The Emergency Medical Services (EMS) is a medical infrastructure which purpose is to ensure the proper treatment and transportation of emergency patients before arriving hospital. The responsibilities of EMS include field treatment, patient care during transportation, and emergency care prior to admission to the designated facility. The EMS must be granted full access to all the databases of different levels of medical facilities. In addition, candidates for the EMS must understand the communication schemes and collaboration systems between these facilities. How to send the emergency patient to the most appropriate hospital in shortest time is also one of the essential issues. According to the Emergency Medical Services Act of Taiwan, EMTs should transport the patient from emergency site to the appropriate medical care institution in the vicinity. However, the criteria to decide appropriate medical care institution for patient are not very clear. So far, the transportation is often decided by EMTs. Sometimes, incorrect judgment of the traffic condition or hospital facilities, such as number of beds or ability of supporting patients will result in the delay of golden hours of emergency rescue and lead to decrease the EMS quality. The purpose of our research is to help EMTs decide a most appropriate hospital by matching the attributes between patient and hospital. The attributes of patient include triage degree, seriousness degree and vital signs. The attribute of hospitals include the quantity of on-call doctors, special equipment, number of beds, emergency medical liability,...et al. This research will use these attributes to build algorithms and calculate the most appropriate hospital. We started this research from 2005, focusing on the IT assistance systems of EMS records for EMT to use. After that, in this two year (NSC 99-2221-E-038 -009-MY2), we established an emergency decision-making model by using 11 factors of EMS. The AHP (Analytic Hierarchy Process, AHP) was used to calculate the weighting of the factors to complete the model’s objective function. Undertaking the above results, in the new project, we will continue to modify the decision-making model, and use a variety of data mining methods to verify the correctness of arguments, and continue to collect information on hospital emergency resources, to strengthen the integrity of the database, and create a dashboard to present for the decision makers. In the first year, we will use data mining algorithms to evaluate the attributes of the model to see the feasibility of the prediction power on calculating the target hospital. We will use System Simulation Method to verify the weighting of variables. The model will also be applied in another city with different dataset for further evaluation. As we know, it is important for the EMTs to master the distribution of emergency resources. Therefore, in second year, we will build an information dashboard to show information including equipment, man-power, and degree of congestion in real-time for helping EMTs make decisions. We will also use Operation Research methods such as Lagrangean Relaxation method to evaluate the relevance of emergency resources distribution in Taipei City and give suggestions.
|Effective start/end date||8/1/12 → 7/31/13|
- Emergency Medical Service
- Resource Allocation
- Decision Support
- Operation Research
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