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研究生:李佩珊
研究生(外文):Peisan Lee
論文名稱:雲端血壓: 轉譯個人化量測的治療方法
論文名稱(外文):Cloud BP-Translating personalized measurement to therapeutic strategy
指導教授:李友專李友專引用關係
指導教授(外文):Yu-Chuan Li
學位類別:博士
校院名稱:國立陽明大學
系所名稱:生物醫學資訊研究所
學門:生命科學學門
學類:生物化學學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:48
中文關鍵詞:居家血壓雲端科技醫療決策血壓控制
外文關鍵詞:Self blood pressure monitoringCloud technologyDecision making
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血壓的治療方式及治療目標值都在改變中,不過唯一沒改變且重要的就是病人要持續地量測血壓,此能有效協助醫師監控與治療高血壓。目前血壓計的發展雖已朝向雲端血壓計,但卻仍然無法與門診醫令系統整合,也造成醫師無法有效且迅速地掌握病人的血壓情況。有別於以往相關的試驗,本研究的重點有二項:
一、隨機對照臨床試驗 (Randomized Controlled Trial):目前已有多項將雲端技術應用於醫療相關領域的相關研究,然而,有別於其它試驗設計是用前測/後測評量試驗結果,其研究結果可能因霍桑效應(Hawthorne effect)而影響研究結果的可靠性,本研究設計採用隨機對照臨床試驗,並且在試驗設計中特別避免霍桑效應(Hawthorne effect),控制了可能改變試驗結果的其它因素,大大提升了試驗結果的可靠性及作為臨床證據的可行性。
二、與門診醫令結合:目前相關的雲端技術應用,使用者將血壓數據上傳於網站,這些資料生命週期 (Data life cycle) 僅限於個人健康管理,不易提供可信、可用之臨床參考,遑論提升病人的血壓控制;因此本試驗將雲端血壓系統與門診醫令系統進行整合,將結果直接回饋給治療決策者(醫師)以便有效地協助高血壓的監控與治療。
量測血壓的理想模式就是血壓計資料最終能與門診醫令系統做連結,醫師於病人就診時,直接從門診醫令系統就可以了解病人居家的血壓平均值與趨勢圖,完全不需要再另外登入其他系統或使用其他方式,但是相關的隨機對照臨床試驗所要投入的成本往往達數千萬,而本研究利用雲端技術的優勢,於北台灣兩所醫院心臟內科進行隨機對照臨床試驗,將雲端血壓計導入門診醫令系統,率先以實證醫學角度來說明整合雲端血壓計與門診醫令系統之結果,有益於更好的血壓控制。
Importance: Less than fifty percent of patients with hypertensive disease manage to maintain their blood pressure (BP) within normal levels. Practical (Pragmatic) and sustainable models are needed in order to improve BP management in patients with hypertension.
Objective: The main objective of this study is proving our hypothesis that the cloud computing may improve healthcare. To evaluate whether a Cloud-based BP measurement system integrated with computerized physician order entry (CPOE) can improve BP management as compared with traditional care.
Design, Setting and Patients: A randomized controlled trial was done on a random sample of 382 adults recruited from 786 patients who had been diagnosed with hypertension and receiving treatment for hypertension in two district hospitals in the north of Taiwan. Physicians had access to cloud-based BP data from CPOE. Neither patients nor physicians were blinded to group assignment. The study was conducted over a period of seven months.
Interventions: Patients from two hospitals were randomized into a control group that received traditional care (n=212) and an intervention group (n=170) that received a cloud-based home BP monitoring device. The device measured and transmitted BP data to a cloud server, which was integrated with the CPOE system in the hospitals. Physicians could access the BP data during the patient encounter to adjust the antihypertensive therapy accordingly. In addition, patients could also browse their own BP data from their computers, smart phones, and tablet computers.
Primary Outcome(s) and Measure(s): The primary outcome was “BP control”, which is measured as the proportion of patients within a pre-defined range at two, four and six months. The secondary outcome was “BP change” and “antihypertensive drug quantity” at two, four and six months.
Results: At baseline, the enrollees were 50% male with a mean (SD) age of 58.18 (10.83) years. The mean sitting BP of both arms was no different. The proportion of patients with BP control at two, four and six months was significantly greater in the intervention group than in the control group. The average capture rates of blood pressure in the intervention group was also significantly higher than the control group in all three check-points.
Conclusions and Relevance: In this study, we can demonstrate the cloud computing improves healthcare. Cloud BP system integrated with CPOE achieved better BP control compared with traditional care during the six months of intervention follow-up.
Clinical Trial Registration: ClinicalTrials.gov (NCT02175511)

