本研究目的是(1)以民眾健康語彙為基礎，收集及分析長者的健康語彙；以國內外已廣泛使用的基本資料庫評估量表為例，進行健康語彙比對，發展符合長者使用的基本資料庫自評量表；(2)將基本資料庫自評量表建置於行動設備，測試長者隨身使用進行健康自我評估的可行性。國外學者收集民眾健康語彙(Consumer Health Vocabulary)並與醫學語彙(medical terminology)做比對，希望能建立一套語彙比對系統，協助民眾與專業間醫療溝通。基本資料庫(Minimum Data Set)是一套收集長期照護健康需求並擬定照護計畫的有用工具，目前美國已廣泛使用並朝向結合個人電子病歷方向努力。由於行動健康(mobile health)在收集健康資訊及銜接後端照護系統方面具有及時性及有效性之功能；因此，本研究以基本資料庫為例，運用民眾健康語彙概念發展行動健康自我評估系統，探討長者參與行動健康自我評估模式的可行性。
本研究為質量性研究，研究方法有問卷調查及訪談。第一階段分析基本資料庫語彙內容與長者健康概念訪談結果進行比較，發展基本資料庫長者自評量表。研究中邀請臨床醫護專家檢視基本資料庫-護理之家2.1臺灣版，進行篩選評估問題及提供民眾健康語彙；研究者建立1份長者自評量表的5-Likert Scale調查問卷(5-非常清楚、1-非常不清楚)，再將長者清楚的評估項目與基本資料庫-護理之家3.0版比對後，發展出1份基本資料庫3.0版自評量表，並完成信度檢定。第二階段在不同螢幕大小行動設備(3.7吋和6吋)建置基本資料庫3.0版自評量表，由機構住民參與自我評估行動健康模式，測試其使用的可行性。研究中兩所護理之家住民使用行動設備進行每週1次連續3週的自我健康評估並完成科技接受模式(Technology Acceptance Model)問卷調查。
10位臨床醫護專家從基本資料庫-護理之家2.1版中的325個問題，遴選出152個(46.8%)民眾能理解的問題，研究者將此152個評估問題讓長者進行問卷調查。研究結果，參與調查的長者(n=48)表示能清楚基本資料庫-護理之家2.1臺灣版中115個問題；另外，長者(n=28)填答的基本資料庫3.0版自評量表與護理人員的評估結果具有一致性。行動健康自我評估方面，住民(n=51)反應行動設備有助於健康紀錄且容易學習，並認為較大螢幕的行動設備(6吋)易學習及使用(96%、>80%)；其中，僅10位住民(20%)能操作螢幕較小的行動設備(3.7吋)進行自我評估，且對較小螢幕的行動設備的易用性有較低的評價(20%、30%、30%)。此現象顯示較大螢幕行動設備(6吋)較適合長者進行自我評估。
本研究指出長者參與部分基本資料庫自我評估是可行的，且可將此自評量表建置在較大螢幕(6吋)行動設備讓長者可以隨時進行自我評估、將資料回傳進行分析及回饋解決長者健康問題；透過長者主動參與自我評估可以有效收集健康資料及提升照護成果，也可以減輕護理人員工作負擔及精進資料收集品質。

The purpose of this research was to encourage patient engagement in older adults by modifying the Minimum Data Set for nursing homes (MDS 3.0) with a Consumer Health Vocabulary (CHV) that would enable older adults to perform a self-assessment of their health status and to evaluate whether nursing home residents could capably perform a self-assessment with a self-assessment MDS 3.0 using a handheld mobile device. Comprehensive geriatric assessment of nursing home residents, such as the MDS 3.0, has been widely used to collect data on resident’s clinical and psychological status as well as their personal needs in preferences, satisfaction, and daily life events. This process requires that residents be interviewed directly by nursing staff, which is time consuming and adds to the cost of patient care. Patient engagement has been shown to improve both outcomes and care coordination and is an important focus of the Medicare and Medicaid Electronic Health Records Incentive Program, Stage 2, of Meaningful Use.
