Skip to main content

The Road to the Future of Healthcare: Transmitting Interoperable Healthcare Data Through a 5G Based Communication Platform

  • Conference paper
  • First Online:
Information Systems (EMCIS 2018)

Abstract

Current devices and sensors have revolutionized our daily lives, with the healthcare domain exploring and adapting new technologies. The rapid explosion of digital healthcare happened with the help of current 4G LTE technologies including innovations such as the continuous monitoring of patient vitals, teleporting doctors to a virtual environment or leveraging Artificial Intelligence to generate new medical insights. The arised problem is that current 4G LTE based communication platforms will not be able to keep up with the exploding connectivity demands. This is where the new 5G technology comes, expected to support ultra-reliable, low-latency and massive data communications. In this paper, an end-to-end approach is being provided in the healthcare domain for gathering medical data, anonymizing it, cleaning it, making it interoperable, and finally storing it through 5G network technologies, for their transmission to a different location, supporting real-time results and decision-making.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Population health outcomes. http://www.healthcatalyst.com/population-health-outcomes-3-keys-to-drive-improvement

  2. The role of IoT in the healthcare industry. https://hackernoon.com/the-role-of-internet-of-things-in-the-healthcare-industry-759b2a1abe5

  3. Healthcare needs 5G. https://www.chilmarkresearch.com/healthcare-needs-5g/

  4. How will 5G impact different industries? http://prescouter.com/2018/01/5g-impact-different-industries

  5. The Journey to 5G. http://www.healthcareitnews.com/news/journey-5g

  6. Pires, F., et al.: A platform for integrating physical devices in the Internet of Things. In: Embedded and Ubiquitous Computing (EUC), pp. 234–241. IEEE (2014)

    Google Scholar 

  7. Gong, P.: Dynamic integration of biological data sources using the data concierge. Health Inf. Sci. Syst. 1, 1–19 (2013)

    Article  Google Scholar 

  8. GDPR requirements. https://www.delphix.com/white-paper/gdpr

  9. El Emam, K., Arbuckle, L.: Anonymizing Health Data: Case Studies and Methods to get you started, 2nd edn, p. 1005. O’Reilly Media Inc., Newton (2013)

    Google Scholar 

  10. Kruger, P., Hancke, G.: Benchmarking internet of things data sources. In: 12th IEEE International Conference on Industrial Informatics (INDIN). IEEE (2014)

    Google Scholar 

  11. Macfarlane, S., Tannath, T., Scott, J., Kelly, V.: The validity and reliability of global positioning systems in team sport: a brief review. JSCR 30(5), 1470–1490 (2016)

    Google Scholar 

  12. Mead, C.: Data interchange standards in healthcare IT-computable semantic interoperability. JHIM 20, 71–78 (2006)

    Google Scholar 

  13. HL7 FHIR. https://www.hl7.org/fhir/

  14. HEALTHCARE 4.0: A NEW WAY OF LIFE? http://www.vph-institute.org/news/healthcare-4-0-a-new-way-of-life.html

  15. A new Generation of eHealth Systems Powered by 5G. http://www.wwrf.ch/files/wwrf/content/files/publications/outlook/Outlook17.pdf

  16. 5G on eHealth. https://5g-ppp.eu/wp-content/uploads/2016/02/5G-PPP-White-Paper-on-eHealth-Vertical-Sector.pdf

  17. INTERNET OF THINGS & 5G REVOLUTION. http://www.astrid-online.it/static/upload/stud/studio-i-com_internet_5g_.pdf

  18. Mishra, A., Agrawal, P.: Continuous health condition monitoring by 24 × 7 sensing and transmission of physiological data over 5G cellular channels. In: ICNC, pp. 584–590 (2015)

    Google Scholar 

  19. Banaee, H., et al.: Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges. Sensors 13(12), 17472–17500 (2013)

    Article  Google Scholar 

  20. Ryan, M., et al.: Facilitating health behaviour change and its maintenance: interventions based on self-determination theory. Eur. Health Psychol. 10, 2–5 (2008)

    Google Scholar 

  21. Oleshchuk, V., Fensli, R.: Remote patient monitoring within a future 5G infrastructure. Wirel. Pers. Commun. 57, 431–439 (2011)

