【摘要】：醫(yī)療保險在人們的醫(yī)療行為中扮演著非常重要的作用,醫(yī)保數(shù)據(jù)記錄了病人疾病、費用、就診時間、就診地點及人口統(tǒng)計學等方面的諸多信息。通過對醫(yī)保數(shù)據(jù)的分析和挖掘,我們可以做很多有意義的研究,如發(fā)現(xiàn)疾病的發(fā)病模式,預測疾病的發(fā)展趨勢,分析病人的就診模式,評估醫(yī)療政策在實際中的效果等等。這一方面可以為醫(yī)療專業(yè)領(lǐng)域的人提供進一步研究的啟示,另一方面可以提示人們在面對疾病時的注意事項,也可以從政策層面,為醫(yī)療管理者提供有借鑒意義的參考。本文針對醫(yī)保數(shù)據(jù),主要從兩方面來進行研究。一個是疾病之間的潛在關(guān)系,通過數(shù)據(jù)中相似病人的發(fā)病歷程,尋找疾病之間的關(guān)聯(lián)性,從而為疾病的預防控制和治療提供指導。這是從疾病的防控方面來進行論述。另一方面,由于該研究使用的數(shù)據(jù)是全樣本數(shù)據(jù),可以對病人在觀察期內(nèi)的所有就診記錄進行分析,從而找到影響病人就診行為的因素。這是從病人就診治療的過程方面來進行論述。傳統(tǒng)的疾病預測主要是集中研究某一種或某幾種疾病將來的預后狀況,而且是寬泛的研究,并不能針對病人自身,進行個性化的預測。另一方面,傳統(tǒng)的研究方法需要進行長期的觀察和實驗,對病人的狀況進行跟蹤和記錄,需要消耗大量的人力和物力成本。本研究提出的CAC方法,將數(shù)據(jù)挖掘中的幾種算法結(jié)合起來,僅僅利用醫(yī)保數(shù)據(jù)中人口統(tǒng)計學和疾病相關(guān)的信息,可以對多病種進行分析,還可為病人進行個性化的預測。本文根據(jù)慢性病人和急性病人的不同特征,分別進行了預測,對急性疾病的預測準確率達到了 71%,對慢性疾病的預測準確率則達到了 82%。這種對多病種個性化的預測方法在文獻中很少出現(xiàn),本文的結(jié)果比僅有的少數(shù)研究都有所提高。針對本研究的預測方法,本文利用測試集數(shù)據(jù)做了案例分析,病人的實際狀況與本研究的預測結(jié)果吻合度非常高。將預測結(jié)果與病人的不同特征相結(jié)合,本研究發(fā)現(xiàn)病人的醫(yī)療費用并沒有反映其病情的真實狀況,由此對醫(yī)療政策給出相關(guān)的建議,并引出第二個研究點。本文這里研究的病人就診行為主要是病人就診時的轉(zhuǎn)診行為。我們國家的醫(yī)療體系是一個龐大而復雜的層級系統(tǒng)。理論上來講,病人對醫(yī)療機構(gòu)的選擇是不受限制的。但是,在實際就醫(yī)的過程中,針對不同的醫(yī)療群體,又存在政策、醫(yī)院和病人自身因素等多方面的影響。在多種因素的綜合作用下,病人會如何選擇,哪些因素對病人的選擇會產(chǎn)生怎樣的影響,這是政府層面、醫(yī)院方面以及病人都非常關(guān)注的問題。本研究化繁為簡,對病人就診時的不同選擇行為進行歸類,對病人的就診行為(本文稱之為廣義轉(zhuǎn)診,簡稱轉(zhuǎn)診)進行了清晰的定義,并以此為基礎,提出細致化的轉(zhuǎn)診模型,考慮到了多次轉(zhuǎn)診的情況,對轉(zhuǎn)診模式進行了全面分析。提出換醫(yī)院和是否再住院的回歸模型,通過對比,分析了不同因素對病人轉(zhuǎn)診模式的影響,發(fā)現(xiàn)了一些非常具有實際指導意義的轉(zhuǎn)診規(guī)律。提出有時間限制的和無時間限制的轉(zhuǎn)診模型,根據(jù)對比的結(jié)果,對相關(guān)政策進行評判,并提出相應建議。由于轉(zhuǎn)診行為極其復雜,加之我國的醫(yī)療體系龐大而獨特,本研究在相關(guān)領(lǐng)域具有一定的啟發(fā)意義,可為以后的研究提供新的思路。
[Abstract]:Medical insurance plays a very important role in people's medical behavior. Medical insurance data record a lot of information about patient's disease, cost, time, place and demography. On the one hand, it can provide enlightenment for people in the medical profession to further study, on the other hand, it can remind people to pay attention when facing diseases, and it can also provide reference for medical managers from the policy level. Reference. In this paper, medical insurance data, mainly from two aspects to study. One is the potential relationship between diseases, through the data similar to the course of disease, to find the relationship between diseases, so as to provide guidance for disease prevention and control and treatment. The data used in this study are full-sample data, which can be used to analyze all patient visits during the observation period to identify the factors affecting patient behavior. This is discussed from the process of patient visits. On the other hand, the traditional research methods need long-term observation and experiments to track and record the patient's condition, which requires a lot of manpower and material costs. Combining with the data of medical insurance, demography and disease-related information can be used to analyze multiple diseases and personalized prediction for patients. According to the different characteristics of chronic patients and acute patients, the prediction accuracy of acute diseases is 71% and chronic diseases are predicted. The accuracy of this method is 82%. This method rarely appears in the literature, and the results of this paper are higher than those of only a few studies. This study found that the patient's medical expenses did not reflect the true state of the patient's illness, thus giving relevant suggestions to the medical policy, and leading to the second research point. A large and complex hierarchical system. Theoretically, patients'choice of medical institutions is unrestricted. However, in the actual process of seeking medical treatment, according to different medical groups, there are policies, hospitals and patients' own factors and other factors. Under the combined effect of a variety of factors, patients will choose which factors. The government, hospitals and patients are all concerned about the impact of patient selection. This study simplifies the complexity of the study, classifies the different choices of patients, and defines the patient's behavior (referred to as general referral, referred to as referral) clearly. A meticulous referral model was developed, and the referral mode was comprehensively analyzed considering the situation of multiple referrals. A regression model was proposed for the change of hospital and re-hospitalization. By comparison, the influence of different factors on the referral mode was analyzed, and some referral rules with practical significance were found. According to the results of the comparison, the relevant policies are judged and corresponding suggestions are put forward. Because of the extremely complex referral behavior and the huge and unique medical system in our country, this study has some inspiration in the relevant fields and can provide new ideas for future research.
【學位授予單位】：中國科學技術(shù)大學
【學位級別】：博士
【學位授予年份】：2017
【分類號】：R197.1;F842.684

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