In the first phase of this study the MDS 3.0 was simplify with an abbreviated format that would allow older adults to perform a self-assessment. A group of experience health care providers evaluated the 325 items of MDS-NH 2.1 and screened out 152 (48.2%) items to be candidate self-assessment items of elders based on CHV. 48 older adults determined the MDS-NH 2.1 questions that they could understand 115 items with a 5 point Likert-Scale. There was a result of 171 items as a self-assessment tool for elderly when mapping to MDS 3.0. The language of the abbreviated MDS 3.0 was enhanced with a CHV, allowing the participants to easily understand the meaning of the items in the self-assessment. This self-assessment MDS 3.0 was tested with a second set of participants (n=28) to ensure the validity of the instrument. Twenty-two of these items were subsequently excluded, as they were considered to be too complex and/or difficult for a self-assessment format, resulting in a total of 149 items (40.7% of the original) and 13 sections.

In the second phase of the study 51 participants from two nursing homes in Taiwan used mobile devices to perform the self-assessment. The self-assessment MDS 3.0 was converted to a format for use with both handheld devices with different display size (6-inch and 3.7-inch). Participants answered the assessment questions using the mobile device, on their own, once every week for three weeks with each device and the completion rate and completion time was measured. A Technology Acceptance Model questionnaire (TAM) was designed to analyze the participants’ experience. All participants were able to use the device with larger display, with an average completion rate of 92.9% and an average completion time of 21 minutes. Only 20% of the participants could complete the assessment with the device with smaller display. All participants preferred the large one, as they found it easier to use.
These studies suggest that older adults are capable of engaging in a self-assessment of their health status and older nursing home residents are able to perform this self-assessment utilizing a mobile device. It is important that a compatible device with suitable display size and interface design for them, is available. This form of resident engagement has the potential to reduce the workload of the health care staff in nursing homes as well as potentially reduce the cost of patient care in these facilities.

Content

Acknowledgements i
Chinese Abstract iii
English Abstract v
List of Tables xi
List of Figures xii
CHAPTER 1 INTRODUCTION AND STATEMENT OF THE PROBLEM 1
1.1 Background 2
1.2 Statement of the problem 15
1.3 Research Questions 16
1.4 Significance of the Study 17
1.5 Glossary 19
1.6 Limitations of the study 21
1.7 Organization of the dissertation 22
CHAPTER 2 THE REVIEW OF RELATED LECTURE 23
2.1 Health consumer vocabulary 23
2.2 Mobile health 25
2.3 Patient engagement and Meaningful Use 28
2.4 Comprehensive Geriatric Assessment 31
CHAPTER 3 RESEARCH DESIGN AND METHDOLOGY 36
Stage 1-The Consumer Health Vocabulary and Self-assessment Items of the MDS 3.O 37
3.1.1 Study design 37
3.1.2 Participants and background 38
3.1.3 Study flow and procedure 39
3.1.4 Data collection and analysis 40
Stage 2- Self-assessment Items of the MDS 3.0 with a Handheld Mobile Device 41
3.2.1 Study design 42
3.2.2 Ethics approval 42
3.2.3 Participants 42
3.2.4 The self-assessment items of MDS 3.0 43
3.2.5 The Long-Term Care Comprehensive Geriatric Self-Assessment (CGSA) mHealth 45
3.2.6 The perception survey 47
3.2.7 The study procedure 48
3.2.8 Data collection and analysis 50
CHAPTER 4 RESULTS 52
Stage 1-The Consumer Health Vocabulary and Self-assessment Items of the MDS 3.O 52
4.1.1 The candidate items of MDS 2.1 for the elders 52
4.1.2 The self-assessment items of MDS 3.0 57
Stage 2- Self-assessment Items of the MDS 3.0 with a Handheld Mobile Device 61
4.2.1 Participant characteristics 61
4.2.2 System usability 65
CHAPTER 5 DISCUSSION 72
