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A smart citizen healthcare assistant framework

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Abstract

This paper focuses on the development of a citizen-oriented lifelong healthcare assistant system, which will support not only health Care Delivery Organizations (CDOs) but also health care providers. In other words this work proposes an u_Health system that will enhance not only the health care services but it will also provide individuals with the opportunity to manage their health needs via a “Five Cs” comprehensive and consistent scheme. Its goal is to take advantage of the ongoing emerging technologies by including them in the process of healthcare with the synergy among health experts’ and the continuous interactive-exchange of knowledge and information in connection with the providers’ and the users’ experiences. Acting in this direction, the smart citizen healthcare assistant (sCHcA) framework proposes the infrastructure and the tools necessary in order to achieve the continuous monitoring and provision of care no matter where the individual may be. Specifically, this article presents a platform for a mobile mashup system that can be established on a CDO server and it will extend to a group of interrelated widgets that will work as a mobile service. The system will be able to create, install and operate as an integrated construction that will monitor and gather data from various health sensors and then send them to the CDO server and further may implement individuals’ health matters. Healthcare professionals and organizations can further exploit this novel integrated tele-monitoring mechanism. Moreover, they may also utilize it as a ubiquitous individual home health care service and facilitate vulnerable groups, those living in remote areas, patients suffering from incurable diseases and long-term patients.

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Abbreviations

5G:

5th generation

ABE:

Attribute based encryption

B2C:

Business to consumer

BAN:

Body area network

CCAC:

Community care access centre

CDO:

Care delivery organization

CAP:

client assessment protocol

CP-ABE:

Ciphertext policy attribute based encryption

e_Health:

Electronic health

ECC:

Elliptical curve cryptography

ECG:

ElectroCardioGraphy

EEG:

ElectroEncephalography

eGovernment:

Electronic government

EΗR:

Electronic health record

EMR:

Electronic medical record

FP7:

Seventh framework programme

HcP:

Health care provider

HCQI:

Health care quality indicator

HIT:

Health information technology

HCQI:

Home care quality indicator

HTTP:

Hypertext transfer protocol

ICT:

Information and communications technology

m_Health:

Mobile health

MDS:

Minimum data set

MDS-HC:

Minimum data set for home care

MDS/RAI:

Minimum data set/resident assessment instrument

PAN:

Personal area network

PDA:

Personal digital assistant

PHR:

Personal health record

PKI:

Public key cryptography

QoL:

Quality of life

RAI:

Resident assessment instrument

RAP:

Resident assessment protocol

RBAC:

Role based access control

RUG:

Resource utilization group

RUG-III:

Resource Utilization Groups version III

RUG-III/HC:

Resource Utilization Groups version III/home care

sCHcA:

Smart citizen healthcare assistant

sCiAI:

Smart citizen assessment instrument

sCiAP:

Smart citizen assessment protocol

sCiAITM:

Smart citizen assessment instrument templates management subsystem

SLA:

Service level agreement

SPO2:

Spirometer of oxygen 2

SPoC:

Single point of contact

SSL/TLS:

Secure sockets layer/transport layer security

u_Health:

Ubiquitous health

WRHA:

Winnipeg regional health authority

WSN:

Wireless sensor network

References

  1. eHealth priorities and strategies in European countries. eHealth ERA report. The European Commission, Directorate General Information Society and Media; 2007.

  2. Hajjam A. Enhancing eHealth Information Systems for chronic diseases remote monitoring systems. 2012;3(8):48–53

  3. Snae C, Brueckner M. Personal health assistance service expert system (PHASES). Life Sci. 2007;26:109–12.

    Google Scholar 

  4. Istepanian RSH, Laxminaryan S. UNWIRED, the next generation of wireless and internetable telemedicine systems-editorial paper. IEEE Trans Inf Technol Biomed. 2000;4:189–94.

    Article  Google Scholar 

  5. Jovanov E, Lords A, Raskovic D, Cox P, Adhami R, An-drasik F. Stress monitoring using a distributed wireless intelligent sensor system. IEEE Eng Med Biol Mag. 2003;22(3):49–55.

    Article  Google Scholar 

  6. Pattichis CS, Kyriacou E, Voskarides S, Pattichis MS, Istepanian RSH, Schizas CN. Wireless telemedicine systems: an overview. IEEE Antennas Propagat Mag. 2002;44(2):143–53.

