International Journal of Medical and Health Sciences Research

Published by: Asian Medical Journals
Online ISSN: 2313-2752
Print ISSN: 2313-7746
Total Citations: 64
Quick Submission    Login/Submit/Track

No. 12

Empirical and Practical Implementation Methodology for Clinical Integration of E-Health Iot Technology

Pages: 117-125
Find References

Finding References


Empirical and Practical Implementation Methodology for Clinical Integration of E-Health Iot Technology

Search :
Google Scholor
Search :
Microsoft Academic Search
Cite

DOI: 10.18488/journal.9/2016.3.12/9.12.117.125

Citation: 2

Abel Garai

Export to    BibTeX   |   EndNote   |   RIS

  1. B. Fong, A. C. M. Fong, and C. K. Li, Telemedicine technologies: Information technologies in medicine and telehealth. Chichester: Wiley, 2011.
  2. U. Varshney, "Pervasive healthcare and wireless health monitoring," Mobile Networks and Applications Springer US, New York, vol. 12, pp. 113-127, 2007.
  3. R. J. Rossi, Applied biostatistics for the health sciences. Hoboken, NJ: Wiley, 2010.
  4. L. Martinez and C. Gomez, Telemedicine in the 21st century. Applied biostatistics for the health sciences. New York: Nova Science Publishers, 2008.
  5. R. L. Bashshur and G. W. Shannon, History of telemedicine: Evolution, context, and transtiormation. New Rochelle, NY: Mary Ann Liebert, 2009.
  6. H. Eren and J. G. Webster, The E-medicine, E-health, M-health, telemedicine, and telehealth handbook. Oakville: CRC Press, 2015.
  7. P. Baranyi, A. Csapo, and G. Sallai, Cognitive infocommunications (CogInfoCom) vol. 1. New York: Springer International, 2015.
  8. T. J. Hajdu, Z. Pistar, B. Domokos, and Z. Török, "An ensemble-based collaborative framework to support customized user needs," presented at the 3rd IEEE International Conference on Cognitive Infocommunications (CogInfoCom 2012), Kosice, Slovakia, 2012.
  9. B. Péter and C. Ádám, "Definition and synergies of cognitive infocommunications," Acta Polytechnica Hungarica, vol. 9, pp. 67-83, 2012.
  10. [S. Gyula, "Chapters of future internet research," presented at the IEEE 4th International Conference on Cognitive Infocommunications (CogInfoCom), Budapest, 2013.
  11. H. Marjo, P. Sakari, D. J. Sikke, and E. Christian, "Experimenting industrial Internet with a mobile robot: Expanding human cognitive functions," presented at the IEEE 6th International Conference on Cognitive Infocommunications (CogInfoCom), Győr, 2015.
  12. H. Toman, L. Kovacs, A. Jonas, L. Hajdu, and A. Hajdu, "Generalized weighted majority voting with an application to algorithms having spatial output," presented at the 7 th Int. Conf. on Hybrid Artificial Intelligence Systems (HAIS 2012), Lecture Notes in Computer Science, 2012.
  13. R. Szabo, K. Farkas, M. Ispány, A. Benczur, L. Kollar, and A. Adamkó, "Framework for smart city applications based on participatory sensing," presented at the Cognitive Infocommunications (CogInfoCom) IEEE 4th International Conference on, IEEE, 2013.
  14. K. Mahmud and J. Lenz, "The personal telemedicine system. A new tool for the delivery of health care," Journal of Telemedicine and Telecare, vol. 1, pp. 173–177, 1995.
  15. R. Wootton, "Telemedicine in the national health service," Journal of the Royal Society of Medicine, vol. 91, pp. 289–292, 1998.
  16. G. Singh, J. O'Donoghue, and C. K. Soon, "T telemedicine: Issues and implications," Technology Health Care, vol. 10, pp. 1-10, 2002.
  17. I. M. Llorente, R. S. Montero, E. Huedo, and K. Leal, "A grid infrastructure for utility computing," in Proceedings of the 15th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises, 2006.
  18. H. H. Asada, P. Shaltis, A. Reisner, R. Sokwoo, and R. C. Hutchinson, "Mobile monitoring with wearable photoplethysmographic biosensors," In Engineering in Medicine and Biology Magazine, IEEE, 2003.
  19. S. Tr, M. As, and A. Mh, "An ad hoc wireless sensor network for telemedicine applications," Arabian Journal for Science and Engineering, vol. 32, pp. 131-143, 2007.
