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請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/96854
完整後設資料紀錄
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dc.contributor.advisor陳家麟zh_TW
dc.contributor.advisorChialin Chenen
dc.contributor.author陳偉勵zh_TW
dc.contributor.authorWei-Li Chenen
dc.date.accessioned2025-02-24T16:16:11Z-
dc.date.available2025-02-25-
dc.date.copyright2025-02-24-
dc.date.issued2024-
dc.date.submitted2025-01-03-
dc.identifier.citation王一中,臺大眼科部2023年度的年報紀錄。
李吉仁,2019,哈佛百年經典系列2-4:臺大教授李吉仁提4大關鍵,讓驅動轉型成轉機,https://www.hbrtaiwan.com。
李吉仁,2024,轉型再成長:策略架構與執行力,天下雜誌出版社。
Barbier C, Neri L, Stuard S. et. al. 2023. From electronic health records to clinical management systems: how the digital transformation can support healthcare services. Clinical Kidney Journal. 16: 1878-1884.
Drucker, Peter Ferdinand. 2006. Managing the Non-Profit Organization: Principles and Practices. Harper Business.
Ghosh K, Dohan MS, Veldandi H, et al. 2023. Digital Transformation in Healthcare: Insights on Value Creation. Journal of Computer Information Systems, 63: 449-459.
Hsieh YT, Chuang LM, Jiang YD, et al. 2021. Application of deep learning image assessment software VeriSee™ for diabetic retinopathy screening. Journal of the Formosan Medical Association, 120: 165-171.
Kang D, Wu H, Yuan L, et al. 2024. A Beginner’s Guide to Artifcial Intelligence for Ophthalmologists. Ophthalmol Ther, 13:1841–1855.
Kang EYC, YT Hsieh, CH Li, et al. 2020. Deep learning–based detection of early renal function impairment using retinal fundus images: model development and validation. JMIR med Inform, 8: 223472.
Kotter JP. 1995. Leading Change: Why Transformation Efforts Fail. Harvard Business Review, 1995 (May).
Kotter JP, 2002. The Heart of Change: Eight Stage Mode of Successful Change
Kraus S, Schiavone F, Pluzhnikova A, et al. 2021. Digital transformation in healthcare: Analyzing the current state-of-research. Journal of Business Research, 123: 557-567.
Kretschmer T, Khashabi P. 2020. Digital Transformation and Organization Design: An integrated approach. California Management Review. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3437334.
Kuo BI, Chang WY, Lian TS, et al. 2020. Keratoconus screening based on deep learning approach of corneal topography. Translational Vision Science & Technology, 9: 53-53.
Li YH, Sheu WH, Chou CC, et al. 2021. The clinical influence after implementation of convolutional neural network-based software for diabetic retinopathy detection in the primary care setting. Life, 11: 200.
Libert B, Beck M. 2016. Wind Y. How to Navigate a Digital Transformation. Harvard Business Review, 2016 June 22. https://hbr.org/2016/06/how-to-navigate-a-digital-transformation.
Tsai MJu, Hsieh YT, Tasi CH, et al. 2022. Cross-camera external validation for artificial intelligence software in diagnosis of diabetic retinopathy. Journal of Diabetes Research, 2022: 5779276.
Waisberg E, Ong J, Kamran SA, et al. 2024. Generative artificial intelligence in ophthalmology. Survery of Ophthalmology 2024, https://doi.org/10.1016/j.survophthal.2024.04.009.
Wu H, Jin K, Yip CC, et al. 2024. A systematic review of economic evaluation of artificial intelligence-based screening for eye diseases: From possibility to reality. Surgery pf Ophthalmology, 69: 499-507.
Yang CN, Chen WL, Yeh HH, et al. 2024. Convolutional Neural Network–Based Prediction of Axial Length Using Color Fundus Photography. Translational Vision Science & Technology.13: 23-23.