    Article  Google Scholar 

  7. Istepanian RSH, Jovanov E, Zhang YT. Guest editorial introduction to the special section on M-Health: beyond seamless mobility and global wireless health-care connectivity. IEEE Trans Inf Technol Biomed [Internet]. 2004;8(4):405–14. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1362649. Accessed 20 Aug 2012.

    Article  Google Scholar 

  8. Sohn M, Hahn D, Lee J. The strategy development of u_Health Service. Heart. 2006;(c):1931–4.

  9. Alliance for Health Reform. A reporter’s toolkit: health information technology. An alliance for health reform toolkit—produced with support from the Robert Wood Johnson Foundation. http://www.allhealth.org/publications/health_information_technology/health_information_technology_toolkit.asp. Accessed 20 Apr 2013.

  10. Gallagher BP. Selected health information technology. www.pinellashealth.com/RHIO/Terminology_Master.pdf.

  11. Abidi SR. A conceptual framework for ontology based automating and merging of clinical pathways of comorbidities. In Proc. of. Knowledge Management for Health Care Procedures, ECAI 2008 Workshop, K4HelP 2008. Patras, Greece; 2008. p. 55–66

  12. Isern D, Moreno A. Computer-based execution of clinical guidelines: a review. Int J Med Inform. 2008;77:787–808.

    Article  Google Scholar 

  13. Batet M, Isern D, Marin L, Martínez S, Moreno A, Sánchez D, et al. Knowledge-driven delivery of home care services. J Intell Inf Syst. 2011. doi:10.1007/s10844-010-0145-0. Accessed 20 Aug 2012.

    Google Scholar 

  14. Abidi S, Abidi SSR, Hussain S, Shepherd M. Ontology-based modelling of clinical practice guidelines: a clinical decision support system for breast cancer follow-up interventions at primary care settings. In: 12th World Congress on Medical Informatics; 2007.

  15. Shapoor S. Knowledge modeling to develop a clinical practice guideline ontology: towards computerization and merging of clinical practice guidelines. Masters Thesis, Dalhousie University; 2007.

  16. Hurley K, Abidi SSR. Ontology engineering to model clinical pathways: towards the computerization and execution of clinical pathways. In: 20th IEEE Symposium on Computer Based Medical Systems, Maribor. Los Alamitos: IEEE Press; 2007.

  17. Abidi SR, Abidi SSR. Towards the merging of multiple clinical protocols and guidelines via ontology-driven modeling. In Proc. of. 12th Conference on Artificial Intelligence in Medicine, AIME 2009. Verona, Italy; 2009. p. 81–5.

  18. Moskovitch R, Lavie T, Leibowitz A, Denekump Y, Shahar Y. A multiple-ontology template-based query interface for a clinical-guidelines search engine. Proceedings of ECAI06 workshop on AI techniques in healthcare: evidence-based guidelines and protocols, Italy; 2006.

  19. Shalom E, Shahar Y, Lunenfeld E, Taieb-Maimon M, Young O, Goren-Bar D, et al. The importance of creating an ontology-specific consensus before a markup-based specification of clinical guidelines. In: Proceeding of the biennial European Conference on artificial intelligence (ECAI). Riva del Garda, Italy; 2006.

  20. Gray LC, Berg K, Fries BE, Henrard J-C, Hirdes JP, Steel K, et al. Sharing clinical information across care settings: the birth of an integrated assessment system. BMC Heal Serv Res. 2009;9:71. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2685125&tool=pmcentrez&rendertype=abstract. Accessed 13 Aug 2012.

    Article  Google Scholar 

  21. Hirdes JP, Smith TF, Rabinowitz T, Yamauchi K, Pérez E, Telegdi NC, et al. The Resident Assessment Instrument-Mental Health (RAI-MH): inter-rater reliability and convergent validity. J Behav Heal Serv Res. 2002;29(4):419–32. http://www.ncbi.nlm.nih.gov/pubmed/12404936. Accessed 17 Aug 2012.

    Article  Google Scholar 

  22. Dalby DM, Hirdes JP, Fries BE. Risk adjustment methods for Home Care Quality Indicators (HCQIs) based on the minimum data set for home care. BMC Heal Serv Res. 2005;5(1):7. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=548266&tool=pmcentrez&rendertype=abstract. Accessed 10 Aug 2012.

