顧客關係管理(CRM)在國內外已有不少應用實例，但在醫療服務業鮮少被研究過，本研究嚐試將資料採礦的三大核心技術：資料庫管理、Domain知識與資料採礦技術三者予以整合，針對一個獨立經營主體（聯合診所），從行銷策略制定、營運策略描述與執行到經由資料採礦得到具體結果，重新檢討行銷策略之STP定位與導引未來經營策略，並提出一對一行銷的診所病患管理架構。 本研究以一個新成立的診所，取其開業之初(89年12月)至92年1月底止，所累積九千三百多位患者的5萬多筆門診就醫記錄進行資料採礦分析希望研究以下幾個問題： 1.哪些病患帶來最大利潤?為甚麼?哪些患者容易流失?為甚麼? 2.哪些交叉服務對何種患者適合?哪些服務對增加慢性病患者有幫助?糖尿病患者接受視網膜檢查的可能原因為何?婦產科門診所增加的病患,是否會同時接受診所內其他科的服務?是否應該繼續擴大其他專科? 3.診所病患主要的居住地區如何描述？ 研究結果顯示較高獲利組與高醫療費用,高忠誠度,高就診次數,高藥費比率,高慢性病費用比率有關，以疾病別來看，集中在慢性疾病患者身上。顯然經營策略上的意涵是如何爭取慢性病人的高度滿意及信賴度，贏得高忠程度，患者願意將診所視做健康上的守門人(Gate Keeper)，而從地區別分析中也發現一些，診所服務之涵蓋範圍，可以區分為距離效益、慢性病患者口碑效應與轉移效益。慢性病患者之分群可以分成黃金老主顧、會忘記看病的老主顧、快流失的老主顧、高穩定低忠誠度高獲利新客戶、不常來但還會來的一般客戶、已流失的舊客戶、已流失的中期客戶及流失已久的舊客戶，至於非慢性病患則不需太複雜的分群，本研究建議將非慢性病患者依健保卡卡序計算就醫忠誠度區分。慢性病患群流失的原因與無法提供完整治療，疾病症狀不明顯或與民眾對治療效益的看法改變有關（如更年期）有關。 就病人區隔分析及交叉服務的相關分析都可以發現，以慢性疾病群為中心，針對不同疾病群發展網路治療團隊，應該是未來診所擴張時需要遵循的最重要策略原則；另外健保案件類別的交叉分析，也發現增加預防保健服務可以增加慢性病人的案件，診所需要將成人健康檢查業務當作策略性業務，加強重視並提升品質。 本研究針對描述患者求醫行為過程所發展出對個人主要疾病診斷碼的歸戶處理、RFM相關指標方式、健保卡卡序計算忠誠度及邊際利潤的計算方式對於類似研究應該有其參考價值。至於本研究所提出的診所病患群分群架構，則有待進一步評估其達到CRM顧客最佳化的效果。 / At present, there are much of researches of Customer Relationship Management ( CRM ) and data mining in Taiwan. There is little research in medical service. Our research tried to integrate the three domains knowledge, DBA, domain knowledge of medical service and data mining techniques. This is a case study type research. The CRM Strategy Planning for Outpatient in Kai-Nan Group Practice Clinic by Data Mining on National Health Insurance Dataset. This research included 9300 cases of Kai-Nan Clinic, with nearly 50,000 records of OPD records from Dec, 2000 to Jan, 2003. Our research questions include as followings: 1、How to segment the outpatient, which segment is the most profitable? Which segment is loosing? Why? 2、Which cross service is necessary for what kinds of patients? What kinds of services will be benefit for recruiting chronic patient? What is the reason for the diabetes patient will receive funds examination in this clinic? Are the patients of GYN/OBS will also to be patients of other specialty? Is it necessary to include other specialty in this clinic? 3. Where is the most profitable patient in nearby area? Our study revealed that the most profitable patients is characterized by high medical cost, high loyalty to this clinic, high visit frequencies, high portion of medication fee and high portion of fee for chronic disease. Most of the profitable patients are suffered with chronic diseases. This implies that how to satisfy chronic patient with high satisfaction and earn their trust to be health gate keeper for this patient is very import issue for a clinic. From the results of area analysis for these chronic patients, we concluded the three effects for different areas, such as near-distant effect, public praise and addict effect for original doctors. The segments of chronic patients include golden regular customer、forgetful regular customer、loosing old customer、regular but lower loyalty profitable new customer、irregular general customer、loosed old customer、loosed past customer and loosed old customer. Regarding the segmentation of outpatients of acute illness, we recommended simplify classification according to loyalty that was calculated from the sequence of national health insurance card used in Taiwan. The chronic patients loosed in the clinic was due to lack of comprehensive treatment options, non obvious symptoms or the fears of treatment side effects announced from public media，such as hormone replacement therapy for post menopausal syndrome. We conclude that multidisciplinary team for comprehensive disease management is very important for clinics as our previous success experiences on diabetes patients. Our clinic should expand teams with out bond member according to the needs of our profiles of chronic patients. From association mining, periodic health examinations increase the base of chronic patients. It is strategic important to enhance the staffs and facility for handling periodic health examinations. Our research will also contribute to the following research issues , such as how to describe patients behaviors, how to extract the dominant diagnosis from patients health insurance records, modified RFM dimensions indexes、loyalty based on sequences of health insurance card in Taiwan and the model of calculation of marginal revenue for clinics. As regarding the efficacy of the patients’ segmentation model deserved further study.