Agents
1. Victim casualties requiring emergency medical care
2. Ambulances vehicles to transport the victims ; has three EMT skill level
3. DMAT disaster medical assistance team; DMAT consists of EMS physicians and nurses
4. Field EMS mobile ED unit with care personnel and equipment
5. Hospital includes emergency department and operating rooms of different specialties
6. EMA emergency management agency who is in charge of managing response operation
InjuryType: ( Itype:int * severity: int)
Patient ( ID:int * age:int * Survival:int * numI:int * Injuries:[ ] :Int * symptoms: [ ]:int * Xray:bool * TD:int )
Ambulance (ID:int, type:int, deployed:bool, delay:int, treatmentDelay:int)
1. ID are sequential starting at #1000
2. Age is 1, 2, 3, 1 = young (25%) , 2 = elderly (25%), 3 = adult (50%)
3. Survival is the survival rate (unknown to responders but effected by them)
When injuries are added the survival rate will be reduced.
4. numI is the number of injuries
5. TD is the time to death without treatment in MINUTES
6. Symptoms [ ] is generated for the patient when the injuries[ ] is generated.
7. %percent chance of mis-triage will either leave out an injury or add one by mistake
Casualties [ ]:patient
Coding Phase 1:
Accept as input the number of casualties and then generate their associated pseudo-random Oct-tuples.
Display ()
Final Coding Phase
Check Patient list for deaths based on survival percentage
Patients [list] -> Event_manager function
Event_manager -> deploy the ambulances ( severity =3 if avail EMT3 (best emergency response team), else send EMT 2, else send EMT 1.)
· Severity = 2 first send EMT2 if avail, else send EMT1
· Severity = 1 send EMT1
Ambulance tuple: 21
Id: 1 through 21, 1 EMT3, 8 EMT2, and 12 EMT1
Type 1, 2, 3
Bool deployed
Delay in time steps 5 to 30 (road conditions and distance) + (severity(worst)*5).
Delay to treatment = int(Delay/2)
Once deployed == true we will subtract 1 from the Delay until Delay = 0
When TreatmentDelay = 0, the Survival % is improved by 5*type of ambulance.
Once deployed they are no longer available until the delay is passed then deployed to false.
Ambulance (
1. Patient Profiles
Age category 1 young, 2 adult, 3 elderly
Injury Type (IT): 1 injury, 2 infection, 3 disease, 4 poison
Severity 1, 2 or 3
Number of injuries, 1 through 4 with reduced chances for multiple injuries.
Time to death w/o care in hours
A function of age, model, and severity.
Symptoms id (1 to 12) as a result of
Injury even id
Infection odd id
Disease Prime numbers
Poison multiples of 5
//optional
Likelihood for mistriage: a function of who is doing the triage & symptoms
Low <3%
Medium <10%
High <20%
ER procedures a function of ID and Severity
X-ray required Boolean
Program Outline:
Time Step
Hd(patients) -> Event manager
Event manager will attempt to deploy ambulances where appropriate
Deployed ambulance -> delay, treatment delay, set deployed to true
Ambulances continue to travel if deployed
Id treatment delay =0 they improve chance of survival.
Once delay is zero , the patient is at the hospital and deployed = false.
Table 3: EMS resources in the three locales.
Gangnam-district
Area [km2] 39
Emergency care 4 Level-2 EMC 1 Level-3 ED
Ambulances 20
119-operated 10 (9 stations)
Hospital-operated 10
For the Gangnam-district, we choose a large convention center in the downtown area as an MCI site
and assume there are 80 casualties – 10 black, 20 red, 30 yellow, and 20 green.
As a main performance measure, we define a preventable death ratio, R as follows:
where _P0i__ is the initial survival probability of patient i, while Pi
f is the survival probability of patient i at the moment of care provision. The fraction in the parenthesis is the ratio of the expected number of surviving patients as a result of EMS provision to the expected number of survivors if EMS is immediately provided.
we use three variables related to the pre-hospital phase and
four variables for the hospital phase.
The three pre-hospital phase variables are:
1) number of ambulances dispatched,
2) ratio of level-1 and level-2 Emergency Medical Technicians(EMTs),
3) number of DMATs dispatched.
The number of ambulances is varied at three levels: current level, 150% and 50% of
the current level.
For the ratio of level-1 and level-2 EMTs, we use 4:6, 6:4 and 8:2 in the experiments.
EMTs carry out three functions in the simulation (triage, first-aid, hospital selection), and we assume
level-1 EMTs have a higher probability of success over level-2 EMTs.
A DMAT is a medical assistance team dispatched to a disaster site, and it consists of doctors and nurses. They perform triage at a massive scale and provide treatments to stabilize a patient’s condition. We use two levels in the experiments for DMATs: number of DMATs = 1 or 2.
Table 4: L18 orthogonal array used in the experiments.
Pre-hospital phase factors Hospital phase factors
Experiment
Set
EMT
level-1:level-2
No. of
Ambulances†
No. of
DMATs
ED
capacity
No. of
X-ray rooms†
No. of
EMS physicians†
No. of
ORs
1
4:6
50 %
1
200 %
50 %
50 %
2
2
4:6
100 %
1
250 %
100 %
100 %
3
3
4:6
150 %
1
300 %
150 %
150 %
4