    Article  Google Scholar 

  23. Fries BE. Changing the technology of assessing the elderly: the example of the R.A.I. Generations Spring [XXI]; 1997. p. 59–61.

  24. Hirdes JP, Ljunggren G, Morris JN, Frijters DHM, Finne Soveri H, Gray L, et al. Reliability of the interRAI suite of assessment instruments: a 12-country study of an integrated health information system. BMC health services research [Internet]. 2008 Jan [cited 2013 Apr 8];8:277. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2631461&tool=pmcentrez&rendertype=abstract. Accessed 20 Apr. 2013.

  25. Carpenter GI, Ikegami N, Ljunggren G, Carrillo E, Fries BE. RUG-III and resource allocation: comparing the relationship of direct care time with patient characteristics in five countries. Age Ageing [Internet]. 1997;26 Suppl 2:61–5. http://www.ncbi.nlm.nih.gov/pubmed/9464557. Accessed 14 Aug 2012.

    Article  Google Scholar 

  26. http://www.interrai.org/classification.html

  27. Peleg M, Tu SW. Design patterns for clinical guidelines. Artif Intell Med. 2009;47:1–24. doi:10.1016/j.artmed.2009.05.004.

    Article  Google Scholar 

  28. Lenz R, Peleg M, Reichert M. Healthcare process support: achievements, challenges, current research. IJKBO. 2012

  29. Terenziani P. A hybrid multi-layered approach to the integration of workflow and clinical guideline approaches. In: Proc. Business Process Management Workshops, LNBIP 43; 2009. p. 539–544.

  30. Stosz L, Carpenter I. Developing the use of MDS/RAI reports for UK care homes. 2008. Retrieved November 4, 2009. http://www.jrf.org.uk/sites/files/jrf/2309.pdf. Accessed 14 Aug 2012.

  31. Shanker U. Issues in mobile distributed real time databases: performance and review. 2011;3(4):3504–17

  32. Anjomshoaa A, Bader G, Tjoa AM. Exploiting mashup architecture in business use cases; 2012.

  33. Paredes-Valverde Mg M, Alor-Hernandez G. Enterprise mashups: a new approach for business solutions. Lampsakos. 2012;7:39–45.

    Google Scholar 

  34. Vrieze P, Xu L, Bouguettaya A, Yang J, Chen J. Process-oriented enterprise mashups. Proceedings of the 2009 Workshops at the Grid and Pervasive Computing Conference; 2009. p. 64–71.

  35. A Business Guide to Enterprise Mashups, JackBe Corporation; 2008.

  36. Hirdes JP, Poss JW, Curtin-Telegdi N. The method for assigning priority levels (MAPLe): a new decision-support system for allocating home care resources. BMC Med. 2008;6:9. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2330052&tool=pmcentrez&rendertype=abstract. Accessed 10 Aug 2012.

    Article  Google Scholar 

  37. User Guidelines for the Job Aid Putting It All Together. RAI-MDS and integration PIECES TM; 2010. p. 1–28.

  38. Mor V, Intrator O, Hiris J, Fries BE, Phillips CD, Hawes C, et al. Impact of the MDS on changes in nursing home discharge rates and destinations. J Am Geriatr Soc. 1997;45(8):1002–10.

    Google Scholar 

  39. Morris JN, Fries BE, Bernabei R, Ikegami N, Gilgen R, Steel K, Carpenter I. RAI-home care assessment manual. Washington, D.C.: interRAI Corporation. Italian edition, Bernabei R, Landi F, Lattanzio F, Di Niro MG, Manigrasso L, editors. published as Resident Assessment Instrument-VAOR (Valutazione Anziano Ospite di Residenza): Manuale d’istruzione. Rome: Pfizer Italia Spa; 1996.

  40. Morris JN, Fries BE, Steel K, Ikegami N, Bernabei R, Carpenter GI. Comprehensive clinical assessment in community setting: applicability of the MDS-HC. J Am Geriatr Soc. 1997;45:1017–24.

    Google Scholar 

  41. Phillips CD, Morris JN, Hawes C, et al. Association of the Resident Assessment Instrument (RAI) with changes in function, cognition, and psychosocial status. J Am Geriatr Soc. 1997;45:986–93.