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/96854-
dc.description.abstract本研究旨在探討臺大眼科部作為臺灣領先的眼科醫學中心,在「數位智慧轉型」過程中所面臨的挑戰與機會。隨著全球醫療系統的高度競爭與數位化進展,「數位智慧轉型」已成為提升醫療品質與競爭力的關鍵議題。臺大眼科部在此方面的布局甚早,然其過程中亦面臨諸多問題,這些挑戰不僅涉及科技升級,更涵蓋組織文化變革、資源整合及管理模式調整等。本研究旨在系統性分析臺大眼科部的「數位智慧轉型」現況,探討其挑戰與可能的解決方案,並從商業管理視角提出具體領導策略,以促進轉型成功。
本研究採用「個案分析法」對臺大眼科部進行深入分析,該方法讓我們得以了解該醫療機構在技術應用、管理模式及組織文化變革中的實踐經驗與策略,並分析其如何在資源有限的環境中有效推動「數位智慧轉型」。研究進一步探討轉型過程中面臨的問題與挑戰,並分為「數位轉型」和「智慧轉型」兩大部分。
在「數位轉型」部分,臺大眼科部目前面臨的挑戰主要來自過多的門診病患、擁擠的看診流程、醫護人員短缺及過勞,並伴隨病患的抱怨。此外,傳統「非結構化病歷」須轉型為「結構化病歷,」以提升資料管理效率及跨院區互通性,符合政府期待。然而,技術實施與使用者接受度仍有待克服。基於此,本研究將「數位轉型」分為「門診報到叫號系統」與「結構化病歷」兩部分進行探討,資料來源包括(I)採用「半/非結構式訪談法」訪問十位來自六種職別的員工,及(II)組織內部資料。訪談結果揭示了數位轉型中的具體需求與挑戰,如醫護人員對新技術適應困難、跨部門協作問題,以及技術實施中的資源限制。研究使用PIVOT數位轉型模式、非營利機構管理分析法及C-SOP架構進行分析,以探討臺大眼科如何在資源有限的情況下有效推動數位轉型。PIVOT模型強調數位轉型過程中的靈活性與適應性,鼓勵在快速變化的環境中持續調整策略以達成轉型目標。「非營利組織經營分析法」則從非營利醫療機構的角度,探討如何在資源有限的情況下,有效推動技術創新與應用,特別是在人力與經費受限的狀況下,合理配置資源以實現數位轉型。C-SOP模型提供了系統性框架,幫助我們全面分析數位轉型的各個關鍵步驟,涵蓋從策略制定到執行的全過程,確保轉型策略的落實與持續優化。
在「智慧轉型」部分,臺大眼科部在「偵測式AI」與「生成式AI」領域均有初步成果。在「偵測式AI」方面,除發表學術論文外,謝易庭醫師團隊與宏碁智醫合作的VeriSee Dr網膜病變輔助診斷系統已開發並投入應用。然而,科技進步快速,臺大眼科部仍需加強管理策略來應對挑戰。在「生成式AI」方面,儘管已初步組成團隊並開始嘗試發表論文及應邀演講,仍需商業管理的視角進行策略性經營。研究採用VRIO模式、PEST模式、SWOT模式及「商業模式九宮格」進行分析,這些工具協助臺大眼科部了解其核心競爭力及宏觀環境中的關鍵影響,並分析其如何運用商業策略推動智慧轉型。這些分析方法協助臺大眼科部深入了解自身的核心能力,並掌握在宏觀環境中政治、經濟、社會及科技等方面的關鍵影響,幫助評估這些變化對「智慧轉型」商業策略的影響。同時,這些方法也讓臺大眼科部清楚辨識目前所具備的優勢、劣勢、機會與威脅,並針對智醫產品商品化的挑戰,從商業角度進行策略性佈局,為其未來發展奠定基礎。
透過以上分析,本研究多面向剖析了臺大眼科部在「數位智慧轉型」中的挑戰與未來前景,整合管理模式、組織文化及資源運用後,期待提出具體的領導策略與建議,以推動臺大眼科的未來發展,並為其他醫療機構提供寶貴的「數位智慧轉型」參考。最後,本研究以Kotter 變革八部曲作為總結,期待本研究的成果可以落地執行,真正走入成功變革的階段。
zh_TW
dc.description.abstractThis study aims to explore the challenges and opportunities faced by the Ophthalmology Department of National Taiwan University Hospital (NTUH), as a leading ophthalmic medical center in Taiwan, during its process of "digital and smart transformation." As global healthcare systems become increasingly competitive and digitalized, "digital and smart transformation" has emerged as a critical issue for improving the quality of care and competitiveness. While NTUH Ophthalmology Department has been an early mover in this area, it has encountered numerous problems along the way. These challenges not only involve technological upgrades but also encompass organizational culture change, resource integration, and adjustments in management models. This study seeks to systematically analyze the current state of NTUH Ophthalmology Department's "digital and smart transformation," investigate the challenges and potential solutions, and propose specific leadership strategies from a business management perspective to ensure a successful transformation.
