Agents
- Victim casualties requiring emergency medical care
- Ambulances vehicles to transport the victims ; has three EMT skill level
- DMAT disaster medical assistance team; DMAT consists of EMS physicians and nurses
- Field EMS mobile ED unit with care personnel and equipment
- Hospital includes emergency department and operating rooms of different specialties
- 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)
- ID are sequential starting at #1000
- Age is 1, 2, 3, 1 = young (25%) , 2 = elderly (25%), 3 = adult (50%)
- Survival is the survival rate (unknown to responders but effected by them)
When injuries are added the survival rate will be reduced.
- numI is the number of injuries
- TD is the time to death without treatment in MINUTES
- Symptoms [ ] is generated for the patient when the injuries[ ] is generated.
- %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 à 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 (
- 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 |