    Article  Google Scholar 

  22. Mattos, W., Gondim, P.: M-health solutions using 5G networks and M2M communications. IT Prof. 18(3), 24–29 (2016)

    Article  Google Scholar 

  23. Leventer-Roberts, M., Balicer, R.: Data integration in health care. In: Amelung, V., Stein, V., Goodwin, N., Balicer, R., Nolte, E., Suter, E. (eds.) Handbook Integrated Care, pp. 121–129. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56103-5_8

    Chapter  Google Scholar 

  24. Rolim, C.O., et al.: A cloud computing solution for patient’s data collection in health care institutions. In: Second International Conference on ETELEMED 2010. IEEE (2010)

    Google Scholar 

  25. Carbonaro, A., Piccinini, F., Reda, R.: Integrating heterogeneous data of healthcare devices to enable domain data management. JeLKS 14(1), 45–56 (2018)

    Google Scholar 

  26. Pötter, B., Sztajnberg, A.: Adapting heterogeneous devices into an IoT context-aware infrastructure. In: Software Engineering for Adaptive and Self-Managing, pp. 64–74. ACM (2016)

    Google Scholar 

  27. OWL. https://www.w3.org/TR/owl-guide/

  28. Globle, C., et al.: Transparent access to multiple bioinformatics information sources. IBM Syst. J. 40, 534–551 (2001)

    Google Scholar 

  29. Donelson, L., et al.: The BioMediator system as a data integration tool to answer diverse biologic queries. In: Proceedings of MedInfo, pp. 768–772 (2004)

    Google Scholar 

  30. Philippi, S.: Light-weight integration of molecular biological databases. Bioinformatics 20, 51–57 (2004)

    Article  Google Scholar 

  31. Eckman, B., Lacroix, Z., Raschid, L.: Optimized seamless integration of biomolecular data. In: IEEE International Conference on Bioinformatics and Biomedical Engineering, pp. 23–32 (2001)

    Google Scholar 

  32. Martín, L., et al.: Ontology based integration of distributed and heterogeneous data sources in ACGT. In: HEALTHINF, pp. 301–306 (2008)

    Google Scholar 

  33. Jabbar, S., et al.: Semantic interoperability in heterogeneous IoT infrastructure for healthcare. Wirel. Commun. Mobile Comput. (2017)

    Google Scholar 

  34. Truta, T., Vina, B.: Privacy protection: p-sensitive k-anonymity property. In: 22nd International Conference on Data Engineering Workshops, Atlanta (2006)

    Google Scholar 

  35. El Emam, K.: Data anonymization practices in clinical research. a descriptive study. University of Ottawa (2006)

    Google Scholar 

  36. El Emam, K., et al.: A systematic review of re-identification attacks on health data. PLoS One 6(12), e28071 (2011)

    Article  Google Scholar 

  37. Zhong, S., et al.: Privacy-enhancing k-anonymization of customer data. In: PODS 2005, pp. 139–147 (2004)

    Google Scholar 

  38. Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Unc. Fuzz. Knowl. Based Syst. 10(5), 557–570 (2002)

    Article  MathSciNet  Google Scholar 

  39. Benjamin, E., et al.: Systematic literature review on the anonymization of high dimensional streaming datasets for health data sharing. Procedia Comput. Sci. 63, 348–355 (2015)

    Article  Google Scholar 

  40. Dubovitskaya, A., Urovi, V., Vasirani, M., Aberer, K., Schumacher, M.I.: A cloud-based eHealth architecture for privacy preserving data integration. In: Federrath, H., Gollmann, D. (eds.) SEC 2015. IAICT, vol. 455, pp. 585–598. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18467-8_39

    Chapter  Google Scholar 

  41. Li, H., et al.: (a, k)-anonymous scheme for privacy-preserving data collection in IoT-based healthcare services systems. J. Med. Syst. 42(3), 56 (2018)

    Article  Google Scholar 

  42. Lu, Y., Sinnott, R.O., Verspoor, K.: A semantic-based k-anonymity scheme for health record linkage. Stud. Health Technol. Inform. 239, 84–90 (2017)