中文摘要 i
Abstract iii
Content v
List of Tables viii
List of Figures ix
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 2
1.3 Barriers 3
1.4 Objective 3
1.5 Defination 3
Chapter 2 Literature Review 5
2.1 Important of self monitoring of blood pressure at home 5
2.2 Advantages of self monitoring at home 6
2.3 Chllaenge of patient compliance 6
2.4 Characteristics of patients with uncontrolled hypertension 7
2.5 Cloud computing in healthcare 8
2.6 Benefits of cloud computing in healthcare 9
2.7 Technologies for improving compliance 10
Chapter 3 Methods 11
3.1 Design and setting 11
3.2 Locations 12
3.3 Procedures 13
3.4 Interventions 14
3.5 Outcomes 17
3.6 Sample size and Statistical Analysis 17
Chapter 4 Results 19
4.1 Participant flow 19
4.2 Recruitment 20
4.3 Baseline data 21
4.4 Outcomes Measures 22
4.5 Outcomes and estimation 28
Chapter 5 Discussion 29
5.1 Discrepancy in self monitoring compliance between two groups 29
5.2 Effect of patient reminder in this study 30
5.3 Physicians behavior change for the better look at the data in the cloud? 31
5.4 The explaination of the non-significant difference in ABPM measurements and the mean number of prescrbied anti-hypertensive drugs between two groups 32
5.5 Contributions of this study 34
5.6 Limitations 35
5.6.1 Hawthorne effect 35
5.6.2 Interface Design 35
5.6.3 The limitation of a cloud technology application in this study 36
5.6.4 The advanced studies in the future 37
Chapter 6 Conclusions 38

List of Figures
FIGURE 1: THE ILLUSTRATION FOR THE FREQUENCY OF SELF MONITORING 17
FIGURE 2: THE ILLUSTRATION FOR THE FREQUENCY OF SELF MONITORING 18
FIGURE 3: WORK FLOW OF DEVICE (MICROLIFE, WATCHBP HOME) 20
FIGURE 4: BP REVIEW BUTTON ON THE CPOE SCREEN 20
FIGURE 5: TEXT REMINDERS FLOW CHART 21
FIGURE 6: PARTICIPANT RECRUITMENT, ENROLLMENT, AND FOLLOW-UP 27
FIGURE 7: CLOUD BP REVIEW BUTTON CLICK RATE 40

List of Tables
TABLE 1: BASELINE CHARACTERISTICS A 25
TABLE 2: BLOOD PRESSURE (BP) MEASUREMENT COMPLIANCE 28
TABLE 3: BLOOD PRESSURE (BP) CONTROL 29
TABLE 4: BLOOD PRESSURE (BP) REDUCTION FROM BASELINE 30
TABLE 5: PRESCRIPTION OF ANTIHYPERTENSIVE DRUGS 32
TABLE 6: AVERAGE OF CAPTURE RATE OF BLOOD PRESSURE READINGS (%) 33
TABLE 7: DISTRIBUTION OF 3C OPERATION ABILITY BETWEEN TWO GROUPS (%) 35
TABLE 8: ABPM (BASELINE/ 6-MONTH) RESULTS 38





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