臺灣博碩士論文加值系統

論文摘要
隨著國民健康保險的實施讓醫療顧客負擔的診療成本急速下降，及快速與便利的媒體傳播訊息等因素，造成目前日益競爭的醫療環境。醫療機構一直思考如何利用有限的醫療資源投資在認同本身經營特色與理念的忠誠醫療顧客上，以維持舊顧客的忠誠度。
醫療服務型態的改變，漸漸典範移轉成以醫療顧客為中心的新經營典範。新經營典範是以醫療顧客的觀點為主，而醫療機構的經營服務是去達成滿足醫療顧客的不同需求，以建立長期的醫療顧客良性互動關係。醫療資源的分配也重新以醫療顧客的需求作為重要的參考依據，讓醫療資源的分配能夠滿足醫療顧客的重要需求。
提昇醫療服務品質及建立優良的醫療口碑，是相當重要的醫療服務課題。對於醫療院所來說就是提供更準確、更高品質的醫療服務；對於醫療顧客來說就是滿足個人化的不同醫療需求。本研究由醫療顧客的需求及滿意度資料出發，利用統計方法找出因子相互關係，以資料探勘的技術分析醫療顧客的不同集群特性，再分類建立決策樹以預測分群結果，根據集群的顧客特性將有限的醫療資源重新組合投入在最需要服務的醫療顧客上。
本研究以某準醫學中心為研究對象，由訪查人員對內科、家醫科、復健科及住院病患進行田野調查，共蒐集300份問卷，利用統計的信度測量樣本具有極高的信度，並利用相關分析有下列重要發現：
(1) 需求重要性面：顧客中所接受的教育程度愈高，所使用的健保掛號次數越多，推薦別人到醫院的意願愈高者，則對本身需求越重要。
(2) 顧客的年齡越大者對於本身需求重要性就會較不迫切。
(3) 滿意度面：顧客中的年齡越大者，所使用的健保掛號次數越多，平均到醫院的交通時間越長，推薦別人到醫院的意願愈高者，則對醫院有較高的滿意度。
(4) 顧客所接受的教育程度越高者對於滿意度較多抱怨。
根據重要性及滿意度兩個不同維度，將顧客分為四個集群包括：重要高滿意高顧客、重要高滿意低顧客、重要低滿意低顧客及重要低滿意高顧客。本研究提供若干建議，如：瞭解重要高滿意低顧客對於縮短醫療的等候時間有明顯的需求，所以建議縮短醫院等候時間讓就醫流程更人性化與合理化。針對重要低滿意高顧客部分，由於本群對於醫療之外的其他相關醫療軟體及硬體設施並不明確，造成顧客並未真正瞭解到準醫學中心提供的服務項目與特色。例如：電動車服務、電話諮詢服務、互助團體服務、先進的醫療設備等。建議主動關心這群醫療顧客的需求，並多做市場行銷活動等等，作為本研究成果的總結。

