隨著網路蓬勃發展，線上醫療問病網站隨之興起，當中大量的問答案例，是貴重的醫療資源，如能有效率地提供大量使用者查詢瀏覽、重複利用，將可避免重複提問、重複回答造成的醫療資源浪費。對於缺乏基礎醫療知識的使用者而言， 對於缺乏基礎醫療知識的使用者而言， 想有效查詢利用醫療問病網站上的現案例，依然是相當吃力工作 。
本研究 提出一個概念流程，透過整合「模擬主述」 和分析目的性需求提出一個概念流程，透過整合「模擬主述」 和分析目的性需求提出一個概念流程，透過整合「模擬主述」 和分析目的性需求提出一個概念流程，透過整合「模擬主述」 和分析目的性需求提出一個概念流程，透過整合「模擬主述」 和分析目的性需求提出一個概念流程，透過整合「模擬主述」 和分析目的性需求提出一個概念流程，透過整合「模擬主述」 和分析目的性需求兩個概念，並 兩個概念，並 兩個概念，並 結合關鍵字判斷與分類標籤工作， 結合關鍵字判斷與分類標籤工作， 結合關鍵字判斷與分類標籤工作， 設計出一套 「階層引導式查詢系 階層引導式查詢系 統」，引導使用者依序透過「模擬主述程」、選擇科別下子分類和析目 ，引導使用者依序透過「模擬主述程」、選擇科別下子分類和析目 ，引導使用者依序透過「模擬主述程」、選擇科別下子分類和析目 的性需 求」三個階段，透過選項引導不的性需 求」三個階段，透過選項引導不的性需 求」三個階段，透過選項引導不的性需 求」三個階段，透過選項引導不的性需 求」三個階段，透過選項引導不的性需 求」三個階段，透過選項引導不主動 輸入任何關鍵字，即可完成案 輸入任何關鍵字，即可完成案 輸入任何關鍵字，即可完成案 例篩選的過程。
本研究設計的查詢系統通過醫療專業者協助， 驗證了篩選流程正確性本研究設計的查詢系統通過醫療專業者協助， 驗證了篩選流程正確性本研究設計的查詢系統通過醫療專業者協助， 驗證了篩選流程正確性本研究設計的查詢系統通過醫療專業者協助， 驗證了篩選流程正確性並透過問卷調查法， 觀察 33 位使用者 的「自我效能」、預期成果 的「自我效能」、預期成果 的「自我效能」、預期成果 /感知有用性」、 感知有用性」、 感知有用性」、 感知有用性」、 感知有用性」、 感知有用性」、 感知有用性」、 「感知易用性」三個 「感知易用性」三個 面向 ，證實相對於照組，使用 證實相對於照組，使用 證實相對於照組，使用 本研究之「階層引導式查詢 本研究之「階層引導式查詢 系統時」 在三個驗證面向皆顯著有更好之表現， 在三個驗證面向皆顯著有更好之表現， 顯示 本研究之概念流程，有助於 本研究之概念流程，有助於 本研究之概念流程，有助於 本研究之概念流程，有助於 提升易用性 、降低使用門檻的同時，更能 降低使用門檻的同時，更能 降低使用門檻的同時，更能 降低使用門檻的同時，更能 提升 使用者的結果預期，有助於 使用者的結果預期，有助於 使用者的結果預期，有助於 使用者的結果預期，有助於 拉抬 使 用者使 用 查詢系統之意願，促進醫療問答案例資源更有效運。

A large number of Q&A cases on online medical Q&A platforms are valuable medical resources. By providing these cases for a large number of users to browse and reuse efficiently, medical resources would be saved by reducing repeated questions and repeated answers. For users who have a lack of basic medical knowledge, it is quite a difficult task to effectively query the existing cases on the medical Q&A platforms.
This study proposes a conceptual process with "simulated subjective description" and "analyze purpose requirements" and designs a "hierarchical guided query system" combined the conceptual process with keyword judgment and classification label work. The users are able to complete the case screening process through the guidance of the options without actively entering any keywords by following the three stages of "simulated subjective description", "choose the sub-category " and "analyze purpose requirements ".