5.1 Self-assessment items for MDS based on Consumer Health Vocabulary 72
5.2 The feasibility of the Long-Term Care CGSA mHealth 74
5.3 The interface of the Long-Term Care CGSA mHealth 77
5.4 Resident engagement and self-assessment using a CGSA mHealth 79
CHAPER 6 CONCLUSION 82
REFERENCES 85
APENDIX I – Consumer Health Vocabulary and the MDS-NH 2.1 99
APENDIX II – The MDS-NH 2.1 Self-assessment Items Questionnaire 103
APENDIX III – The MDS 3.0 Self-assessment Items 113
APENDIX IV – The Longn-Term Care CGSA mHealth Items 123
APENDIX V – TAM Survey 135

List of Tables
Table 1. The studies of Consumer Health Vocabulary 24
Table 2. The self-assessment items of MDS3.0 that the elderly were able to understand. 45
Table 3. The characteristics of the elderly participants in considering candidate items 52
Table 4. The characteristics of the elderly participants in testing elderly self-assessment items study 53
Table 5. The candidate item counts of the MDS-NH 2.1 for elderly people 54
Table 6. The comparison of items between the MDS-NH 2.1 and the MDS-NH 3.0 56
Table 7. The item counts of the MDS-NH 3.0 elderly self-assessment items 58
Table 8. Residents’ demographic data and the homogeneity testing in two groups. 62
Table 9. The number of times of use and the time consumed for residents to enter their health data using a mobile device. 63
Table 10. The TAM survey: NH residents' perceptions of large display for mHealth, % agreement 67
Table 11. Associated factors for participants’ completion rate (CR) with demographics, completion time (CT) and frequency of use 68
Table 12. Significance of residents' perception between the small and large display 70

List of Figures
Figure 1-Development of a CHV using the UMLS 25
Figure 2-The study design and process 36
Figure 3-The study flowchart for developing CGSA items 38
Figure 4-The included criteria of elderly participants 39
Figure 5-The section H items of the MDS-NH 3.0 40
Figure 6-Two screenshots of the Long-Term Care CGSA mHealth 47
Figure 7-The study flowchart for testing the Long-Term Care CGSA mHealth 50
Figure 8-The procedure of considering candidate items in the study 54
Figure 9-The considering process of the MDS-NH 3.0 Elderly Self-assessment Items 60
Figure 10-The elder residents reported their health status using devices with a large (left) verses a small (right) display. 65
Figure 11- The Completion Rate (CR) and Time for participants using (A) the large display for CGSA mHealth, (n=51) and (B) the participants able to use both the large and small display for mHealth (B). 66
Figure 12-Analysis of the completion rate of residents using both mobile devices for three times. 67
Figure 13-TAM residents’ perception of using the mobile pad with large display. 70

REFERENCES
Abrams, W. B., Beers, M. H., &; Berkow, R. (1995). Comprehensive geriatric assessment. The Merck Manual of Geriatrics (2nd ed., pp.224-235). New Jersey: MERCK.
Adams, D. A., Nelson, R. R., &; Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology: a replication. MIS Quarterly 16, 227–247.
Beck, R. S., Daughtridge, R., &; Sloane, P. D. (2002). Physician-patient communication in the primary care office: a systematic review. Journal of the American Board of Family Medicine 15 (1), 25-38.
Bernabei, R., Landi, F., Onder, G., Liperoti, R., &; Gambassi, G. (2008). Second and third generation assessment instrument: the birth of standardization in geriatric care. Journal of Gerontology: Medical Sciences 63A (3), 308-313.
Blechman, E. A. (2009). Personal health records for older adults with chronic conditions and their informal caregiver. Aging Families and Care Giving. (pp.287-310). New Jersey: John Wiley &; Sons Inc.
Blummenthal, D., &; Tavenner, M. (2010). The “Meaningful Use” Regulation for Electronic Health Records. The New England Journal of Medicine 363, 501-504.
Brennan, P. F., &; Aronson, A. R. (2003). Towards linking patients and clinical information: detecting UMLS concepts in e-mail. Journal Biomedicine Informatics (36), 334-341.