    Google Scholar 

  42. Countries E. Implementing the resident assessment instrument: case studies of policymaking for long-term care in eight countries. Milbank Memorial Fund. Electronic reports. 2003. http://www.milbank.org/reports/interRAI/030222interRAI.html.

  43. Mahony M. Trust remains key barrier to eHealth. http://euobserver.com/893/31958. Accessed 10 Aug 2012.

  44. Fan L, Buchanan W, Lo O, Theummler C, Lawson A, Uthmani O, Ekonomou E, Khedim A. SPoC: protecting patient privacy for e_Health services in the cloud. In: eTELEMED; 2012. p. 99–104.

  45. Jovanov E, Raskovic D, Price J, Chapman J, Moore A, Krishnamurthy A. Patient monitoring using personal area networks of wireless intelligent sensors. Biomed Sci Instrum. 2001;37:373–8.

    Google Scholar 

  46. Ferraiolo DF, Sandhu R, Gavrila S, Kuhn DR, Chandramouli R. Proposed NIST standard for role-based access control. ACM Trans Inf Syst Secur. 2001;4(3):224–74.

    Article  Google Scholar 

  47. Ibraimi L, Asim M, Petkovic M. Secure management of personal health records by applying attribute-based encryption. In 6th International Workshop on Wearable Micro and Nano Technologies for Personalized Health, pHealth’09, Oslo, Norway; 2009. p. 71–74.

  48. Alemdar H, Ersoy C. Wireless sensor networks for healthcare: a survey. Comput Netw. 2010;54(15):2688–710.

    Article  Google Scholar 

  49. Lounis A, Hadjidj A, Bouabdallah A, Challal Y. Secure and scalable cloud-based architecture for e_Health Wireless sensor networks.

  50. Oleshchuk V, Fensli R. Remote patient monitoring within a future 5G infrastructure. 2010;(123).

  51. Directive 95/46/EC of the European Parliament and of the Council of 24 October 1995 on the protection of individuals with regard to the processing of personal data and on the free movement of such data. Off J Eur Communities. L 281, 23/11/1995 P. 0031–0050.

  52. Directive 2002/58/EC of the European Parliament and of the Council of 12 July 2002 concerning the processing of personal data and the protection of privacy in the electronic communications sector (Directive on privacy and electronic communications). Off J Eur Communities. L 201, 31/7/2002, p. 37.

  53. Directive 2006/24/EC of the European Parliament and of the Council of 15 March 2006 concerning the processing of personal data and the protection of privacy in the electronic communications sector (Directive on privacy and electronic communications). Off J Eur Communities. L 105, 13/4/2006.

  54. Directive 2009/136/EC of the European Parliament and of the Council of 25 November 2009 concerning the processing of personal data and the protection of privacy in the electronic communications sector (Directive on privacy and electronic communications). Off J Eur Communities. L 337, 18/12/2009.

  55. Kavadias CD, Koutsopoulos KA, Vlachos MP, Bourka A, Kollias V, Stassinopoulos G. A monitoring/auditing mechanism for SSL/TLS secured service sessions in health care applications. Technol Health Care. 2003;11(1):1.

    Article  Google Scholar 

  56. Kambourakis G, Maglogiannis I, Rouskas A. PKI-based secure mobile access to electronic health services and data. Technol Health Care. 2005;13(6):511–26.

    Google Scholar 

  57. Agoyi M, Seral D. SMS security: an asymmetric encryption approach. Sixth International Conference on Wireless and Mobile Communications. 2010;69.

  58. Lauter K, Microsoft Corporation. Topics in wireless security the advantages of elliptic curve cryptography for wireless security; 2004. p. 62–7.

  59. Dieng-kuntz R, Minier D, Corby F, Ruzicka M, Corby O, Alamarguy L, et al. Medical ontology and virtual staff for a health network. In: Engineering knowledge in the age of the semantic web. EKAW’2004, Springer-Verlag Heidelberg, Lecture Notes in Computer Science 3257, October 5–8. p. 187–202.

  60. Nanou DG, Poulis AM, Likas A, Fotiadis DI. h-Life: an intelligent personal health assistant. Proceedings of the 6th International Symposium on Health Information Management Research, Halkidiki; 2001.

