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研究生:姚維正
研究生(外文):Wei-Cheng Yao
論文名稱:智能醫學科技公司實務研究報告-影響病人使用智能醫學照護系統意向之因子初探
論文名稱(外文):The Practicum Report of Smart Care Company- The Influencing Factors of Patients’ Intention to Use the Smart Care System
指導教授:張睿詒張睿詒引用關係
指導教授(外文):Ray-E Chang
口試委員:張怡秋侯穎蕙
口試日期:2013-07-14
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:公共衛生碩士學位學程
學門:醫藥衛生學門
學類:公共衛生學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:中文
論文頁數:116
中文關鍵詞:科技接受模式問卷資訊科技健康產業遠距照護
外文關鍵詞:technology acceptance model (TAM)questionnaireinformation technologyhealthcaretelecare
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背景: 過去醫院管理者引入資訊系統主要都運用於內部相關的營運面、作業面和管理面,其目的多以效率為考量,主要對象都是以員工為主。隨著”以病人中心”的理念的落實,和資訊科技的長足發展,使醫院管理者大量將資訊科技引入病人的照護,目的在於契合病人的需要及提升服務品質。
近年來,因為網際網路和智慧型手機的技術發達,更貼近病人的資訊科技產品更進一步深入照護領域,例如病人可以掛號和繳款免排隊的自動掛號繳款機(kiosk)、直接從手機或電腦就可以輕鬆掛號的網路掛號系統,與藉著網路連結雙向傳輸,使病人在家裡仍能夠將生理數據上傳醫院伺服器,醫院可以密切追蹤病人慢性疾病的控制,跨越時間和空間障礙的持續照護-「遠端照護(telecare)系統」,並獲致一些成果,但是應用在持續照護層面,仍面臨著民眾利用擴大與強化的難題。本研究的目的在於建構科技接受模式的結構式問卷,用以探討影響病人使用遠端照護裝置的因子。

方法: 本研究以Davis等於1989年依理性行為理論(TRA)為基礎,所發展的科技接受模型(TAM)為架構,來探討可能影響使用者對使用遠端照護系統之意向(intentions)的影響因子。問卷調查之結果運用統計軟體進行結構方程模式(SEM)的分析與研究。

結果: 本問卷調查研究的結果,除本研究所建構中文化醫療照護版的科技接受模式(TAM)的標準問卷與用語具有信度和效度外,發現知覺易用性、知覺有用性等內部因素和電腦自我效能、主觀規範等外部因素,可能直接或間接影響使用者的使用意向,且具統計上的顯著意義。

結論: 要能操作遠端照護系統需有其基本的電腦能力,一般而言,年齡、性別、教育程度等對電腦自我效能可能有不同程度的影響,內部因素如知覺易用性、知覺有用性則直接或間接影響病人的使用意向。未來若要進一步推廣遠端照護系統,必須降低病人使用的障礙,提升對病人健康照護的價值。


Background: The administrators of the hospitals used to apply information technology in the operational, financial and administrative aspects, and the main targets were internal customers. As the realization of the concept of patient- centered medicine and rapid progression of information technology in recent years, the administrators start to apply information technology more and more on the patient care. The main idea is to fit the demand of patients and improve the quality of care.
The prosperity of internet and smart mobile industry provide further penetration of patient- friendly information technology into the patient care. For example, the kiosk could reduce the waiting time of registration and billing; the internet registration system provides an easy and efficient way to make appointments with desired physicians than before; the telecare system enables the patients to upload physiological data to the server of the hospital, and the medical personnel could analyze the data and provide continuum of care beyond the boundary of time and distance. However, there are still a long way to achieve the wide application and successful implementation for the telecare system. The specific aim of this study is to find the influencing factors of utilizing telecare system.

Methods: Based on the technology acceptance model (TAM) proposed by Davis et al. and theory of reasoned action (TRA), we developed a structured model and questionnaire to measure the measureable and latent variables and constructs. The data analysis is performed by the statistic software based on the structural equation modeling (SEM).

Results: The reliability and validity of questionnaire and model are supported by the factor analysis. The results reveal the endogenous constructs such as perceived easy of use (PEOU) and perceived usefulness (POU), and the exogenous variables such as computer literacy and subjective norms influence the patient’s intention to use (ITU) the telecare system.

