تجزیه و تحلیل های داده کاوی به وسیله بانک اطلاعاتی در تریاژ طب اورژانس در یک بیمارستان تایوانی منطقه ای

“Emergency medicine” is the front line of medical service a hospital provides; also it is the department people seek medical care from immediately after an emergency happens. The statistics by the Department of Health, Executive Yuan, indicate that over years, the number of people at the emergency department has been increasing. The US has introduced and practiced the triage system in the emergency medicine in 1960, whereby to aid the emergency department in allocating the patients, to give them appropriate medical care by the fast decision of the nurses and doctors in case of the patients’ seriousness through their judgment. This study takes on the knowledge contained in the massive data of unknown characteristics in the triage database at a Taiwanese regional hospital, using the cluster analysis and the rough set theory as tools for data mining to extract, with the analysis software ROSE2 (Rough Sets Data Explorer) and through rule induction technique, the imprecise, uncertain and vague information of rules from the massive database, and builds the model that is capable of simplifying massive data while maintaining the accuracy in classifying rules. After analyzing and evaluating the knowledge obtained from relevant mining in the hospitals past medical data for the consumption of emergency medical resources, this thesis proposes suggestions as reference for the hospitals in subsequent elevation of medical quality and decrease in operative costs.

1.1. Background and motivation of the research Emergency department, the front line of a hospital facing urgent patients, consists of doctors, nurses, technicians, social workers, emergency medical technicians, administrative persons, employees and volunteers as members, who maintain a 24-h operation and are able to do anything like first aid, observation in detention or surgical operation, in a way as if of a hospital in hospital (Shi, 2008). According to the 2007 statistics by the Department of Health, Executive Yuan, as shown in Fig. 1, the daily emergency medical services provided by all hospitals in Taiwan increased from 14,405 person-visits in 1997 to 18,392 person-visits in 2007, a significant growth. The statistics by US Center for Disease Control and Prevention also showed an increase in the number of emergency patients from 94.9 million in 1997 to 175 million in 2001 (McCaig & Burt, 2003). These all suggest a trend, worldwide, of continuous increase in visitors to emergency department, which has also kept such environment in hectic condition like in warfare.To avoid the delay in saving the really urgent patients among the numerous visitors to the emergency room, the emergency triage system was established. As such, the US introduced the triage system in emergency medicine in 1960 (Weiner & Edwards, 1964); the US Emergency Nurse Association published the “Standards of Emergency Nursing Practice”, which specifically provides that the emergency nurses should conduct a triage on every patient showing up in the emergency room from the physiological and psychological angles to identify the priority of medical care among patients (Gilboy, Travers, & Wuerz, 1999). Triage is the screening station set up in the emergency medicine; its purpose is chiefly to “place the right person at the right time in the right place to use the right resources” (Chan, 2006). This study investigates the current condition of the emergency patients, extracting by data mining techniques, from the implicit and latent data of emergency patients in the hospital, the trend and data that can serve as reference, and analyzing and understanding the correlation between triage and patient structure and consumption of medical resources. The study, then, evaluates the data obtained from relevant mining to present suggestions for improvement as reference for the hospitals in subsequent elevation of medical quality and decrease in operative costs. It is hoped to serve the basis of reference for the government’s health agencies in deliberation on the human power training and allocation in emergency medicine related units at hospitals when reviewing medical expenses and revising health insurance policies in future. Also, the medical modes and trends obtained by data mining techniques can be stored in the existed database of medical knowledge and will be able to make the management of information and knowledge, which is very useful to the medical institutions. 1.2. Research objectives Taking a regional hospital for example, this study explores the effect of patients’ use of resources by analyzing the data of triage of emergency patients. The study also finds the knowledge of diagnosis by employing the knowledge discovery theory in the field of data mining—rough set theory, RST. Through the application of RST method, data mining is conducted in the historic triage data at a Taiwanese regional hospital to uncover the implicit knowledge in the database, to build the model that can simply massive data while maintaining the accuracy in rules of classification, which serves as the tool for analyzing the original anamnesis data that are massive, vague and full of uncertainty, whereby to analyze the triage data. This study has the following objectives: 1. To use the cluster analysis to classify the triage and modification cases in the triage database to reduce the noises in the classification and, then, to find out the classifying model of routine triage and modification by classification. 2. To analyze the data and to employ the rough set theory to uncover the implicit knowledge in the database, to build the model that can simply massive data while maintaining the accuracy in rules of classification. 3. To analyze the triage database to identify the key attributes of the triage and to summarize the important rules of decision.

This study employed cluster analysis technique as the tool of data mining to examine the emergency triage database at a local hospital in Taiwan. The implicit knowledge with unknown features in huge databases were analyzed by the combination of SOM and K-Means cluster analysis, as cluster analysis has the advantage of the ability to avoid the uncertainty in the analysis of numeric-type data caused by arbitrary definition of classification and clustering criteria, as well as to effectively segment the data with different group characteristics. Then, by rough set theory analysis, the uncertain, vague and rough data could be treated, with every field of data regarded as a symbol when they were being read, the advantage of which is that, unlike conventional statistic analysis, RST analysis does not produce different analytical results from different sizes of data. Also, it was allowed to classify the affecting attributes in core attributes (period, arrival, gender, age, subject and medical expenses) and non-core attributes. This study combined these two techniques to apply in data process and as tool of data mining, and achieved good results. As the results of this study, the patients “with longer overstay at emergency”, “with higher consumption of medical expenses” and “of older average ages”, which were found by two-stage cluster analysis, were in the group with high risks that consumes resources, and they had certain similarities as most of them were patients of level 1 and level 2 triage. In “triage”, the medical expenses also increased with the aggravation of seriousness; in “subject”, patients of internal medicine departments consume most; in “arrival”, most patients arrived in ambulance. Apart from the overstay length at emergency department, the classification of patient diseases is another key factor if it is desired to monitor the emergency patients medical expenses. Finally in this study, the rough set theory was used to find out the attributes of decision rules for comparison with original data. It was found in this study that in “disease classification”, the triage types with high expenses concentrated on rare diseases or severe casualties. Therefore, to control the medical expenses of emergency patients, besides overstay length at emergency, the classification of patient diseases is also one of the key factors.