開放政府資料是政府積極推動的政策，台灣的開放政府資料平臺也在短時間累積上萬資料集。本研究目標有二，首先檢視衛生相關開放政府資料的資料格式品質與內容，接著開發三個衛生政策決策相關的資料視覺化模組。截至2015年10月底，衛生福利部暨所屬機構開放840個資料集到平臺。可是許多單位將資料分年分病拆解成太多小的統計表資料集，不符合開放政府資料完整性原則的要求，不方便資料分析者使用。約有五分之一的資料集格式還是文書格式檔，不方便機器處理。最常見的四種資料主題是疾病統計類，醫療品質類，名單項目類與預算決算類，占總數七成。下載次數前五名分別為藥局基本資料，不符合食品資訊資料集，死因統計，PIC/S GMP藥廠名單資料集與醫院病床統計。本研究使用套裝商用軟體Tableau及Excel Power Pivot使用衛生福利部釋出的死因統計與中央健康保險署釋出的糖尿病醫療品質指標資料，開發三種衛生政策決策分析模組，一是問題發現，二是優先順序決定，三是績效評價。由於Tableau可以將設計模組上傳至公司提供的網站免費讓人使用，使用者可以透過不同死因別，地區別，年代別選項產出自己所要的統計圖表，非常彈性且方便。本研究結論：衛生相關單位開放政府資料集應該要符合完整性原則，未來應該進一步檢視衛生相關開放政府資料的加值應用情形，開發與其他非衛生單位資料集整合分析的應用。本研究所開發的衛生政策分析視覺化模組應該要讓政策利害相關者實際操作，並且回饋修改意見，讓這些模組能真正提升決策品質。

Taiwan government organizations have implemented Open Government Data (OGD) policies to make their data public available. Over ten thousand datasets are currently available in data.gov.tw within short period. The aims of this study were to examine the quality and content of health-related datasets in data.gov.tw. and to develop three data visualization models for health policy decision making. Until end of October 2015, there were 840 health-related datasets available in data.gov.tw. However, many organizations decomposed the datasets by year or by disease, which did not conform OGD principle of completeness. About one fifth of datasets in which the formats were not machine processable. With regard to the content of the datasets, disease statistics, quality indicators of medical care, lists commitee and items, budegets were the four main themes accounted for seventy percent of all datasets. The most popular downloaded datasets were basic information of pharmacies, inappropriate food information, cause of death statistics, name list of PIC/S GMP pharmaceutical industries and hospital beds statistics. With regard to the second objective, I used Tableau and Excel Power Pivot to develop three data visualization health policy decision making, i.e., problems identification, priority setting, and performace evaluation. The three models have been uploaded into Tableau Public and freely available to general public. Users of the three models could select the year, the cause of death, and the area they concerned to produce the statistical plot they want. In conclusion, health-related organization should conform the OGD principles to release the datasets as complete as possible. Further studies are needed to survey the applications of using health-realted OGD and explore the potential of integrating health-related OGD datasets with non-health OGD datasets. The three models developed in this study should be tested by stakeholds of health policies and some feedbacks to improve the performance of the models and in the long run promote the quality of health policy decision making.

中文文獻
王怡人(2011)，邁向二代健保新紀元－全民健康保險法100年1月修正重點介紹，醫事法學 第18期，頁89-95。
王榮德(2010)，流行病學方法論。台北：健康文化事業股份有限公司。
李治安、林誠夏、莊庭瑞(2014)，開放政府資料的基本原則與相關政策議題，公共治理季刊，第2卷第1期，頁65-76。
宋餘俠、李國田(2012)，政府部門資料加值推動策略與挑戰，研考雙月刊，第36卷第4期，頁10-21。
宋餘俠、簡宏偉，李國田(2012)，政府資料開放加值應用的契機與展望，研考雙月刊，第36卷第4期，頁3-9。
林文宗(2013)，政府資料開放大步走，公共治理季刊，第1卷第3期，頁98-100。
林水波、張世賢(2006)，公共政策。台北：五南出版社。
吳泰廷、楊文新、崔文(2012)，語意網、鏈結資料與開放資料之實務技術與應用，電腦與通訊。第145卷，頁102-9。
郭年真、鍾國彪、賴美淑(2015)，醫院品質報告卡之風險校正，台灣衛誌，第34卷第6期，頁576-591。
陳國東(2007)，應用流行病學。台北：合記出版社。
陳國東譯，Fletcher, R. et al.原著(2015)，簡明臨床流行病學。台北：九州出版社。
衛生福利部醫療品質政策諮詢委員會(2012)，2012醫療品質政策白皮書。台北：衛生福利部。
曹龍彥(2008)，成本效益分析簡介，中華民國新生兒醫學會刊，第17卷第3期，頁4-5。
潘城武(2012)，政府統計資料開放與發展，研考雙月刊，第36卷第4期，頁80-86。
劉自強、黃芝瑩譯，Murray, S.原著(2013)，網頁互動式資料視覺化。台北：基峯資訊股份有限公司。
蕭景燈(2012)，資料開放發展現況與展望，研考雙月刊，第36卷第4期，頁22-38。
錢鉦津(2013a)，政府資訊開放的資料品質與測試概念（上篇），品質月刊，第49卷第5期，頁22-7。
錢鉦津(2013b)，政府資訊開放資料品質與測試概念（下篇），品質月刊，第49卷第6期，頁15-20。
羅清俊、陳志瑋譯，Thomas R. Dye原著(2010)，公共政策分析新論。台北：韋伯出版社。