The study adopts a "case analysis method" to conduct an in-depth analysis of NTUH Ophthalmology Department. This method enables us to understand the practical experiences and strategies of the institution in technological applications, management models, and organizational cultural change, while also analyzing how the department effectively advances its "digital and smart transformation" in a resource-limited environment. The study further delves into the problems and challenges encountered during the transformation process, divided into two major parts: "digital transformation" and "smart transformation."
In the "digital transformation" section, the main challenges faced by NTUH Ophthalmology Department include an overwhelming number of outpatient visits, congested consultation processes, shortages of healthcare personnel, staff burnout, and patient complaints. Additionally, traditional "unstructured medical records" must transition into "structured medical records" to enhance data management efficiency, enable cross-hospital data communication, and meet government expectations. However, challenges remain in terms of technological implementation and user acceptance. Therefore, this study breaks down "digital transformation" into two parts: "outpatient registration and queuing system" and "structured medical records." The data sources include (I) semi/non-structured interviews with ten employees from six different roles within the department, and (II) internal organizational data. The interviews revealed specific needs and challenges in the digital transformation, such as healthcare workers' difficulties in adapting to new technologies, inter-departmental collaboration issues, and resource limitations in technological implementation. The study employs the PIVOT digital transformation model, nonprofit organization management analysis, and the C-SOP framework to explore how NTUH Ophthalmology effectively drives digital transformation under limited resources. The PIVOT model emphasizes flexibility and adaptability in the digital transformation process, encouraging continuous strategy adjustments in rapidly changing environments. The "nonprofit organization management analysis" examines how to effectively promote technological innovation and application in nonprofit healthcare institutions, especially in situations with limited financial and human resources. The C-SOP model provides a systematic framework to comprehensively analyze the critical steps of digital transformation, covering the entire process from strategy formulation to execution, ensuring the successful implementation and continuous optimization of transformation strategies.
In the "smart transformation" section, NTUH Ophthalmology Department has made initial progress in both "detective AI" and "generative AI" sections. In the "detective AI" section, in addition to publishing academic papers, Dr. Hsieh Yi-Ting's team has collaborated with Acer Healthcare on the development and implementation of the "VeriSee Dr. Retinal Disease Detection System." However, as technology advances rapidly, NTUH Ophthalmology Department still faces significant challenges and needs to strengthen its management strategies to keep up with the pace. In the "genertive AI" section, although the department has formed a preliminary team, attempted to publish papers, and accepted invitations for lectures, it still requires strategic management from a business perspective. The study applies VRIO, PEST, SWOT, and the Business Model Canvas to analyze the department's core competencies and the key impacts of the macro environment, including political, economic, social, and technological factors, as well as how these changes affect the business strategies for smart transformation. These analytical methods help NTUH Ophthalmology Department to clearly identify its current strengths, weaknesses, opportunities, and threats. Moreover, they provide insights into the commercialization challenges of smart medical products and offer a strategic business layout for future development.