Centers for Disease Control and Prevention (2013). The State of Aging and Health in America 2013. Atlanta, GA: Centers for Disease Control and Prevention, US Dept of Health and Human Services.
Center for Medicare and Medicaid Innovation (2010, May 13). Health Reform GPS, Retrieved from http://healthreformgps.org/resources/center-for-medicare-and-medicaid-innovation/
Chang, P., Hsu, C-L, Liou, Y-M, Kuo, Y-Y, &; Lan, C-F (2011). Design and development of interface design principles for complex documentation using PDAs. Computers, Informatics, Nursing, 29(3), 174–183.
Chery, M. S., Samuellsson, G., Svensson, T., &; Dehlin, O. (1999). Social relationships and health as predictors of life satisfaction in advanced old age: Results from a Swedish longitudinal study. International Journal of Aging and Human Development, 48(4), 301-324.
Craen, K. V., Braes, T., Wellens, N., Denhaerynck, K., Flamaing, J., Moons, P. …Milisen, K. (2010). The effectiveness of inpatient geriatric evaluation and management units: a system review and meta-analysis. The Journal of American Geriatrics Society, 58, 83-92.
DesRoches, CM., Audet, AM., Painter, M., &; Donelan, K. (2013). Meeting meaningful use criteria and managing patient populations: a national survey of practicing physicians. Annals of Internal Medicine, 158(11), 791-9.
Davis, F. D., Bagozzi, R. P., &; Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35, 982-1003.
Devons, C. A. (2002). Comprehensive geriatric assessment: making the most of the aging years. Current Opinion in Clinical Nutrition Metabolic Care, 5(1), 19-24.
Drenkard, K. (2014). Patient Engagement: Essential Partnerships to Improve Outcomes. Journal of Nursing Administration, 44(1), 3-4.
Fisk, A. D., Roger, W. A., Charness, N., Czaja, S. J., &; Sharit, J. ( 2009). Designing for Older Adults. New York, Boca Raton: Taylar &; Francis group.
Free, C., Phillips, G., Watson, L., Galli, L., Felix, L., Edwards, P., Patel, V., &; Haines, A. (2013). The effectiveness of mobile-health technologies to improve health care service delivery processes: a systematic review and meta-analysis. Public Library of Science, 10(1), e1001363.
Fontecha, J., Hervás, R., Bravo, J., &; Navarro, F. J. (2013). A mobile and ubiquitous approach for supporting frailty assessment in elderly people. Journal of Medical Internet Research, 15(9), e197.
Folstein, M., Folstein, S. E. &; McHugh, P. (1975). “Mini-mental state”: A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189-198.
Genitsaridi, I., Kondylakis, H., Koumakis, L., Marias, K., &; Tsiknakis, M. (2013). Towards intelligent personal health record systems: review, criteria and extensions. Procedia Computer Science, 21, 327-334.
George, L. K., &; Clipp, E. C. (1991). Subjective components of aging well. Generations, 15, 57-60.
Harris, Y., &; Clauser, S. B. (2002). Achieving improvement through nursing home quality measurement. Health Care Financing Review, 23(4), 5-18.
Hennekens, C. H., &; Buring, J. E. (1987). Epidemiology in Medicine. Boston: Lippincott Williams &; Wilkins.
Hilleras, P. K., Jorm, A., Herlitz, A., &; Winbland, B. (2001). Life satisfaction among the very old: A survey on a cognitively intact sample aged 90 years or above. International Journal of Aging and Human Development, 52(1), 71-90.
Horrigan, J. B., &; Rainie, L. (2013). Getting serious online, Pew Internet &; American Life Project, Washington, DC, Available at: http://www.pewinternet.org/pdfs/PIP_Getting_Seniors_Online3ng.pdf, 2013.
Houdt, S. V., Lepeleire, J. D., Driessche, K. V., Thijs, G., &; Buntinx, F. (2011). Multidisciplinary team meetings about a patient in primary care: an explorative study. Journal of Primary Care &; Community Health, 2(2), 72-76.