  61. Jang MS, Shin KS, Lee EH, Choi SB. A study on ubiquitous intelligent healthcare system in home service aggregation Business Model. Int Conf UIC2007 (LNCS). 2007;4611(1):983–92.

    Google Scholar 

  62. Grant RW, Wald JS, Poon EG, Schnipper JL, Gandhi TK, Volk LA, et al. Design and implementation of a web-based patient portal linked to an ambulatory care electronic health record: patient gateway for diabetes collaborative care. Diabetes Technol Ther. 2006;8(5):576–86. http://www.ncbi.nlm.nih.gov/pubmed/17037972. Accessed 11 Aug 2012.

    Article  Google Scholar 

  63. http://www.mobiguide-project.eu/2-uncategorised/27-elevator-pitch. Accessed 16 Aug 2012.

  64. Fan L, Buchanan W, Thuemmler C, Lo O, Khedim AS, Uthmani O, Lawson A, Bell D. DACAR Platform for e_Health Services Cloud. In Proc of CLOUD’11. IEEE; 2011. p. 1–8.

  65. Sneha S, Varshney U. Enabling ubiquitous patient monitoring: model, decision protocols, opportunities and challenges. Decis Support Syst [Internet]. 2009;46(3):606–19. Available from: http://dx.doi.org/10.1016/j.dss.2008.11.014.

    Article  Google Scholar 

  66. Beemer B, Gregg D. Mashups: a literature review and classification framework. Future Internet [Internet]. 2009 Dec 22 [cited 2013 Mar 31];1(1):59–87. Available from: http://www.mdpi.com/1999-5903/1/1/59/.

  67. Cheung K-H, Yip KY, Townsend JP, Scotch M. HCLS 2.0/3.0: health care and life sciences data mashup using Web 2.0/3.0. J Biomed Inform [Internet]. 2008 Oct [cited 2012 Mar 26];41(5):694–705. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2933816&tool=pmcentrez&rendertype=abstract.

  68. Tollmar K, Bentley F, Viedma C. Mobile health mashups: making sense of multiple streams of wellbeing and contextual data for presentation on a mobile device. In Proc Pervasive Health; 2012.

  69. Gray AJG, Sadler J, Kit O, Kyzirakos K, Karpathiotakis M, Calbimonte J-P, et al. A semantic sensor web for environmental decision support applications. Sensors (Basel, Switzerland) [Internet]. 2011 Jan [cited 2013 Mar 15];11(9):8855–87. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3231498&tool=pmcentrez&rendertype=abstract.

  70. Sow D, Biem A, Blount M, Ebling M, Verscheure O. Body sensor data processing using stream computing. Proceedings of the international conference on Multimedia information retrieval—MIR’10 [Internet]. 2010;449. Available from: http://portal.acm.org/citation.cfm?doid=1743384.1743465.

  71. Seyfang A, Paesold M, Votruba P, Miksch S. Improving the execution of clinical guidelines and temporal data abstraction high-frequency domains. Stud Health Technol Inform [Internet]. 2008;139:263–72. Available from: http://www.ncbi.nlm.nih.gov/pubmed/18806335.

    Google Scholar 

  72. Patkar V, Hurt C, Steele R, Purushotham A, Love S, Williams M, et al. Impact of a computerised decision support system on compliance with evidence-based guidelines for triple assessment.

  73. Patkar V, Hurt C, Steele R, Love S, Purushotham A, Williams M, et al. Evidence-based guidelines and decision support services: a discussion and evaluation in triple assessment of suspected breast cancer. Br J Cancer. 2006;95:1490–6.

    Article  Google Scholar 

  74. Peleg M, Wang D, Fodor A, Keren S, Karnieli E. Lessons learned from adapting a generic narrative diabetic-foot guideline to an institutional decision-support system. Stud Health Technol Inform. 2008;139:243–52.

    Google Scholar 

  75. Peleg M, Patel VL, Snow V, Tu S, Mottur-Pilson C, Shortliffe EH, et al. wSupport for guideline development through error classification and constraint checking. Proceeding of the American Medical Informatics Association Annual Symposium; 2002. p. 607–11.

  76. Peleg M, Kantor R. Approaches for guideline versioning using GLIF. Proceeding of the American Medical Informatics Association Annual Symposium; 2003. p. 509–13.

