近年來，隨著我國國民的壽命持續增長，人口老化愈加明顯。預期臺灣在2021年將進入人口零成長，2025年65歲以上人口比例也將超過20%（來源：國家發展委員會2014年人口推估）。人口老化帶來許多問題，如老年生活、醫療、以及長期照顧等需求，其中照顧需求與年齡正相關，預期需求將隨壽命延長而增加，需要及早規劃及因應，這也是今年通過長期照護法的原因。由於各國國情不同，對於長期照護的定義、補助及需求也不盡相同，有必要發展適用於臺灣特性的，推估長期照顧需求的所需之資源。重大傷病中的許多疾病與失能、甚至長期照護有關，由於全民健保實施至今已逾20年，重大傷病的認定標準及程序相對客觀、中立，受到民眾、學術、政府各界肯定。 有鑑於此，本文以全民健保資料庫的重大傷病資料庫為基礎，挑選八類引發長照的重大傷病，作為規劃長期照護保險的參考。本文以這些傷病的發生率、罹病後死亡率、罹病後存活率等，結合國發會所人口推估的結果，利用年輪組成法(Cohort Component Method)推估長期照顧的未來需求。研究發現：未來需求人口從2013年約10萬人，迅速增加至2060年的21萬人，增加速度相當快。而參考「長期照顧保險法」草案的給付內容，若聘請一名外籍看護每月20,000元計算，每人分擔將從2012年的$530元/月升至2060年的2,728元/月；若不調整保費且以隨收隨付計算，每人每月繳交400元長照保費，長照給付將從2012年每月13,353元降至2060年每月3,556元，由此可知壽命延長、人口老化將造成長照保險的財務問題。另外，本文考量的八項重大傷病較為保守，沒有加入老化、遺傳等因素的長照需求，預期將不足以因應實際需求，未來有必要引入商業保險來彌補社會保險的不足。 / In recent years, with the sustainable growth of the life expectancy in our country, population aging becomes more apparent. Taiwan’s population of ages 65 and over will exceed 20% within 10 years, before 2025. (Source: National Development Council - Population Projection on 2014). The population aging an prolonging life incurs a big demand for caring the elderly, such as the economic need after the retirement, medical cost, and long-term care. Among these needs, the demand of long term care was under-estimated and is only recognized recently. Thus, this study focuses on predicting the need of long-term care in Taiwan. Specifically, the definition and standard (as well as types and amounts of subsidy) for juding whether one needs long-terma care is not yet determined, although Taiwan’s government passed the long-term care law (Long-Term Care Insurance Law) earlier this year. We should adapt the notion of catastrophic illness (CI) and use certain CI categories, which are related to long-term care, to design the long-term care insurance. Catastrophic illness (CI) is one of the key features of Taiwan’s National Health Insurance (NHI), and the definition and process of evaluating if one is with the CI is quite complete. We choose eight categories of CI and use the NHI database to obtain their incidence rates, mortality rates, and survival probability. Together with the population projection from National Development Council in 2014 and the cohort component method to predict the long-term care demand in Taiwan. The syudy result shows that the population needing long-term care will rise from about 100 thousands in 2013 to about 210 thousands in 2060. Moreover, if the long-term care insurance is funded via pay-as-you-go, the individual premium required will rise 5 times from 2012 to 2060. This indicates that the long-term care might be too expensive and the commercial insurance can play an important role as a supplement.

全球每日產出的資料量持續成長，龐大的資料量、雜亂的資料檔案格式造成資料處理的困難；此外，全球智慧型手機的出貨量持續上升，未來將會至少人手一台行動裝置，同時行動網路的效能提升將可負荷更多的資料流量，行動工作者的數量也因此逐年增加。對商業智慧系統而言，透過企業資料的分析可以發現資訊之間的關連與隱藏其中的事實，讓使用者掌握更多的知識用於決策，分析的資料來源越豐富，其可提供做為決策用的訊息就更為準確。 　　過往商業智慧透過關聯式資料庫處理資料來源及電子郵件的通知使用者，但是龐大的巨量資料遠超過前者所能有效處理的數量，進而造成對資料擷取、保存、使用、分享以及分析時的處理難度；後者對於外出的使用者來說，電子郵件僅只是收到通知而已，使用者依然得需要電腦才能觀看分析報表。 　　故本研究使用雲端運算分散儲存及運算的技術及行動裝置隨手可得的特性解決前述的兩個問題，先透過雲端資料庫加速處理巨量資料的存取並製作成資料倉儲供商業智慧使用，接著透過行動應用程式即時接收推播訊息並呈現分析報表於行動裝置上。 　　在實作中，利用非結構化資料庫進行資料的存取，比起過往的關聯式資料庫確實可以有效提升巨量資料處理的速度；透過行動裝置的報表呈現，在平板電腦有較佳的成效，在手機上則是因為螢幕大小的關係，畫面呈現效果較差，這方面則有待改善。 　　本研究透過非結構化資料庫及行動應用程式設計新的行動商業智慧解決方案，實作雛型系統，並且透過異常申報健保費用醫院為案例，進行系統整體的測試，證明其架構及運作模式之可行性。經過驗證，本系統將能提供使用者使用巨量資料做為分析數據，並且透過行動應用程式立即取得分析報表。 / The volume of daily output data continues to grow world- widely. The huge amount of data and the disorder of data format cause the difficulty of data processing. Additionally, the number of smartphone sales is continuously growing, so everyone will own at least one smartphone in the future. In the meantime, the effectiveness of mobile internet and wireless is largely improved, so it can be loaded with more data flow. Because of this phenomenon, the number of mobile workers will be increasing per year. For business intelligence systems, through the analysis of enterprise's data we can find the relevance and facts hidden in information, allowing users to acquire more knowledge for decision-making. The more data sources we analyze, the more accurate information can be used to make decision. 　　