Huang, F., &; Chang, P. (2011). The feasibility of building a self-report component for the Taiwan version of MDS. The Journal of Taiwan Association for Medical Informatics, 20(4), 37-51.
Huh, J., Le, T., Reeder, B., Thompson, H. J., &; Demiris, G. (2013). Perspectives on wellness self-monitoring tools for older adults. International Journal of Medical Informatics, 82 (11), 1092-1103.
Huss A., Stuck, AE., Rubenstein, LZ., Egger, M., &; Clough-Gorr, KM. (2008). Multidimensional preventive home visit programs for community-dwelling older adults: a systematic review and meta-analysis of randomized controlled trials. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 63(3), 298-307.
Jethwani, K., Ling, E., Mohammed, M., Myint-U, K., Pelletier, A., &; Kvedar, J.C. (2012). Diabetes connect: an evaluation of patient adoption and engagement in a web-based remote glucose monitoring program. Journal of Diabetes Science Technology, 6(6), 1328-36.
Joe, J., &; Demiris, G. (2013). Older adults and mobile phones for health: A review, Journal of Biomedical Informatics, 46(5), 947-954.
Juva, K., Sulkava, R., Erkinjuntti, T., Ylikoski, R., Valvanne, J. &; Tilvis, R (1995). Usefulness of the clinical dementia rating scale in screening for dementia. International Psychogeriatrics, 7, 17-24.
Kane, R.A., &; Kane, R.L. (1998). Long-term care: Principle, programs and policies. New York: Springer.
Kane, R. L., Ouslander, J. G., &; Abrass, I. B. (1999). Evaluating the geriatric patient. Essentials of Clinical Geriatrics. (4th ed, pp. 43-73). New York: McGraw-Hill.
Keselman, A., Smith, C.A., Divita, G., Kim, H., Browne, A.C., &; Leroy, G. (2008). Consumer health concepts that do not map to the UMLS: where do they fit? Journal of the American Medical Informatics Association, 15(4), 496-505.
Kharrazi, H., Chisholm, R., VanNasdale, D., Thompson, B. (2012). Mobile personal health Records: An evaluation of features and functionality. International Journal of Medical Informatics, 81 (4), 579-593.
Kroenke, K., Spitzer, R.L., &; Williams, J.B. (2001). The PHQ-9: validity of a brief depression severity measure. Journal of General Internal Medicine, 16 (9), 606–613.
Kyriacou, E. C., Pattichis, C. S., &; Pattichis, M. S. (2009). An overview of recent health care support systems for eEmergency and mHealth applications. Conference Proceeding IEEE of Engineering in Medicine Biology Society, 1246-1249.
Lavarreda, S. A., Brown, E. R., &; Bolduc, C. D. (2011). Underinsurance in the United States: an interaction of costs to consumers, benefit design, and access to care. Annual Review of Public Health 32, 471-82.
Lee, JS., Chau, PP., Hui, E., Chan, F., &; Woo, J. (2009). Survival prediction in nursing home residents using the Minimum Data Set subscales: ADL Self-Performance Hierarchy, Cognitive Performance and the Changes in Health, End-stage disease and Symptoms and Signs scales. European Journal Public Health, 19(3), 308-312.
Legris, P., Ingham, J., &; Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information &; Management, 40(3), 191–204.
Luft, H. S. (2012). From small area variations to accountable care organizations: how health services research can inform policy. Annual Review of Public Health 33, 377-392.
Min, L., Yoon, W., Mariano, J. Wenger, N.S., Elliott, M.N., Kamberg, C., &; Saliba, D. (2007). The vulnerable elders-13 survey predicts 5-year functional decline and mortality outcomes in older ambulatory care patients. Journal of the American Geriatrics Society, 57(11), 2070-2076.