Conclusion: The telecare system requires the basic ability and passion of computer literacy which may be influenced by age, gender and education level. If we want to implement the telecare system successfully, to reduce the entering barrier and provide the health promotion are major issues.


口試委員會審定書..........................................i
謝辭…………………………iii
中英文摘要…………………………………………v
表目錄……………………………………………………………ix
圖目錄………………………………………………………………………...………xi
第一章 導論…………………………………………………………………………..1
第一節 實習單位特色與簡介……………………………………………………......1
第二節 文獻回顧………………………………………………………………...…...9
第三節 研究目的與研究問題………………………………………………………17
第二章 方法…………….…………………………………………………………...18
第一節 研究架構與假說……………………………………………………………18
第二節 研究設計……………………………………………………………………28
第三節 研究對象與分析分法………………………………………………………29
第四節 研究限制……………………………………………………………………43
第三章 結果…………………………………………………………………………44
第一節 問卷回收之結果……………………………………………………………44
第二節 問卷之信效度檢定…………………………………………………………46
第三節 病人對智能醫學照護系統之看法…………………………………………52
第四節 病人對智能醫學照護系統之使用意向及可能影響因素…………………65
第四章 討論…………………………………………………………………………84
第一節 結果討論……………………………………………………………………84
第二節 結論…………………………………………………………………………88
第三節 是否達成實習目的…………………………………………………………92
第四節 對於實務實習單位的建議與回饋…………………………………………93
第五節 相關政策上的意涵或政策建議……………………………………………94
參考文獻……………………………………………………………………………..95
中文部分………………………………………………………………………95
英文部分………………………………………………………………………97
附錄一: 本研究調查之問卷內容……………………………………………….…110
附錄二: 敏盛綜合醫院人體試驗委員會研究計畫通過函…………………….....115
附錄三 敏盛綜合醫院人體試驗委員會研究計畫變更通過函…………………..116


中文部分:
1.王怡舜,湯宗益,湯宗泰: 電子商務之服務品質衡量模式—以數位行銷為例。中華管理學報 第三卷 第三期 第75-94頁 民國九十一年(2002)
2.林佩欣: 老人退化量測平台之研究。台灣職能治療研究與實務 2008; 4(2): 129-138。
3.江哲光,侯傑泰: 應用結構方程模式之問題與謬誤。教育學報 1997;25(1)
4.李茂能: SEM 適配度指標的潛藏問題:最佳模式難求。測驗統計年刊 2008;第十六輯下期:p17-30。
5.李茂能: 圖解AMOS在學術研究之運用,第二版。台北,五南圖書出版公司,2011。
6.吳明隆: 論文寫作與量化研究,更新二版。台北,五南圖書出版公司,2010。
7.吳明隆,張毓仁: 結構方程模式實務應用密笈,第一版。台北,五南圖書出版公司,2010。
8.邱皓政: 結構方程模式Structural Equation Modeling- LISREL的理論、技術與運用。台北,雙葉書廊有限公司,2003。
9.侯穎蕙: 個人健康紀錄系統使用意向之影響因素探討 台大公共衛生學院醫療機構管理研究所博士論文,2009。
10.許嘉麟等: 智慧型高齡者照護設備科技接受問卷之內容效度—以「互動式隨身照護手錶」為例。台灣職能治療研究與實務 2008; 4(2): 104-115。
11.陳順宇: 結構方程模式 AMOS操作。台北,心理出版社股份有限公司,2007。
12.陳正昌、程炳林、陳新豐、劉子鍵: 多變量分析方法(Multivariate Analysis)-統計軟體應用。台北,五南圖書出版公司,2003。
13.陳寬裕,王政華: 論文統計分析實務: SPSS與AMOS的運用,第二版。台北,五南圖書出版公司,2010。
14.陳寬裕,王政華: 結構方程模式分析實務: AMOS的運用。台北,五南圖書出版公司,2010。
15.黃營杉,汪志堅編譯: 研究方法。(原書: Kerlinger FN, Lee HB: Foundations of Behavioral Research.)台北,華泰文化事業股份有限公司,2002。
16.楊志良主編: 健康保險,二版。台北,華格納企業有限公司,2010。
17.楊珺涵: 醫院導入RFID醫護人員之關鍵接受因素探討 台大公共衛生學院醫療機構管理研究所碩士論文,2009。
18.詹錦宏、洪志華: 醫療電子產業發展模式之研究—數位血壓計之案例。台灣職能治療研究與實務 2008; 4(2): 165-173。



