醫療機構病患流失之預警-倒傳遞神經網路研究

A Back-Propagation Networks System For Hospital Patient Churn Premonitory

范牧蘭(Fan Mu-Lan)

指導教授 ： 施能仁

亞洲大學/管理學院/經營管理學系碩士班/碩士(2004年)

摘要

醫療產業隨著全民健康保險的實施、同業競爭的壓力、醫療成本的高漲，及快速與便利的媒體訊息傳播等等因素，造成了目前日益競爭的醫療環境。醫療機構一直思考著如何利用有限的醫療資源投資在醫療理念相同的忠誠病患，來維繫既有病患的忠誠度，並能早期發現潛在流失的病患以便及早進行醫療穩固。
本研究是對台中縣太平市安新診所顧客關係管理議題中之顧客流失進行分析討論，利用資料探勘分類技術中之人工類神經網路(Artificial Neural Networks, ANNs)方法建構顧客流失分析模式，期望藉由此一模式為顧客流失預測出相關之影響變數，在顧客流失之前先給予醫療機構警訊，其研究結果如下：
1.以病患性別變數最重要，其中男性的流失情況較女性病患為高。
2.其次為疾病型態，以急性病患流失較多，慢性病患較少。
3.第三為病患身份別，自費病患流失情況較少，健保身份病患流失較高。
4.第四為病患婚姻狀況，以未婚的流失情況較多，已婚較少。
5.第五為病患年齡，其流失情況隨著年齡的增加而減少。
因此，管理者在了解流失特徵後應該確切作出正確的決策，調整醫療經營，進而增強醫院的競爭力。本研究所提出之預警模式確實能有效找出潛在流失病患並具實務應用價值。

關鍵字

基層醫療 ； 顧客關係管理 ； 顧客流失 ； 資料探勘 ； 倒傳遞神經網路

並列摘要

The current increasingly competitive pressure has been created in the health medical industry due to the facts such as the performance of health insurance for the entire people, extremely high medical costs, and fast and quick media propagation. It has been taken under consideration by heath medical institutions how to invest in loyal patients with the same health medical concept by using the limited health medical resource to hold existing patients’ loyalty and prevent from potential patients’ churns。
This research is to discuss and analyze the customers’ churn in the issue of customers relationship management for An-Xin clinic, Tai-Pin City, Taizhong County, by means of Artificial Neural Networks, ANNs to construct the analysis mode of customers churns and hope to forecast relevant influential variables for customers’ churns, giving a premonition to health medical institutes prior to customers’ churns. The result of research is as follows:
1.Sexual variables of patients’ are the most significant, among which males’ churn condition is higher than females.
2.Types of illness are secondary, among which the churns of patients with acute diseases are more than ones with chronic diseases.
3.The third is the status of patients, among which the churns of patients at one’s own expense are less than the ones with the states of healthcare insurance.
4.The fourth is marriage state, among which churns of the unmarried are more than the married.
5.The fifth is the age of patients, among which the older are more
than the young.
Therefore, the management should accurately make policy, adjust the operation of medical treatment and further after learning the churn characteristics. The premonition mode offered by our graduate school can effectively find potential churn patients and possess value of practical applications.

並列關鍵字

Foundational Health Treatment ； Customer Relationship Management ； Customer Churn ； Data Mining ； Back-Propagation Networks

參考文獻

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