  20. Y. Xiao, Y. Tao, and Q. Li, "A new wireless web access mode based on cloud computing," presented at the Computational Intelligence and Industrial Application, 2008. PACIIA '08. Pacific-Asia Workshop, 2008.
  21. U. Varshney, "Pervasive healthcare and wireless health monitoring," Mobile Networks and Applications, New York, vol. 12, pp. 113-127, 2007.
  22. A. Attila, A. Mátyás, F. Gábor, and J. István, "Performance evaluation of large-scale data processing systems," in Proceedings of the 7th International Conference on Applied Informatics, Eger, Hungary, 2007, pp. 295–301.
  23. A. Attila and K. Lajos, "MDA-based development of data-driven web applications," in Proceedings of the Fourth International Conference on Web Information Systems and Technologies, Volume 1, Funchal, Madeira, Portugal, 2008, pp. 252-255.
  24. A. Attila, G. Ábel, and P. István, "Adaptive services with cloud architecture for telemedicine," presented at the Cognitive Infocommunications (CogInfoCom), 6th IEEE Conference, 2015.
  25. G. Ábel, "Methodology for assessment validation of platform migration of roboust critical IT-systems," presented at the ICAI 2010, 8th International Conference on Applied Informatics, 2010.
Abel Garai (2016). Empirical and Practical Implementation Methodology for Clinical Integration of E-Health Iot Technology. International Journal of Medical and Health Sciences Research, 3(12): 117-125. DOI: 10.18488/journal.9/2016.3.12/9.12.117.125
This paper structures empirical and practical implementation methodologies for clinical integration of eHealth IoT smart device technology embedded in Cloud service architecture. The results and findings of this two-year research program are summarized from mathematical, system architectural and software engineering perspective. The research takes place in the European Union, in Hungary. The program is the manifestation of the trilateral industry-university collaboration of the University of Debrecen Faculty of Informatics, T-Systems Healthcare Competence Center Central and Eastern Europe and Semmelweis University 2nd Pediatric Clinic Department of Pulmonology. The paper presents the mathematical model for the system architecture optimization. Selected system-architectural solution plans are mapped into directed graphs and converted into adjacency and availability matrices for optimization. Adequate technologies are collected and identified for the research based on industry megatrends. The experiment establishes multidirectional interoperability among eHealth smart devices, telemedicine instruments and clinical information systems. The Open Telemedicine Interoperability Hub, interoperability core module, was developed and embedded into Cloud service architecture. This module transposes and transmits the captured bio-sensory data stream from the eHealth IoT smart-devices and from the telemedicine instrument into the clinical information system. Dominant international healthcare interoperability standards are reviewed and selected for the research. The research program defined different interoperability levels and mapped these against the open systems interconnection model layers. The international interoperability standard, Health Level Seven, was selected for the research. The research explicitly tested interoperability among eHealth consumer electronic sensor-enabled smart-device, spirometer telemedicine instrument and cloud-based hospital information test system.  The research proved that universal interoperability between IoT eHealth smart devices and clinical information system technology is from technical perspective absolutely possible. The paper describes the lessons learned, drawbacks and achievements of this research program. An insight is also given into the forthcoming research phase.
Contribution/ Originality
The paper contributes the first logical analysis for clinical integration of the eHealth IoT technology. This study uses new determination method for mathematically optimized healthcare service architecture. The research originates new mathematic formula for healthcare systems integration. The paper’s primary contribution is validating the empirical results in real clinical environment.