In the past, business intelligence processes data sources through relational database and uses e-mail to notify users. However, the huge amount of data exceeds the number that can be effectively processed by relational database. On account of this, it becomes difficult regarding data acquisition, storage, application, sharing, and analysis. As far as the users are concerned, they only receive notifications by emails, so they still need a computer to view the analysis report. 　　In this study, I use cloud computing technology and mobile devices to solve the two aforementioned issues. First, we speed up the process of big data in data acquisition through Hadoop Hbase, and made it into data warehouse for Business Intelligence use. Secondly, we use mobile applications to receive push messages instantly and present analysis reports. 　　In the practical work, I use NoSQL database to acquire and store data. Compared with relational database, we can indeed effectively enhance the speed of big data processing. In reports’ presentation on mobile devices, the Tablet has better user experience then the phone. The phone is displayed comparatively poorly because of its small screen. This part needs to be improved. 　　In this research, I conceive a new solution of mobile business intelligence through NoSQL database and mobile applications, and implement this method into a prototype system. Moreover, through an example of the analysis of hospitals which have anomalous health-insurance reporting expenses we can test the whole system. It proves that this system’s structure and the mode of operation are feasible. The system will be able to provide big data as the source of analysis and present reports immediately through mobile devices to users.

重大傷病是我國全民健康保險的重要特色之一，透過社會保險的風險分擔機制，病患享有免部分負擔等優惠，降低因為罹病帶來的財務負擔，但重大傷病同時也成為全民健保的主要支出項目。民國102年領取重大傷病證明者不過98餘萬人（約總人口的4%），但其一年的醫療費用多達一千五百多億元（接近總支出的27%），平均每位重大傷病患者的醫療費用約為平均值的7.34倍，其中癌症又是重大傷病中人數最多者，大約佔了49%（資料來源：衛生福利部中央健康保險署）。因為許多重大傷病的發生率、盛行率與年齡成正比（黃泓智等人，2004），未來隨著人口老化，全民健保支出也將跟著上升。 　　本文使用全民健保資料庫，探討近十年重大傷病（尤其是癌症）趨勢，估計重大傷病的年齡別發生率、死亡率，評估人口老化對全民健保造成的影響，其中承保資料檔（ID）、重大傷病檔（HV）為本研究主要的依據資料。而由於健保資料庫的資料種類及數量龐雜，在初期資料的偵錯及處理上非常重要但也相當費時，至於發生率、死亡與否的判斷亦十分棘手，因此過程中我們將一一說明資料分析步驟及注意事項。本文發現癌症及重大傷病的盛行率逐年上升，但發生率並沒有明顯變化，加上近年癌症死亡率幾乎不變（但台灣全體國民的死亡率逐年遞降），因為台灣的人口老化，預期未來罹患癌症人數會逐年增加，癌症將繼續蟬聯十大死因之首，但罹癌死亡率的下降也可發現近年醫療進步所造成的影響。此外，我們也考量隨機死亡模型（Lee-Carter Model），發現無論是癌症死亡率、或是罹癌死亡率都有不錯的估計結果。而在文末也提出癌症病患的就醫行為以供後續研究者參考。 / Catastrophic illness (CI) is one of the key features of Taiwan’s National Health Insurance (NHI). Through risk-sharing mechanisms of social insurance, it can reduce the financial burden of the CI patients since treating the CI is usually expensive. However, the CI also becomes a major expenditure item of NHI. The people receiving the CI card are just 0.98 million in 2013 (about 4% of the total population), but their smedical costs are over 150 billion NT dollars (nearly 27% of total expenditures). The average medical cost per CI patient is about 7.34 times of the national average. (Source: Department of Health and National Health Insurance Agency). Because the incidence and prevalence rates increase with age (Huang et al, 2004), the total NHI expenditure is expected to increase in the future due to population aging. This study intends to use the NHI database, including the records of personal identification and out-patient visit from all CI patients, to explore the incidence and mortality rates, for example, of CI patients. Because the NHI database is big and messy, we shall first debug and clean them. Also, since the death of CI patients are not fully reported in the NHI database, we propose a method to identify the deaths and use the official statistics to evaluate. The results show that the prevalence rates of all CI increased every year, but their incidence rates did not change significantly. The mortality rates of cancer patients also did not change much. Based on these findings, we expect the proportion of CI patients and their size will continue to grow. In addition, we applied the Lee-Carter model to the cancer mortality rates, and the fit is pretty good.