    Google Scholar 

  43. Lu, Y., Verspoor, K., Sinnott, R.O., Parampalli, U.: Effective preservation of privacy during record linkage. In: School of Computing and Information Systems, p. 25 (2017)

    Google Scholar 

  44. Fatima, A., Nazir, N., Gufran, K.: Data cleaning in data warehouse: a survey of data pre-processing techniques and tools. JITCS 9, 50–61 (2017)

    Google Scholar 

  45. Rahm, E., Do, H.: Data cleaning: problems and current approaches. IEEE Bull. Tech. Comm. Data Eng. 23(4), 2000–2012 (2000)

    Google Scholar 

  46. Krishnan, S., Haas, D., Franklin, M., Wu, E.: Towards reliable interactive data cleaning: a user survey and recommendations. In: HILDA, California (2016)

    Google Scholar 

  47. Dallachiesa, M., et al.: NADEEF: a commodity data cleaning system. In: ACM SIGMOD International Conference on Management of Data, New York (2013)

    Google Scholar 

  48. Dagade, A., Mali, M., Pathak, N.: Survey of data duplication detection and elimination in domain dependent and domain-independent databases. IJARCSMS 4(5), 238–243 (2016)

    Google Scholar 

  49. Benjelloun, O., et al.: Swoosh: A Generic Approach to Entity Resolution. Stanford InfoLab, Stanford (2005)

    Google Scholar 

  50. Bohannon, P., Fan, W., Flaster, M., Rastogi, R.: A cost-based model and effective heuristic for repairing constraints by value modification. In: ACM SIGMOD (2005)

    Google Scholar 

  51. Cong, G., Fan, W., Geerts, G., Jia, X., Ma, S.: Improving data quality: consistency and accuracy. In: The 33rd International Conference on Very Large Data Bases, Vienna (2007)

    Google Scholar 

  52. Fan, W., et al.: Towards certain fixes with editing rules and master data. VLDB J. 21(2), 213–238 (2012)

    Article  Google Scholar 

  53. Yakout, M., et al.: Guided data repair. Proc. VLDB Endowment 4(5), 279–289 (2011)

    Article  Google Scholar 

  54. Cheng, K., Hong, J.: A novel data cleaning with data matching. Adv. Sci. Technol. Lett. 136, 161–169 (2016)

    Article  Google Scholar 

  55. Gohel, A., et al.: A commodity data cleaning system. Int. Res. J. Eng. Technol. 4(5), 1011–1014 (2017)

    Google Scholar 

  56. Joseph, W.: Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. J. Strength Cond. Res. 19(1), 231 (2005)

    Google Scholar 

  57. Toporkov, A.: Criteria and methods for assessing reliability of medical equipment. Biomed. Eng. 42(1), 11–16 (2008)

    Article  Google Scholar 

  58. Mudasir, A.: Reliability models for the internet of things: a paradigm shift. In: IEEE International Symposium on ISSREW. IEEE (2014)

    Google Scholar 

  59. Zin, T.T., et al.: Reliability and availability measures for Internet of Things consumer world perspectives. In: 5th Global Conference on Consumer Electronics. IEEE (2016)

    Google Scholar 

  60. Ryan, R., et al.: Validity and reliability of Fitbit activity monitors compared to ActiGraph GT3X+ with female adults in a free-living environment. J. Sci. Med. Sport 20(6), 578–582 (2017)

    Article  Google Scholar 

  61. Kooiman, T., et al.: Reliability and validity of ten consumer activity trackers. BMC Sport. Sci. Med. Rehabil. 7(1), 24 (2015)

    Article  Google Scholar 

  62. Ruggiero, L., et al.: Validity and reliability of two field-based leg stiffness devices: implications for practical use. J. Appl. Biomech. 32(4), 415–419 (2016)

    Article  Google Scholar 

  63. Justin, L., et al.: Reliability and validity of a point-of-care sural nerve conduction device for identification of diabetic neuropathy. PLoS One 9(1), e86515 (2014)

    Article  Google Scholar 

  64. Misra, P., et al.: An interoperable realization of smart cities with plug and play based device management (2015)

    Google Scholar 

  65. Rastegar-Mojarad, M., et al.: Need of informatics in designing interoperable clinical registries. Int. J. Med. Inform. 108, 78–84 (2017)

