Giving information
Action to <id>, Searching, Moving to <location> , Waiting for E
ambulance.
Healthy; Hemodynamic-Constant; Barthel Index (degree of
Physical condition
Variables Values E/I/N
Observability
dependence).
Healthy, Cardiac/respiratory arrest, severe/moderate
Symptoms (patients) E/I
trauma, headache, vomiting, diarrhea
Communication skills Low, Medium, High E
Level of experience Resident (1 to 5); Junior (5-10); Senior (10 - 15) and E/I
Current state Next state / (doctors) Consultant (over 15 years)
Input
/ Output Output
…. …. …. Level of experience
E/I
(triage Low, Medium, High
Sx / Ox Ia (p1) Sy / Oy nurses)
Level of experience E/I
Sx / Ox Ia (p2) Sz / Oz (emergency nurses)
Low, Medium, High
Level of experience E/I
Sx / Ox Ia (p3) Sx / Ox (admissions)
Low, Medium, High
…. …. ….

13. 1) Active Agents
2) Passive Agents
Patients
Information system
Companions of patients
Admission personnel Loudspeaker system
Sanitarian technicians Pneumatic pipes
Nurses (Triage, Emergency) Tests services
Doctors (Emergency,
Specialists)
1 to Zone: individuals in Zone
1 to 1(One-to-One) 1 to n (Multicast)
(Area- Restricted Broadcast)

14. The Environment
Arrival/dismissal
by ambulance
Arrival/dismissal
by own means
The model should include the spatial topographical design from the ED

15. ED
functionality
Agents
interactions
Agents

16. Arrival/dismissalb
y ambulance
Arrival/dismissal
by own means
 A
 N
 D

17. ED Simulator
Input Patients arrival:
 Could arrive every 3 min. , but with different probabilities:
20% (4 pat/hr), 40% (9 pat/hr),
60% (13 pat/hr) , 80% (17 pat/hr)
Configuration of the ED Staff
Staff Number Junior Senior
Admission 1-2 2 min. 1 min. 15 sec.
Triage Nurse 1-3 8 min. 5 min.
Doctor 1-4 20 min. 15 min.
Output Patients:
 How many arrive to the service
 How many leave the service
 Times of staying in each area
What if?

19. • Find the best/optimum solution from all the
possible solutions.
 Given any objective (index) function f :
f :A
max / min f x
subject to x A
A constraintset; xo A
f xo f x f xo f x
Maximize minimize
xo A

20. Is it always the "best solution" (the
optimum) the most interesting for us?

22. Methodology
 Simulator: 2nd version

 Parameter configuration: A, N, D = > 3D + P => 4D
 A
 N
 D
 ~ 400 patients daily

23. Methodology: Computational complexity
Multidimensional Discrete
DD
D • Search space
– # Dimensions = Patients,
B
staff (D, N, A, …), T, B,
P
PP …
NN
N N
– Each dimension=>
range of possible
A
A
AA T values
– # Points = #
simulations
(indexes)(time)
COMBINATORIAL!

24. ABM SIMULATOR PARAMETERS
DSS I
N
D
E
+ X
constraints

25. Quality Index:
Minimize patient “Length of Stay” (LoS)
 Constraint: Cost <= 3500 €
20% (4 pat/hr) 14 D, 9 N, 9 A = 1,134 cases
Patient 40% (9 pat/hr)
Arrival 60% (13 pat/hr) 1,134 cases * 4 = 4,536 cases
80% (17 pat/hr) 25,000 ticks => 1 day
 4,536 total cases => 2,408 cases under limit
Staff Time (ticks) Quantity Cost (€)
Senior Junior Senior Junior
Doctors 260 350 1- 4 1000 500
Nurses 90 130 1- 3 500 350
Admission personnel 20 35 1- 3 200 150

26. 20% (4 pat/hr)
Cost constraint <= 3500 €
Patient 40% (9 pat/hr)
Arrival 60% (13 pat/hr)
 Average patient “LoS” 80% (17 pat/hr)
4 p/hr
9 p/hr
Time € # D N A
(ticks) Staff
428 3,200 5 2S 2S 1S
Optimum 428 2,900 5 2S 1S 2S
428 2,850 5 2S 1S 1 S, 1 J
13 p/hr 17 p/hr

27. Cost constraint <= 3500 €
 Average patient “LoS”
4 p/hr 9 p/hr
Time € # Staff D N A
(ticks)
3,266 3,350 7 1 S, 3 J 2J 1J
13 p/hr
17 p/hr
Optimum

28. Cost constraint <= 3500 €
 Average patient “LoS”
4 p/hr 9 p/hr
13 p/hr 17 p/hr

29. Cost constraint <= 3500 €
 Average patient “LoS”
4 p/hr 9 p/hr
Optimal
vs
Suboptimal
13 p/hr 17 p/hr