The correctness of the screening process is verified by medical experts. Through the questionnaire, 33 users were observed for "self-efficacy", "outcome expectancy/perceived usefulness", and "perceived ease-of-use". Compared with the control group, users feedback in all three aspects are better on experimental group, revealing that the conceptual process of this study not only improves the user's outcome expectancy but also promotes the user's willingness to use the query system, will leading more effective use of medical resources.

論文審定書 i
誌謝 ii
摘要 iii
Abstract iv
圖次 vii
表次 viii
1. 緒論 1
2. 文獻探討及回顧 4
2.1 醫療紀錄整理與再利用 4
├2.2 專業知識落差與詞彙落差造成的查詢困難 5
├2.3 醫療問答之類型 6
└2.4 影響使用意圖之因素 7
├2.4.1 科技接受模型 7
├2.4.2 自我效能 7
└2.4.3 預期成果/感知有用性 8
3. 研究方法 9
├3.1 研究設計 11
├3.2 資料前處理 11
│ ├3.2.1 資料來源 11
│ └3.2.2 資料獲取與初步整理除錯 13
├3.3 關鍵字分析 14
│ ├3.3.1 定義關鍵字集合 14
│ └3.3.2 添加關鍵字標籤 19 
└3.4 網頁查詢系統實作 20
├3.4.1 第一階段：「模擬主述過程」 21
├3.4.2 第二階段：「選擇醫師標記之分類」 22
├3.4.3 第三階段：「分析目的性需求」 24
└3.4.4 第四階段：「最終結果展示」 26
4. 實驗與結果 27
├4.1 系統正確性驗證與結果 27
├4.2 理論驗證實驗設計 29
│ ├4.2.1 實驗組與對照組之設計 29
│ ├4.2.2 實驗假設 32
│ ├4.2.3 驗證方法 32
│ └4.2.4 問卷設計 32
├4.3 理論驗證結果 34
│ ├4.3.1 測試與問卷回收 34
│ ├4.3.2 信度檢驗 35
│ └4.3.3 效度檢驗 39
└4.4 假設驗證 42
5. 彙總與討論 44
├5.1 總結 44
├5.2 研究限制 45
└5.3 未來方向 47
參考文獻 49
附錄 54

[1] AHIMA Computer-Assisted Coding e-HIM Work Group. (2004). Delving into computer-assisted coding. Journal of AHIMA/American Health Information Management Association, 75(10), 48A.
[2] Adlassnig, K. P. (2009, August). Automatic mapping of clinical documentation to SNOMED CT. In Medical Informatics in a United and Healthy Europe: Proceedings of MIE 2009, the XXII International Congress of the European Federation for Medical Informatics (Vol. 150, p. 228). IOS Press.
[3] Baker-Eveleth, L., & Stone, R. W. (2008). Expectancy theory and behavioral intentions to use computer applications. Interdisciplinary Journal of Information, Knowledge, and Management, 3, 135-146.
[4] Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological review, 84(2), 191.
[5] Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ, 1986.
[6] Chen, N. S., Huang, H. Y., & Shih, Y. C. (2002, December). Factors affecting usage of web-based teachers' training in elementary and high school. In Computers in Education, 2002. Proceedings. International Conference on (pp. 589-592). IEEE.
[7] Compeau, D. R., & Higgins, C. A. (1995). Application of social cognitive theory to training for computer skills. Information systems research, 6(2), 118-143.
[8] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
[9] Downey, J. (2006, January). Measuring general computer self-efficacy: The surprising comparison of three instruments in predicting performance, attitudes, and usage. In System Sciences, 2006. HICSS'06. Proceedings of the 39th Annual Hawaii International Conference on (Vol. 8, pp. 210a-210a). IEEE.
[10] Grandon, E. E., Alshare, K., & Kwun, O. (2005). Factors influencing student intention to adopt online classes: A cross-cultural study. Journal of Computing Sciences in Colleges, 20(4), 46-56.
[11] Hernandez, B., Jimenez, J., & Jose Martin, M. (2008). The impact of self-efficacy, ease of use and usefulness on e-purchasing: An analysis of experienced e-shoppers. Interacting with Computers, 21(1-2), 146-156.