英文部分:
1.Adler K, Harper R, and Hoyt R. Bridging the Gap: Electronic Health Information Exchanges Could Eliminate the silos of Information and Improve Care. Medical Economics. 2010; Dec 17: 52-56.
2.Agarwal R, Anderson C, Zarate J, Ward C. If we offer it, will they accept? Factors affecting patient use intentions of personal health records and secure messaging. J Med Internet Res. 2013;15(2):e43.
3.Ajzen, I., & Fishbein, M. Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addision-Wesley, 1975.
4.An JY. Theory development in health care informatics: Information and communication technology acceptance model (ICTAM) improves the explanatory and predictive power of technology acceptance models. Stud Health Technol Inform. 2006;122:63-7.
5.An JY, Hayman LL, Panniers T, Carty B. Theory development in nursing and healthcare informatics: a model explaining and predicting information and communication technology acceptance by healthcare consumers. ANS Adv Nurs Sci. 2007; 30(3):E37-49.
6.Asua et al.: Healthcare professional acceptance of telemonitoring for chronic care patients in primary care. BMC Medical Informatics and Decision Making 2012 12:139.
7.Bentler, P.M. and C.P. Chou, “Practical Issues in Structural Modeling,” Sociological Methods & Research, 16, 78-117(1987).
8.Berenson RA, Hammons T, Gans DN, Zuckerman S, Merrell K, Underwood WS, Williams AF. A house is not a home: keeping patients at the center of practice redesign. Health Aff (Millwood). 2008 Sep-Oct;27(5):1219-30.
9.Blakeman T, Chew-Graham C, Reeves D, Rogers A, Bower P. The Quality and Outcomes Framework and self-management dialogue in primary care consultations: a qualitative study. Br J Gen Pract. 2011; 1(591):e666-73.
10.Brant-Zawadzki M, Perazzo C, Afable RF. Community Hospital to Community Health System: A Blueprint for Continuum of Care. PEJ. 2011; Jan-Feb: 16-21.
11.Brown SA, Dennis AR, Venkatesh V. Predicting Collaboration Technology Use: Integrating Technology Adoption and Collaboration Research. Journal of Management Information Systems. 2010; 27(2), 9-53.
12.Brown SA, Venkatesh V, Kuruzovich J, Massey AP. Expectation confirmation: An examination of three competing models Organizational Behavior & Human Decision Processes. 2008; 105(1),52-66.
13.Browne MW, MacCallum RC, Kim CT, Andersen BL, Glaser R. When Fit Indices and Residuals Are Incompatible. Psychological Methods 2002;7(4): 403–21.
14.Brubaker LM, Picano E, Breen DJ, Marti-Bonmati L, Semelka RC. Health care systems of developed non-U.S. nations: strengths, weaknesses, and recommendations for the United States--observations from internationally recognized imaging specialists. AJR Am J Roentgenol. 2011; 196(1): W30-6.
15.Chi MJ, Lee CY,and Wu SC. Multiple morbidity combinations impact on medical expenditures among older adults. Arch Gerontol Geriatr. 2011 ;52(3):e210-4.
16.Chou CC, Chang CP, Lee TT, Chou HF, Mills ME. Technology Acceptance and Quality of Life of the Elderly in a Telecare Program. Comput Inform Nurs. 2013 May 30.
17.Chow SKY et al. Nurses’ perceptions and attitudes towards computerisation in a private hospital Journal of Clinical Nursing. 2011; 21: 1685–1696,
18.Cottrell E, Chambers R, O''Connell P. Using simple telehealth in primary care to reduce blood pressure: a service evaluation. BMJ Open. 2012 Oct 31;2(6).
19.Davis FD, Bagozzi RP, Warshaw PR. User acceptance of computer technology: A comparison of two theoretical models. Management Science.1989; 35(8), 982-1003.