Abstract
The practice of national health insurance that causes the decreasing in the cost of seeking medical treatment that has to be paid by medical customers, as well as the swift and convenient mass media, all cause many of the current healthcare centers’ competitions. Each healthcare center thinks continually about how to utilize limited medical resources to invest in the most valuable and loyal medical customers.
The changes in the form of medical service gradually use the paradigm shift of medical customer as the center of the new management pattern. The new management pattern is based on the viewpoint of medical customer, and the medical center is to reach the various demands of medical customers, as to build a good long term interaction with the medical customers. The distribution of medical resources also becomes the substantial reference for the demand of medical customer, allowing the distribution of medical resources to satisfy the medical customer’s significant demand.
The certain significant topic of medical treatment is to improve the quality of medical treatment and establish the public praise of medical treatment. Regarding to medical center, this means providing more accurate and better quality of medical treatment. Regarding to medical customer, this means satisfies every different individual’s demands. This research starts from the information of medical customer’s demand and satisfaction, uses statistics to find out the mutual relation factors, and then uses the technology of data mining to discover different medical customer clustering’s property. Afterwards, focuses on different clustering of medical customer, and then rebuilds limited portfolio investing to medical customer, who needs the service mostly.
This research uses a certain medical center as the research’s object. Investigating the department of the internal medicine, pediatrics, recovery and inpatient, collecting an amount of three hundreds questionnaires, using the statistics to make an analytic and then discover the following:
1. Significant demand: the higher education level of a medical customer or the higher the number of register or the higher inclination to recommend the medical center to other people, causing the more significant the demand is.
2. The older a medical customer is, the less pressing is there for the significant demand.
3. The degree of satisfaction: the older a medical customer is or the higher the number of register or the longer average time to reach the medical center or the higher inclination to recommend the medical center to other people, causing the higher satisfaction degree towards the medical center.
4. The higher the education level of a medical customer will cause more complaint towards the medical center.
Based on the significance and the level of satisfaction these two different dimensions, the medical customers are then divided into four clusters: higher demand-higher satisfaction, higher demand-lower satisfaction, lower demand-lower satisfaction, and lower demand-higher satisfaction. This research provides some suggestions: higher demand-lower satisfaction customers with regard to shorten medical waiting time has an obvious demand, and then suggest shortening medical waiting time will cause the flow path of seeking medical advice becomes more humanity and rational. The lower demand-higher satisfaction customers this cluster, against the other medical facilities besides the medical treatment is vague. This result in the improper understanding in seeking the characteristics of the medical advice, for example: electronic vehicle service, call center service, community service, advanced medical facilities, etc. The research suggests to considerate about the demand of this cluster initiatively, also do more market sales activities, etc.

目錄
論文摘要 I
目錄 IV
表次 VI
圖次 IX
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機與問題 3
1.3 研究流程 4
第二章 文獻探討 6
2.1 顧客關係管理 6
2.2 醫療顧客關係管理 9
2.3 醫療顧客忠誠度管理 10
2.3.1 顧客價值 10
2.3.2 醫療顧客價值模型 12
2.3.3 顧客忠誠度 16
2.3.4 醫療顧客的分群分析 17
2.4 推動HCRM的基礎架構 21
2.5 資料探勘的技術 23
2.5.1 分類與決策樹 24
2.5.2 分群 25
2.5.3 區別分析 27
第三章 研究架構與假設 28
3.1 研究架構 28
3.2 研究假設 28
第四章 研究方法 30
4.1 研究設計 30
4.1.1 問卷的設計 30
4.1.2 問卷構面 31
4.2 研究資料蒐集方法 32
4.2.1 抽樣調查蒐集歷程 32
4.2.2 研究蒐集資料收集的方法 33
4.3 問卷資料的信度與效度分析 34
4.3.1 信度分析 34
4.3.2 效度分析 35
4.4 研究使用分析技術 36
第五章 研究結果與討論 37
5.1 人口統計變數與滿意度之關係 37
5.1.1訪查人口統計分析 37
5.1.2 Pearson相關係數檢定 39
5.2 醫院醫療服務與滿意度之關係 41
5.2.1 訪查人口對醫院醫療服務滿意分析 41
5.2.2 Pearson相關係數檢定 42
5.3 人口統計變數與顧客對醫院價值判斷之關係 43
5.3.1 Pearson相關係數檢定 43
5.4 醫院醫療服務與顧客對醫院價值判斷之關係 44
5.5 顧客分群 — 依滿意度 46
5.5.1 各群平均值 47
5.5.2 各群的顧客側寫(profile) 47
5.5.3 滿意度決策樹分類規則 56
5.6 顧客分群 — 依重要性 61
5.6.1 各群平均值 62
5.6.2 各群的顧客側寫(profile) 62
5.6.3 重要性決策樹分類規則 71
5.7 顧客分群 — 依滿意度與重要性 76
5.7.1 重要性與滿意度分群結果 76
5.7.2 各群平均值 77
5.7.3 各群的顧客側寫(profile) 78
5.7.4 重要性與滿意度決策樹分類規則 88
5.7.5 重要性與滿意度各群組的差距 91
第六章 結論與建議 93
6.1 研究結論 93
6.2 醫療院所經營之建議 96
6.3 未來研究 98
參考文獻 100
附錄一：問卷設計 104

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