  77. Hatsek A, Young O, Shalom E, Shahar Y. Demonstration of DeGeL: a clinical-guidelines library and automated guideline-support tools. Proceeding of AI Techniques in Healthcare: Evidence-based Guidelines and Protocols, ECAI 2006, Riva del Garda, Italy; 2006.

  78. Hatsek A, Young O, Shalom E, Shahar Y. DeGeL: a clinical-guidelines library and automated guideline-support tools. In: Teije AT, Miksch S, Lucas P, editors. Studies in health technology and informatics, vol. 139. IOS Press; 2008. p. 203–212.

  79. Fox J, Patkar V, Thomson R, Black E. PROforma guidelines and care pathways: performance in trials and future plans. In: AnnettetenTeije P, Lucas S, Miksch, editors. The biennial European Conference on Artificial Intelligence (ECAI) Workshop: AI techniques in healthcare: computerized guidelines and protocols. Riva del Garda: Ios Press; 2006.

    Google Scholar 

  80. Terenziani P, Montani S, Bottrighi A, Torchio M, Molino G, Anselma L, Correndo G. Applying artificial intelligence to clinical guidelines: the glare approach. In: Capelli A, Turini F, editors. Proceedings of AI* IA 2003: advances in artificial intelligence, vol. 2829 of Lecture Notes in Computer Science, Springer; 2003. p. 536–547. http://www.ncbi.nlm.nih.gov/pubmed/18806336.

  81. Terenziani P, Giordano L, Bottrighi A, Montani S, Donzella L. SPIN Model checking for the verification of clinical guidelines. In: ECAI 2006 workshop on AI techniques in healthcare: evidence-based guidelines and protocols; 2006. p. 101–6.

  82. Stosz L, Carpenter I (2008) Developing the use of MDS/RAI reports for UK care homes. http://www.jrf.org.uk/sites/files/jrf/2309.pdf. Accessed 22 Aug 2012.

  83. Health Service Executive (HSE) DML Services for Older People and Health Service Executive (HSE) Nursing and Midwifery Planning and Development. (Apr-2010) DML integrated minimum data set resident assessment instruments and protocols. http://lenus.ie/hse/bitstream/10147/116389/1/dmlmds.pdf. Accessed 22 Aug 2012.

  84. Carpenter GI, Ikegami N, Ljunggren G, Carrillo E, Fries BE. RUG-III and resource allocation: comparing the relationship of direct care time with patient characteristics in five countries. Age Ageing. 1997;26(2):61–5. http://www.ncbi.nlm.nih.gov/pubmed/9464557. Accessed 26 Aug 2012.

    Article  Google Scholar 

  85. Dalby, Hirdes, Fries. Risk adjustment methods for Home Care Quality Indicators (HCQIs) based on the minimum data set for home care. 2005;5:7. http://www.biomedcentral.com/content/pdf/1472-6963-5-7.pdf. Accessed 29 Aug 2012.

  86. Kennedy J, Nolan M. SLA @ SOI final report; 2011.

  87. Butler J, Lambea J, Nolan M, Theilmann W, Torelli F, Yahyapour R, et al. SLAs empowering services in the future internet 1. Future internet assembly. LNCS. 2011;6656:327–38.

    Google Scholar 

  88. Hirdes JP, Smith TF, Rabinowitz T, Yamauchi K, Pérez E, Telegdi NC, et al. The Resident Assessment Instrument-Mental Health (RAI-MH): inter-rater reliability and convergent validity. J Behav Heal Serv Res. 2002;29(4):419–32. http://www.ncbi.nlm.nih.gov/pubmed/12404936. Accessed 26 Aug 2012.

    Article  Google Scholar 

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Acknowledgments

The author is grateful to the anonymous reviewers for their useful comments and valuable suggestions that helped her to reconstruct and improve this document considerably. Also, the author is thankful to the editor-in-Chief of the Health and Technology journal Mr. Lodewijk Bos for kindly providing her the opportunity to continue this effort.

Conflict of interest

The author declare that she has no conflict of interest.

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  1. Department of Health and Welfare Units Management, Technological Educational Institute of Kalamata, Kalamata, Greece

    Stella C. Christopoulou

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Christopoulou, S.C. A smart citizen healthcare assistant framework. Health Technol. 3, 249–265 (2013). https://doi.org/10.1007/s12553-013-0058-3

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