20.Davis GL, Roberts WL. The healthcare burden imposed by liver disease in aging Baby Boomers. Curr Gastroenterol Rep. 2010 Feb;12(1):1-6.
21.Dy P, Morin PC, Weinstock RS. Use of Telemedicine to Improve Glycemic Management in a Skilled Nursing Facility: A Pilot Study. Telemed J E Health. 2013 Jun 12.
22.Evashwick CJ. The continuum of long-term cared, 3rd Ed. New York : Thomson/Delmar Learning, c2005
23.Farrell AM. Insufficient discriminant validity: A comment on Bove, Pervan, Beatty, and Shiu (2009). Journal of Business Research. 2010;63: 324–327.
24.Fishbein, M. A theory of reasoned action: Some applications and implications. Nebraska Symposium on Motivation, 1980; 27, 65-116.
25.Fleetcroft R, Steel N, Cookson R, Walker S, Howe A. Incentive payments are not related to expected health gain in the pay for performance scheme for UK primary care: cross-sectional analysis. BMC Health Serv Res. 2012;12:94.
26.Fornell C and Larcker DF. Evaluating Structural Equation Models with Unobservables and Measurement Error. Journal of Marketing Research. 1981;18:39-50.
27.Frambach RT. “An Integrated Model of Organizational Adoption and Diffusion of Innovations.” European Journal of Marketing 1993;27(5): 22-41.Gallacher K, May CR, Montori VM, and Mair FS. Understanding Patients’ Experiences of Treatment Burden in Chronic Heart failure Using Normalization Process Theory. Ann Fam Med. 2011;9(3): 235-43.
28.Gary TL, Batts-Turner M, Yeh HC, Hill-Briggs F, Bone LR, Wang NY, Levine DM, Powe NR, Saudek CD, Hill MN, McGuire M, Brancati FL. The effects of a nurse case manager and a community health worker team on diabetic control, emergency department visits, and hospitalizations among urban African Americans with type 2 diabetes mellitus: a randomized controlled trial. Arch Intern Med. 2009 Oct 26;169(19):1788-94.
29.Gary TL, Batts-Turner M, Bone LR, Yeh HC, Wang NY, Hill-Briggs F, Levine DM, Powe NR, Hill MN, Saudek C, McGuire M, Brancati FL. A randomized controlled trial of the effects of nurse case manager and community health worker team interventions in urban African-Americans with type 2 diabetes. Control Clin Trials. 2004 Feb; 25(1):53-66.
30.Gary TL, Maiese EM, Batts-Turner M, Wang NY, Brancati FL. Patient satisfaction, preventive services, and emergency room use among African-Americans with type 2 diabetes. Dis Manag. 2005 Dec;8(6):361-71.
31.Glaser M, Winchell T, Plant P, Wilbright W, Kaiser M, Butler MK, Goldshore M, Magnus M. Provider satisfaction and patient outcomes associated with a statewide prison telemedicine program in Louisiana. Telemed J E Health. 2010; 16(4):472-9.
32.Goodwin N. Where next for telehealth? Reflections from the 2nd International Congress on Telehealth and Telecare. Int J Integr Care. 2012;12(Suppl 1):e104.
33.Gorsuch RL. Factor Analysis, 2nd Edition, 1983. L. Erlbaum Associates Inc.
34.Gozu A, Nidiry MA, Natanawongsa N, Carrese JA, and Wright SM. Patient Factors Associated with Following a Relocated Primary Care Provider Among Older Adults. Am J Manage Care. 2009; 15(2):195-200.
35.Hair Jr JF, Black WC, Babin BJ, Anderson RE. Multivariate Data Analysis, 7th Edition, 2009. Englewood Cliffs, N. J. Prentice Hall. Chapter 12.
36.Holden RJ, Karsh BT. THE TECHNOLOGY ACCEPTANCE MODEL: ITS PAST AND ITS FUTURE IN HEALTH CARE. J Biomed Inform. 2010; 43(1): 159.
37.Huang JC, Lee YC. Model construction for the intention to use telecare in patients with chronic diseases. Int J Telemed Appl. 2013; 2013:650238.