Ministry of Health and Welfare (2013, September 22). 2013 Taiwan Public Health Report. Retrieved from http:// http://www.mohw.gov.tw/EN/Ministry/DM2.aspx?f_list_no=46673
Ministry of the Interior. (2013). Interior national Indicator. Retrieved from http://www. moi.gov.tw/stat/index.asp
Ministry of the Interior (2014). Monthly Bulletin of Interior Statistics. Retrieved from http://sowf.moi.gov.tw/stat/month/elist.htm
Monsen, K., Honey, M., &; Wilson, S. (2010). Meaningful use of a standardized terminology to support the electronic health record in New Zealand. Applied Clinical Informatics, 1(4), 368-76.
Morris, J. N., Hawes, C., &; Fries, B. E., et al. (1990). Designing the national resident assessment instrument for nursing homes. The Gerontologist, 30(3) 293-302.
Morris, J. N., Murphy, K., Nonemaker, S., &; Ikegami, N. (2002). Resident Assessment Instrument MDS Version 2.1. Taiwan Taipei: National Health Research Institutes.
Narcisse, MR., Kippenbrock, TA., Odell, E., &; Buron, B. (2013). Advanced Practice Nurses' Meaningful use of electronic health records. Applied Nursing Research, 26(3), 127-32.
Ouslander, J. G. (1997). The resident assessment instrument (RAI): promise and pitfalls. Journal of the American Geriatrics Society 45(8), 975-976.
Outcome and Assessment Information Set (OASIS) (2012, May 24) retrieved from http://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/OASIS/index.html.
Pak, R., &; Mclaughlin, A. (2011). Designing Displays for Older Adults. New York, Boca Raton: Taylar &; Francis group.
Parker, S. J., Jessel, S., Richardson, J. E., &; Reid, M.C. (2013). Older adults are mobile too! Identifying the barriers and facilitators to older adults' use of mHealth for pain management. BMC Geriatrics 13 (43) doi: 10.1186/1471-2318-13-43.
Phiriyapokanon, T. (2011). Is a big button interface enough for elderly users? Towards user interface guidelines for elderly users. A thesis submitted in fulfillment of the requirements for the degree of Master of Computer Engineer of Mälardalen University, Sweden.
Piper, A. M., &; Holland, J. D. (2013). Supporting medical communication for older patients with a shared touch-screen computer. International Journal of Medical Informatics, 82 (11), 1035-1036.
Proulx, J., Kandula, S., Hill, B., &; Zeng-Treitler Q. (2013). Creating consumer friendly health content: Implementing and testing a readability diagnosis and enhancement tool. The 46th Hawaii International Conference on Systems Science (HICSS- 46 2013), 2445-2453.
Rahman, A. N. &; Applebaum, R. A. (2009).The nursing home Minimum Data Set assessment instrument: manifest functions and unintended consequences--past, present, and future. The Gerontologist, 49(6), 727-735.
Reti, S. R., Feldman, H. J., Ross, S. E., &; Safran, C. (2010). Improving personal health records for patient-centered care. Journal of the American Medical Informatics Association, 17, 192-195.
Reuben, D.B. (1999). Principles of geriatric assessment. Principles of Geriatric Medicine and Gerontology (4th ed., pp.467-481). New York: McGraw-Hill.
Riley, W. R., Rivera, D. E., Atienza, A. A., Nilsen, W., Allison, S. M., &; Mermelstein, R. (2011). Health behavior models in the age of mobile interventions: are our theories up to the task? Translational Behavioral Medicine, 1, 53-71.
Rosales, A. R., Byrne, D., Burnham, C., Watts, L., Clifford, K. Zuckerman, D. S. &; Beck, T. (2013). Comprehensive survivorship care with cost and revenue analysis. Journal of Oncology Practice, doi: 10.1200/JOP.2013.000945.
Rubenstein, L.Z., Stuck, A.E., Siu, A.L., &; Wieland, D. (1991). Impacts of geriatric evaluation and management programs on defined outcomes: Overview of the evidence. Journal of American Society, 39(1), 8s-16s.
Saliba, D. (2008, April). Development &; validation of a revised nursing home assessment tool: MDS 3.0. RAND Health Corporation. Retrieved from http://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/NursingHomeQualityInits/download/mds30finalreport.pdf.