[12] Henry, J. W., & Stone, R. W. (1995). Computer self-efficacy and outcome expectancy: the effects on the end-user's job satisfaction. ACM SIGCPR computer personnel, 16(4), 15-34.
[13] Hersh, W. R., & Hickam, D. (1995). Information retrieval in medicine: the SAPHIRE experience. Journal of the American Society for Information Science (1986-1998), 46(10), 743.
[14] Hina, S., Atwell, E., & Johnson, O. (2010). Semantic tagging of medical narratives with top level concepts from SNOMED CT healthcare data standard. Int. J. Intell. Comput. Res, 2, 204-210.
[15] Holden, H., & Rada, R. (2011). Understanding the influence of perceived usability and technology self-efficacy on teachers’ technology acceptance. Journal of Research on Technology in Education, 43(4), 343-367.
[16] Hsu, W. (2013). Bridging the vocabulary gap for EFL medical undergraduates: The establishment of a medical word list. Language Teaching Research, 17(4), 454-484.
[17] Karahanna, E., Agarwal, R., & Angst, C. M. (2006). Reconceptualizing compatibility beliefs in technology acceptance research. MIS quarterly, 781-804.
[18] Kuo, Y. H., Lee, C. S., Guo, S. M., & Tu, F. T. (2004). Apply FNN Model to Construct Ontology-based Q&A System. WSEAS Transactions on Communications, 3(1), 328-335.
[19] Lai, M. L. (2008). Technology readiness, internet self-efficacy and computing experience of professional accounting students. Campus-Wide Information Systems, 25(1), 18-29.
[20] Ma, Q., & Liu, L. (2003). The role of internet self-efficacy in accepting web-based medical records. AMCIS 2003 Proceedings, 110.
[21] Nie, L., Zhao, Y. L., Akbari, M., Shen, J., & Chua, T. S. (2015). Bridging the vocabulary gap between health seekers and healthcare knowledge. IEEE Transactions on Knowledge and Data Engineering, 27(2), 396-409.
[22] Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students' behavioral intention to use e-learning. Educational technology & society, 12(3), 150-162.
[23] Park, Y., & Chen, J. V. (2007). Acceptance and adoption of the innovative use of smartphone. Industrial Management & Data Systems, 107(9), 1349-1365.
[24] Patrick, J., Wang, Y., & Budd, P. (2007, January). An automated system for conversion of clinical notes into SNOMED clinical terminology. In Proceedings of the fifth Australasian symposium on ACSW frontiers-Volume 68 (pp. 219-226). Australian Computer Society, Inc.
[25] Plouffe, C. R., Hulland, J. S., & Vandenbosch, M. (2001). Richness versus parsimony in modeling technology adoption decisions—understanding merchant adoption of a smart card-based payment system. Information systems research, 12(2), 208-222.
[26] Sang, E. T. K., Bouma, G., & De Rijke, M. (2005, July). Developing offline strategies for answering medical questions. In Proceedings of the AAAI-05 Workshop on Question Answering in Restricted Domains, Pittsburgh, PA, USA (pp. 41-45).
[27] Strong, D. M., Dishaw, M. T., & Bandy, D. B. (2006). Extending task technology fit with computer self-efficacy. ACM SIGMIS Database: the DATABASE for Advances in Information Systems, 37(2-3), 96-107.
[28] Surendran, P. (2012). Technology acceptance model: A survey of literature. International Journal of Business and Social Research, 2(4), 175-178.
[29] Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision sciences, 27(3), 451-481.
[30] Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
[31] Wang, S. H. (2016). Ontology-Based Semantic Q&A system in Health Care: An Illustrated Application on Down Syndrome.
[32] Zhang, Y. (2013). Toward a layered model of context for health information searching: an analysis of consumer-generated questions. Journal of the American Society for Information Science and Technology, 64(6), 1158-1172.
[33] 林玉玟. (2013). 從可用性, 知覺有用性, 知覺易用性, 與組織學習對 ERP 系統績效之影響 (Doctoral dissertation, 撰者)