Through the above analyses, this study provides a comprehensive examination of the challenges and future prospects of NTUH Ophthalmology Department in its "digital and smart transformation." By integrating management models, organizational culture, and resource utilization, the study aims to propose specific leadership strategies and recommendations for promoting the department's future development, and it offers valuable references for other medical institutions undergoing "digital and smart transformation." Finally, this study concludes with Kotter's Eight Steps for Leading Change, hoping that the findings can be effectively implemented and truly progress into the stage of successful transformation
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dc.description.tableofcontents目次
口試委員會審定書 I
致謝 II
中文摘要 III
THESIS ABSTRACT V
目次 VIII
圖次 X
表次 XII
第一章 緒論 1
第一節 研究背景和動機 1
第二節 研究問題 3
第三節 研究目的 5
第四節 研究方法6
第二章 文獻回顧 10
第一節 數位轉型對於醫療院所營運的重要性分析 10
第二節 人工智慧(偵測式AI)在眼科疾病篩檢的經濟學分析 11
第三節 Barry Linbert的數位轉型模式(PIVOT模式) 14
第四節 Peter Drucker的非營利機構的經營之道 17
第五節 企業轉型的C-SOP架構 19
第六節 企業策略的SWOT優劣分析法 21
第三章 臺大眼科的數位智慧化探討 24
第一節 臺大眼科的組織架構和營運狀況 24
第二節 臺大眼科門診的壅塞困境分析 30
第三節 臺大眼科部數化智慧化的歷程 33
第四章 個案分析 49
第一節 以Barry Linbert的PIVOT模式分析「數位轉型」 50
第二節 以非營利組織經營分析 66
第三節 以企業經營轉型成長C-SOP進行分析 73
第五章 個案分析:臺大眼科部的「智慧轉型」 81
第一節 以VRIO分析臺大眼科部的「智慧轉型」 81
第二節 以PEST分析臺大眼科部的「智慧轉型」 84
第三節 以SWOT優劣法分析臺大眼科部的「智慧轉型」 86
第四節 以商業模式九宮格(Business Model Canvas)分析 91
第六章 研究結論和未來展望 94
第一節 研究結論 95
第二節 研究限制 97
第三節 未來展望 100
參考文獻 103
附錄 105
 
圖次
圖1-1 :本研究的架構與流程 7
圖2-1 :使用偵測式AI篩選眼科疾病的經濟評估流程 13
圖2-2 :「數位轉型」PIVOT五大流程思考 14
圖2-3 :企業轉型的C-SOP架構 19
圖3-1 :臺大醫院眼科部近五年門診看診人次及門診手術人次26
圖3-2 :臺大醫院眼科部近五年急診照會人數統計 28
圖3-3 :臺大眼科部門診高峰期的壅塞狀況 30
圖3-4 :臺大眼科部的門診公告系統 32
圖3-5 :門診區的反醫療暴力海報 33
圖3-6 :臺大眼科部目前數位化門診插卡報到及檢查流程圖 34
圖3-7 :臺大眼科部門診區病患報告狀況及插卡報到後的檢查單 35
圖3-8 :臺大眼科部門診區目前大小特殊檢查室病患插卡報到後的叫號系統和顯示螢幕 35
圖3-9 :臺大眼科部目前的「非結構化病歷」頁面 37
圖3-10:目前臺大眼科部建構中的「結構化病歷」頁面 38
圖3-11:臺大與宏碁聯手拿到首張眼科智慧藥證之新聞 42
圖3-12:臺大與宏碁聯手拿到首張眼科智慧藥證之記者會檔案照 42
圖3-13:宏碁智醫(6857)聯手臺大醫院合作開發「VeriSee AMD」眼科AI篩檢軟體 43
圖3-14:利用白內障病患的眼底圖預測病患眼軸長度 44
圖3-15:生成式AI在眼科醫學研究和論文發表上扮演的角色及重要考量 45
圖3-16:具備影響係數的國際眼科醫學期刊在投稿須知中明列生成式AI的相關規定 46
圖3-17:臺大眼科部團隊於2023到2024年多次應邀在國內外眼科界及內部演講介紹生成式AI的最新應用 46