Saliba, D., Jones , M., Streim , J., Ouslander , J., Berlowitz , D., &; Buchanan, J. (2012). Overview of significant changes in the Minimum Data Set for nursing homes version 3.0. Journal of the American Medical Directors Association, 13(7), 595-601.
Sammon, D., O’Donoghue, J., Herbert, J., &; O’Connor, K. A. (2012). Special issue on IS/IT applications for the support of older people. International Journal of Medical Informatics, 81(4), 637-648.
Santana, M. J., &; Feeny, D. (2013). Framework to assess the effects of using patient-reported outcome measures in chronic care management. Quality of Life Research,doi:10.1007/s11136-013-0596-1.
Seedorff, M., Peterson, K.J., Nelsen, L.A., Cocos, C., McCormick, J.B., Chute, C.G. &; Pathak, J. (2013). Incorporating expert terminology and disease risk factors into consumer health vocabularies. Pacific Symposium on Biocomputing, 421-32.
Sharma, S. Singh, P. Sharma, R., &; Mahajan , A. (2012). Age based user interface in mobile operating system. International Journal of Computer Science, Engineering and Applications, 2(2), 177-184.
Smith, C. (2011). Consumer language, patient language, and thesauri: a review of the literature. The Journal of Medicine Library, 99(2), 135-144.
Stuck, A. E., Siu, A. L., Wieland, G. D., Adams, J., &; Rubenstein, L.Z. (1993). Comprehensive geriatric assessment: a meta-analysis of controlled trials. Lancet, 342(8878), 1032-1036.
Studenski, S., Perera, S., Patel, K., Rosano, C., Faulkner, K., &; Inzitari, M.et al. (2011). Gait speed and survival in older adults. The Journal of the American Medical Association, 305(1), 50-58.
Tagalicod, R. (2013, September 18). Stage2: Engaging Patients in Their Health Care. Retrieved from http://www.cms.gov/eHealth/ListServ_Stage2_EngagingPatients.html.
The Centers for Medicare &; Medicaid Services. (2014, June 10). 2014 Definition Stage 1 of Meaningful Use. Retrieved from http://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/Meaningful_Use.html.
Tu, M-H, Hsu, C-L, Chu, C-S, Lu, C-C, Lan, C-F, &; Chang, P. (2012). How complex documentation with Personal Digital Assistants could be? Nursing Informatics 2012, 422.
Tu, M-H, Chang, P. (2010). How more complex documentation with PDA could be? AMIA Annual Symposium 2010.
White, P., &; Hall, M. E. (2006). Mapping the literature of case management nursing. Journal of Medical Library Association, 94(2), E99-E106.
Population Reference Bureau. (2014). 2014World Population Data Sheet. Retrieved from https://www.prb.org
Wilson, M.L., Murphy, L.S., Newhouse, R.P. (2012). Patients' access to their health information: a meaningful-use mandate. Journal of Nursing Administration, 42(11), 493-6.
Zeng, Q. T., Goryachev, S., Kim, H., Keselman, A., Rosendale, S. (2007). Making texts in electronic health records comprehensible to consumers: A prototype translator. AMIA Annual Symposium, 846–850.
Zeng, Q. T., Goryachev, S., Tse, T., Keselman, A., &; Boxwala, A. (2008). Estimating consumer familiarity with health terminology: a context-based approach, Journal of the American Medical Informatics Association, 15(3), 349-356.
Zeng, Q.T., Redd, D., Divita, G., Jarad, S., Brandt C, &; Nebeker, J.R. (2011). Characterizing clinical text and sublanguage: A case study of the VA clinical notes. Journal of Health and Medical Informatics, S3, 1-9.
Zeng, Q. T., &; Tse, T. (2006). Exploring and developing consumer health vocabularies. Journal of American Medical Informatics Association, 13(1), 24–29.
Zeng, Q. T., Tse, T., Divita, G., Keselman, A., Crowell, J., Browne, AC., ...Ngo, L. (2007). Term identification methods for consumer health vocabulary development. Journal of Medical Internet Research, 9(1), e4.