英文文獻
Bostock, M., Ogievetsky, V., & Heer, J. (2011), “D3: Data-Driven Documents,” IEEE, 17 pp. 2301~2309.
Brownson, R. C., Baker, E. A., Leet, T. L., & Gillespie, K. N. (2003), Evidence-based public health, New York: Oxford University Press.
Burkhardt, D., Nazemi, K., Parisay, M., & Kohlhammer, J. (2014), “Visual correlation analysis to explain open government data based on linked-open data for decision making,” International Journal of Digital Society, 5 (3&4), pp. 915~923.
Chua, S. K., Cheng, J. J., Shyu, K. G., Kuo, J. Y., Ko, Y. L., Wang, C. C., Chang, K.C., Ku, P.M., & Lee, S.H. (2013), “Improvement in door-to-balloon (D2B) time in acute ST-elevation myocardial infarction through the D2B Alliance,” Circulation Journal, 77 (2) pp. 383~389.
Choy, J., Chawla, V., & Whitman, L. (2012), Data visualization techniques: From basics to big data with SAS Visual Analytics, Cary, NC, USA: SAS Institute Inc.
Cohen, D. J., & Reynolds, M. R. (2008), “Interpreting the results of cost-effectiveness studies,” Journal of American College of Cardiology, 52 (25) pp. 2119~2126.
Dever, G. E. A. (2006), Managerial epidemiology: practice, methods, and concepts, Sudbury, MA: Jones and Bartlett Publishers, 2006.
Graves, A., & Hendler, J. (2013), “Visualization tools for open government data,” Proceedings of the 14th Annual International Conference on Digital Government Research, ACM, pp. 136~145.
Graves, A. (2013), “Creation of visualizations based on linked data,” DataWiz 2014: Data Visualization Workshop, ACM, pp. 1~6.
Graves, A., & Bustos-Jimenez, J. (2014), “Towards visual overviews for open government data,” Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics, ACM, Article No. 41.
Graves, A., & Bustos-Jimenez, J. (2015), “Co-creating visual overview for open government data,” Proceedings of the 16th Annual International Conference on Digital Government Research, ACM, pp. 37~42.
Iliinsky, N. (2013), Choosing visual properties for successful visualizations, New York: IBM Corporation.
Keabey, T., A. (2013), Using visualization to understand big data, New York: IBM Corporation.
Lankow, J., Ritchie, J., & Crooks, R. (2012), Infographics: The power of visual storytelling, New Jersey: John Wiley & Sons.
Lu, T. H., Huang, Y. T., & Chiang, T. L. (2011), “Using the diamond model to prioritize 30 causes of death by considering both level and inequality of mortality.” Health Policy, 103 (1), pp.63~72.
Morton, K., Balazinska, M., Grossman, D., & Mackinlay, J. (2014), “Support the data enthusiast: challenges for next-generation data-analysis systems,” Proceedings of VLDB Endowment, 7 (6), pp.453~456.
Nolte, E., & McKee, M. (2004), Does health care save lives? Avoidable mortality revisited, London: The Nuffield Trust.
Oleske, D. M. (2001), Epidemiology and the delivery of health care services: Methods and applications, New York: Plenum Press.
Sharda, R., Delen, D., & Turban, E. (2014), Business intelligence and analytics, Tenth edition, Essex, England: Pearson Education Limited.
Sopan, A., Noh, A. S., Karol, S., Rosenfeld, P., Lee, G., & Shneiderman, B. (2012), “Community Health Map: A geospatial and multivariate data visualization tool for public health datasets,” Government Information Quarterly, 2012 29 (3), 223~234.
Spasoff, A. (1999), Epidemiologic methods for health policy, New York: Oxford University Press.
Tennekes, M., de Jonge, E., & Daas, P. J. H. (2013), “Viualizing and inspecting large datasets with tableplots,” Journal of Data Science, 11 (1), pp. 43~58.
US Department of Health and Human Services. Office of Disease Prevention and Health Promotion. Healthy People 2010 Toolkit: setting health priorities and establishing objectives, Washington, DC: US Department of Health and Human Services; 2001. http://www.phf.org/resourcestools/Pages/Healthy People 2010 Toolkit.aspx, accessed at Sep 15, 2011.
Vilnius, D., & Dandoy, S. (1990), “A priority rating system for public health programs,” Public Health Report, 105 (3) pp. 463~470.
W3C Government Linked Data Working Group (2013). 5 Star Linked Open Data in Linked Data Glossary, Accessed November 28, 2015 at http://www.w3.org/TR/ld-glossary/#x5-star-linked-open-data.