38.Igbaria, M., Iivari, J., & Maragahh, H. Why do individuals use computer technology? A Finnish case study. Information & Management, 1995; 29(5), 227-238.
39.Katzman S. Becoming patient: a path to effective participation with chronic terminal cancer. Health Care Women Int. 2013;34(1):68-85.
40.Krousel-Wood MA, Re RN, Abdoh A, Bradford D, Kleit A, Chambers R, Altobello C, Ginther B, Gomez N. Patient and physician satisfaction in a clinical study of telemedicine in a hypertensive patient population. J Telemed Telecare. 2001;7(4):206-11.
41.Lim S et al. A study on Singaporean women’s acceptance of using mobile phones to seek health information. International journal of medical informatics. 2011; 80: e189–e202.
42.Morales-Asencio et al. Design of a Case Management Model for People with Chronic Disease (Heart Failure and COPD). Phase I: Modeling and Identification of the Main Components of the Intervention through Their Actors: Patients and-PRO study). 2010; 10:324.
43.Morton ME, Wiedenbeck S. A framework for predicting EHR adoption attitudes: a physician survey. Perspect Health Inf Manag. 2009 Sep 16;6:1a.
44.Okazaki S, Castañeda JA, Sanz S, Henseler J. Factors affecting mobile diabetes monitoring adoption among physicians: questionnaire study and path model. J Med Internet Res. 2012 Dec 21;14(6):e183.
45.Orruño E, Gagnon MP, Asua J, Ben Abdeljelil A. Evaluation of teledermatology adoption by health-care professionals using a modified Technology Acceptance Model. Telemed Telecare. 2011;17(6):303-7.
46.Peeters JM, de Vee AJ, van der Hoek L, Francke AL. Factors influencing the adoption of home telecare by elderly or chronically ill people: a national study. J Clin Nurs. 2012; 21(21-22): 3183-93.
47.Peterson GE. A checklist Approach to Selecting the Optimal Treatment Regimen for a Patient with Type 2 Diabetes. J Fam Pract. 2009; 58(9);S21-S25.
48.Poggio F. Why Hospitals Fail at Lean. Hospitals and Health Network. 2010; Mar 11.
49.Prestigiacomo J. Putting image-sharing in the patient''s hands. Healthc Inform. 2012; 29(9):14, 16-7.
50.Rogers, EM. Diffusion of Innovations. New York, The Free Press, 1995.
51.Ruesch C, Mossakowski J, Forrest J, Hayes M, Jahrsdoerfer M, Comeau E, Singleton M. Using nursing expertise and telemedicine to increase nursing collaboration and improve patient outcomes. Telemed J E Health. 2012 Oct;18(8):591-5.
52.Sanders KA, Whited A, Martino S. Motivational interviewing for patients with chronic kidney disease. Semin Dial. 2013; 26(2):175-9.
53.Saultz JW, Heineman J, Seltzer R, Bunce A, Spires L, and DeVoe J. Uninsured Patient opinions about a Reduced-Fee Retainer Program at Academic Health Center Clinics. J Am Board Fam Med 2011;24: 304-12.
54.Sheehan KB. “Toward a Typology of Interested User and Online Privacy Concerns.” The Information Society. 2002; 18: 21-32.
55.Sibley LM, Weiner JP. An Evaluation of Access to Health Care Services Along the Rural –urban Continuum in Canada. BMC Health Services Research. 2011; 11:20.
56.Sledge WH, Brown KE, Levine JM, Fiellin DA, Chawarski M, White WD, O''connor PG. A randomized trial of primary intensive care to reduce hospital admissions in patients with high utilization of inpatient services. Dis Manag. 2006 Dec;9(6):328-38.
57.Tao D. Intention to use and actual use of electronic information resources: further exploring Technology Acceptance Model (TAM). AMIA Annu Symp Proc. 2009:629-33.