    Article  Google Scholar 

  66. Introduction to HL7 Standards. http://www.hl7.org/implement/standards/

  67. HL7 v3. https://www.hl7.org/fhir/comparison-v3.html

  68. The HL7 Clinical Document Architecture. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC130066/

  69. Goossen, W., et al.: Detailed clinical models. Healthc. Inform. 16, 201–214 (2010)

    Article  Google Scholar 

  70. Wardle, M., Spencer, A.: Implementation of SNOMED CT in an online clinical database. Futur. Hosp. J. 4(2), 126–130 (2017)

    Google Scholar 

  71. EURECA EU project. https://www.dceureca.eu/

  72. Dogac, A., et al.: Artemis: deploying semantically enriched web services in the healthcare domain. Inf. Syst. 31, 321–339 (2006)

    Article  Google Scholar 

  73. Schulz, S., Udo, H.: Part-whole representation and reasoning in formal biomedical ontologies. AI Med. 34(3), 179–200 (2005)

    Google Scholar 

  74. Ryan, A., Eklund, P.: A framework for semantic interoperability in healthcare. Stud. Health Tech Inform. 136, 759 (2008)

    Google Scholar 

  75. Marsch, P., et al.: 5G radio access network architecture: design guidelines and key considerations. IEEE Commun. Mag. 54(11), 24–32 (2016)

    Article  Google Scholar 

  76. VNF. https://searchsdn.techtarget.com/definition/virtual-network-functions

  77. Ferreira, L., et al.: An architecture to offer cloud-based radio access network as a service. In: European Conference on Networks and Communications. IEEE (2014)

    Google Scholar 

  78. Network Functions Virtualisation. http://www.etsi.org/technologies-clusters/technologies/nfv

  79. SDN. https://www.opennetworking.org/sdn-definition/

  80. 5G Development and Validation Platform for global Industry-specific Network Services and Apps. http://5gtango.eu/

  81. Parada, C., et al.: 5GTANGO: A Beyond-MANO Service Platform (in press)

    Google Scholar 

  82. Open Source MANO. http://www.etsi.org/technologies-clusters/technologies/nfv/open-source-mano

  83. Mavrogiorgou, A., Kiourtis, A., Kyriazis, D.: Plug‘n’play IoT devices: an approach for dynamic data acquisition from unknown heterogeneous devices. In: Barolli, L., Terzo, O. (eds.) CISIS 2017. AISC, vol. 611, pp. 885–895. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-61566-0_84

    Chapter  Google Scholar 

  84. Mavrogiorgou, A., Kiourtis, A., Kyriazis, D.: A comparative study of classification techniques for managing IoT devices of common specifications. In: Pham, C., Altmann, J., Bañares, J.Á. (eds.) GECON 2017. LNCS, vol. 10537, pp. 67–77. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68066-8_6

    Chapter  Google Scholar 

  85. Kiourtis, A., et al.: Aggregating heterogeneous health data through an ontological common health language. In: DeSE 10th International Conference. IEEE (2017)

    Google Scholar 

Download references

Acknowledgements

A. Mavrogiorgou and A. Kiourtis would like to acknowledge the financial support from the “Hellenic Foundation for Research & Innovations (HFRI)”. Moreover, part of this work has been partially supported by the 5GTANGO project, funded by the European Commission under Grant number H2020ICT-2016-2 761493 through the Horizon 2020 and 5G-PPP programs (http://5gtango.eu).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Argyro Mavrogiorgou .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mavrogiorgou, A., Kiourtis, A., Touloupou, M., Kapassa, E., Kyriazis, D., Themistocleous, M. (2019). The Road to the Future of Healthcare: Transmitting Interoperable Healthcare Data Through a 5G Based Communication Platform. In: Themistocleous, M., Rupino da Cunha, P. (eds) Information Systems. EMCIS 2018. Lecture Notes in Business Information Processing, vol 341. Springer, Cham. https://doi.org/10.1007/978-3-030-11395-7_30

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-11395-7_30

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-11394-0

  • Online ISBN: 978-3-030-11395-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics