| Summary: | 博士 === 國立臺灣大學 === 生醫電子與資訊學研究所 === 106 === According to previous research, the most important factor for a patient’s survival is the emergency treatment in the ambulance and the effective allocation of emergency resources when emergency patients experience out-of-hospital cardiac arrest (OHCA) before arriving at the hospital.
The current procedure for deciding which hospital a patient is sent to is followed by the emergency medical dispatch (EMD) sent by the emergency command centre, and they make decisions based on limited information and previous experience. Previous research indicated that the age and race of OHCA patients and the timing and rescue capacity of EMD in addition to the process of being taken to the hospital would impact a patient’s recovery and survival rate. However, very few researchers conducted studies on real-time spatial analysis of emergency medical resource allocation and dispatch based on big data and machine learning.
This study intended to combine the regional information of emergency medical resources and the data base of geographic information for ambulances to assess the best method of medical treatment for OHCA patients, such as the closest medical centre, the level of first-aid capabilities, the proper medical department, the information for hospital beds, and the special needs of the medical department so that the EMD can make decisions based on these suggestions.
To provide a strategy for an emergency ambulance service, this research proposed using big data analysis to examine the impact on the survival rate of OHCA patients based on the following features: the optimized population, level of regional development, allocation of emergency medical resources and the optimal execution time for the chain of survival and advanced cardiac life support. Additionally, through increasing the spatial allocation and flexibility of emergency medical technician-paramedic (EMT-P), it can reflect the importance of spatial factors in the challenge of allocating emergency medical resources. The results of this study can provide recommendations for a policy of medical resource allocation.
We evaluated the cases of OHCA patients between 2010 and 2011 collected by the ambulance commend centre in New Taipei City, and the research results showed patients’ different medical conditions before arriving at the hospital. This study utilized two types of research methods. First, the study used the Utstein style to conduct data extraction and adopted a regression analysis to explore the relationship between these factors and on the OHCA patient’s survival rate, extracting an OHCA patient’s risk factor and calculating the coefficient of risk. Then, we utilized the method of decision trees to establish the OHCA’s risk decision model, adding risk factors with coordinates, and formed the OHCA spatial risk factors. In the second part of the research methods, we combined OHCA spatial risk factors with the regionalized quantity of emergency medical resources and spatial distribution of first-aid capability hospitals as well as the demand for medical resources and formed the spatial matrix to evaluate the demand for medical services. Finally, a policy recommendation for the optimized region and an allocation strategy for emergency medical resources were made based on the matrix.
The object of this research is to study OHCA patients in this context. The research method combined a relaxed variable kernel density estimator with the optimized region and allocation for emergency medical resources so that the results can assist EMD in making the proper medical decision before arrival at the hospital. In addition, EMD can formulate the optimal strategy for ambulance dispatch. The model built in this research can provide a practical and efficient analytic tool for the medical emergency field and its results will provide reliable references for emergency dispatch.
|
|---|