58.Tung FC, Chang SC, Chou CM. An extension of trust and TAM model with IDT in the adoption of the electronic logistics information system in HIS in the medical industry. Int J Med Inform. 2008;77(5):324-35.
59.Venkatesh V, Bala H.Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences. 2008;39(2),273-315.
60.Venkatesh V, L. Thong JY, Xu X. CONSUMER ACCEPTANCE AND USE OF INFORMATION TECHNOLOGY: EXTENDING THE UNIFIED THEORY OF ACCEPTANCE AND USE OF TECHNOLOGY. MIS Quarterly. 2012; 36(1), 157-178.
61.Venkatesh, V, Morris MG. WHY DON''T MEN EVER STOP TO ASK FOR DIRECTIONS? GENDER, SOCIAL INFLUENCE, AND THEIR ROLE IN TECHNOLOGY ACCEPTANCE AND USAGE BEHAVIOR. MIS Quarterly. 2000; 24(1), 115-139.
62.Venkatesh V. CREATION OF FAVORABLE USER PERCEPTIONS: EXPLORING THE ROLE OF INTRINSIC MOTIVATION. MIS Quarterly. 1999; 23(2), 239-260.
63.Venkatesh V, Davis FD. A Theoretical Extension of the Technology: Longitudinal Field Studies. Management Science; 2000:46:186-204.
64.Venkatesh V. Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model. Information Systems Research. 2000; 11(1), 342.
65.Venkatesh V, Morris MG, Davis GB, Davis FD. USER ACCEPTANCE OF INFORMATION TECHNOLOGY: TOWARD A UNIFIED VIEW. MIS Quarterly. 2003: 27(3), 425-478.
66.Venkatesh V, Davis FD. A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science. 2000; 46(2), 186-204.
67.Venkatesh V, Brown SA, Maruping LM, Bala H. PREDICTING DIFFERENT CONCEPTUALIZATIONS OF SYSTEM USE: THE COMPETING ROLES OF BEHAVIORAL INTENTION, FACILITATING CONDITIONS, AND BEHAVIORAL EXPECTATION. MIS Quarterly. 2008; 32(3), 483-502.
68.Venkatesh V, Davis FD. A Model of the Antecedents of Perceived Ease of Use: Development and Test. Decision Sciences. 1996; 27(3), 451-481.
69.Valluzzi JL, Larson SL, Miller GE. Indications and Limitations of Structural Equation Modeling in Complex Surveys: Implications for an Application in the Medical Expenditure Panel Survey (MEPS). Joint Statistical Meetings. Section for survey research methods. 2003,P4345~56.
70.William H. Sledge, Karen E. Brown, Jeffrey M. Levine, David A. Fiellin, Marek Chawarski, William D. White, Patrick G. O''Connor. A Randomized Trial of Primary Intensive Care to Reduce Hospital Admissions in Patients with High Utilization of Inpatient Services. Disease Management. December 2006, 9(6): 328-338.
71.West, S. G., Finch, J. F. and P. J. Curran, “Structural Equation Models with Nonnormal Variables: Problems and Remedies,” In R. H. Hoyle (Ed.), Structural Equation Modeling: Concepts, Issues, and Applications, Thousand Oaks, CA: Sage, 56-75(1995)
72.Wilhelmson K, Duner A, Eklund K, Gosman-Hedstrom G, Blomberg S, Hasson H, Gustafsson H, Landahl S, Dahlin-Ivanoff S. Continuum of care for frail elderly people: Design of a randomized controlled study of a multi-professional and multidimensional intervention targeting frail elderly people. BMC Geriatr. 2011 May 14;11(1):24.
73.Wong AMK, Chang W-H, Ke P-C, Huang C-K, Tsai T-H, et al. Technology Acceptance for an Intelligent Comprehensive Interactive Care (ICIC) System for Care of the Elderly: A Survey-Questionnaire Study. PLoS ONE 2012; 7(8): e40591.
74.Woolhouse S, Brown JB, and Thind AT. “Meeting People Where They’re At’: Experiences of family Physicians Engaging Women Who Use Illicit Drugs. Ann Fam Med. 2011;9(3):244-9.
75.Zait A & Bertea PE. METHODS FOR TESTING DISCRIMINANT VALIDITY. Management & Marketing. 2011;9(2): 217-226.


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