圖3-18:臺大眼科部團隊2024年應邀在非眼科界(例如內科腫瘤學科界)內部演講介紹生成式AI的最新應用 47
圖3-19:臺大眼科部(2024年)在臺灣眼科教授醫學會演講生成式AI的部分演講內容 48
圖3-20:臺大眼科部受邀在國內醫學院介紹生成式AI 48
圖4-1 :訪談對象圖 49
圖4-2 :臺大眼科部健檢中心電子病歷系統 53
圖4-3 :Zeiss(蔡司)公司推出影像報告整理系統 53
圖4-4 :Zeiss(蔡司)公司推出雷射屈光手術數位整合系統 54
圖4-5 :Zeiss(蔡司)公司推出眼科檢查報告數位串接系統 54
圖4-6 :重新組圖進行數位化建檔 55
圖4-7 :數位轉型前白內障手術人工水晶體之度數登記方式 57
圖4-8 :數位轉型後白內障手術人工水晶體之度數登記方式 58
圖4-9 :臺大醫院的醫療整合資料庫可以協助整合病患資料 59
圖4-10:臺大醫院的醫療整合資料庫可以協助提供總院和分院的整合病患資料 59
圖4-11:臺大醫院的臨床試驗中心為各種研究所提供的支持 60
圖4-12:上海復旦大學的眼耳鼻喉科醫院數位管理平臺參訪 65
圖5-1 :臺大眼科部在智慧轉型上的PEST模式分析 84
圖5-2 :2024年臺大眼科部組成了跨校醫學生團隊投稿「生成式AI」學術文章的投稿證明 87
圖5-3 :國際科技大廠(AMD)公開國際大學合作招募計畫90
圖5-4 :臺大眼科部針對「智慧轉型」商品VeriSeeTM所分析的商業模式九宮格 93
圖 6-1 : 以Kotter 變革八部曲成為台大眼科部走向成功轉型的策略102
 
表次
表1-1:本研究採取個案研究法的策略 8
表3-1:近五年臺大眼科住院服務量、病房使用現況及住院手術人次 25
表3-2:臺大醫院眼科部2023年指標性手術統計量 27
表3-3:臺大醫院院方、資訊室和眼科部對於門診數位轉型的會議摘要 36
表3-4:臺大眼科部目前「結構化病歷」的發展狀況 38
表3-5:院方近期召開的結構化病歷會議記錄重點整理 39
表4-1:受訪者背景資料和訪談狀況 50
表5-1:臺大眼科部的(智慧轉型)在VRIO架構中的競爭力分析 82
表6-1:管理學對於醫院管理和醫學研究的意涵 95
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dc.language.isozh_TW-
dc.subject醫療體系zh_TW
dc.subject眼科zh_TW
dc.subject數位轉型zh_TW
dc.subject智慧轉型zh_TW
dc.subject人工智慧zh_TW
dc.subjectdigital transformationen
dc.subjectartificial intelligenceen
dc.subjectsmart transformationen
dc.subjectophthalmologyen
dc.subjecthealthcare systemen
dc.title醫療體系在數位智慧轉型上的實踐探討:眼科醫學中心個案研究zh_TW
dc.titleExploring the Practice of Digital and Smart Transformation in Healthcare System:A Case Study of an Ophthalmology Medical Centeren
dc.typeThesis-
dc.date.schoolyear113-1-
dc.description.degree碩士-
dc.contributor.coadvisor簡睿哲zh_TW
dc.contributor.coadvisorRuey-Jer Jeanen
dc.contributor.oralexamcommittee余峻瑜;吳玲玲zh_TW
dc.contributor.oralexamcommitteeJiun-Yu Yu;Ling-Ling Wuen
dc.subject.keyword數位轉型,人工智慧,智慧轉型,眼科,醫療體系,zh_TW
dc.subject.keyworddigital transformation,smart transformation,artificial intelligence,ophthalmology,healthcare system,en
dc.relation.page119-
dc.identifier.doi10.6342/NTU202500006-
dc.rights.note同意授權(限校園內公開)-
dc.date.accepted2025-01-03-
dc.contributor.author-college管理學院-
dc.contributor.author-dept碩士在職專班國際企業管理組-
dc.date.embargo-lift2025-02-25-
顯示於系所單位:國際企業管理組

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