International Conference on Ubiquitous Computing and Ambient Intelligence

Ubiquitous Computing and Ambient Intelligence. Sensing, Processing, and Using Environmental Information pp 467-479 | Cite as

A Cloud-Based Mobile System for Improving Vital Signs Monitoring During Hospital Transfers

  • Andrés Neyem
  • Guillermo Valenzuela
  • Nicolas Risso
  • Juan S. Rojas-Riethmuller
  • José I. Benedetto
  • Marie J. Carrillo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9454)


As the number of patients in hospitals constantly grows, the need for hospital transfers is directly affected. Hospital transfers can be required for several reasons but they are most commonly made when the diagnostic and therapeutic facilities required for a patient are not available locally. Transferring a critical patient between hospitals is commonly associated with risk of death and complications. At the same time, advances in wearable technologies and health applications offer new possibilities to support healthcare. This raises the question: How can we improve the monitoring of vital signs of transported patients through use of information technology and communication services? This paper presents a cloud-based mobile system to support decision-making in the transportation of patients in critical condition. The Rapid Emergency Medicine Score (REMS) scale was used as an outcome variable, being a useful scale to assess the risk profile of critical patients requiring transfers between hospitals. The platform is the result of research and development work performed during the last two years.


Cloud-based mobile system Mobile cloud computing Rapid emergency medicine score Cloud workspaces Design guideline 


  1. 1.
    Bahl, P., Han, R.Y., Li, L.E., Satyanarayanan, M.: Advancing the state of mobile cloud computing. In: Proceedings of the Third ACM Workshop on Mobile Cloud Computing and Services, pp. 21–28. ACM (2012)Google Scholar
  2. 2.
    Berkelman, R.L., Sullivan, P., Buehler, J.W., Detels, R., Beaglehole, R., Lansing, M., Gulliford, M., et al.: Public health surveillance. In: Oxford Textbook of Public Health, 5th edn. The Methods of Public Health, vol. 2, pp. 699–715. Oxford University Press, Oxford (2009)Google Scholar
  3. 3.
    Bourouis, A., Feham, M., Bouchachia, A.: Ubiquitous mobile health monitoring system for elderly (umhmse) (2011). arXiv preprint arXiv:1107.3695
  4. 4.
    Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging it platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener. Comput. Syst. 25(6), 599–616 (2009)CrossRefGoogle Scholar
  5. 5.
    Carrillo, B.M.J.: Deterioro fisiopatológico y mortalidad, de los pacientes adultos sometidos a traslado interhospitalario a Unidades de Paciente Crítico por móviles del Servicio de Atención Médica de Urgencia del área Metropolitana, Santiago de Chile. Ph.D. thesis, Universidad de Sao Pablo (2015)Google Scholar
  6. 6.
    Carrillo, B.M.J., Jerez, C., Cortéz, A.: Traslado interhospitalario samu área metropolitana: En búsqueda de una mejor calidad de atención. FONIS SA12I2045 (2014)Google Scholar
  7. 7.
    Carrillo, B.M.J., Urrutia S, M.T.: Perfil de riesgo de pacientes adultos sometidos a traslado secundario por móviles avanzados del sistema de atención médica de urgencia del Área metropolitana. Revista médica de Chile 140, 1297–1303 (October 2012)Google Scholar
  8. 8.
    Chen, S.K., Kao, T., Chan, C.T., Huang, C.N., Chiang, C.Y., Lai, C.Y., Tung, T.H., Wang, P.C.: A reliable transmission protocol for zigbee-based wireless patient monitoring. IEEE Trans. Inf. Technol. Biomed. 16(1), 6–16 (2012)CrossRefGoogle Scholar
  9. 9.
    Chiauzzi, E., Rodarte, C., DasMahapatra, P.: Patient-centered activity monitoring in the self-management of chronic health conditions. BMC Med. 13(1), 77 (2015)CrossRefGoogle Scholar
  10. 10.
    Chu, Y., Ganz, A.: A mobile teletrauma system using 3g networks. IEEE Trans. Inf. Technol. Biomed. 8(4), 456–462 (2004)CrossRefGoogle Scholar
  11. 11.
    Dobkin, B.H., Dorsch, A.: The promise of mhealth daily activity monitoring and outcome assessments by wearable sensors. Neurorehabil. Neural Repair 25(9), 788–798 (2011)CrossRefGoogle Scholar
  12. 12.
    Mobile Medical Applications: Guidance for Industry and Food and Drug Administration Staff. USA: Food and Drug Administration (2013)Google Scholar
  13. 13.
    Gállego, J.R., Hernández-Solana, Á., Canales, M., Lafuente, J., Valdovinos, A., Fernández-Navajas, J.: Performance analysis of multiplexed medical data transmission for mobile emergency care over the umts channel. IEEE Trans. Inf. Technol. Biomed. 9(1), 13–22 (2005)CrossRefGoogle Scholar
  14. 14.
    Imhoff, B.F., Thompson, N.J., Hastings, M.A., Nazir, N., Moncure, M., Cannon, C.M.: Rapid emergency medicine score (rems) in the trauma population: a retrospective study. BMJ Open 4(5), e004738 (2014)CrossRefGoogle Scholar
  15. 15.
    Neyem, A., Ochoa, S.F., Pino, J.A., Franco, R.D.: A reusable structural design for mobile collaborative applications. J. Syst. Softw. 85(3), 511–524 (2012)CrossRefGoogle Scholar
  16. 16.
    Olsson, T., Lind, L.: Comparison of the rapid emergency medicine score and apache ii in nonsurgical emergency department patients. Acad. Emerg. Med. 10(10), 1040–1048 (2003)CrossRefGoogle Scholar
  17. 17.
    Pavlopoulos, S., Kyriacou, E., Berler, A., Dembeyiotis, S., Koutsouris, D.: A novel emergency telemedicine system based on wireless communication technology-ambulance. IEEE Trans. Inf. Technol. Biomed. 2(4), 261–267 (1998)CrossRefGoogle Scholar
  18. 18.
    Rahimi, M.R., Ren, J., Liu, C.H., Vasilakos, A.V., Venkatasubramanian, N.: Mobile cloud computing: a survey, state of art and future directions. Mobile Netw. Appl. 19(2), 133–143 (2014)CrossRefGoogle Scholar
  19. 19.
    Scully, C.G., Lee, J., Meyer, J., Gorbach, A.M., Granquist-Fraser, D., Mendelson, Y., Chon, K.H.: Physiological parameter monitoring from optical recordings with a mobile phone. IEEE Trans. Biomed. Eng. 59(2), 303–306 (2012)CrossRefGoogle Scholar
  20. 20.
    Sethi, D., Subramanian, S.: When place and time matter: how to conduct safe inter-hospital transfer of patients. Saudi J. Anaesth. 8(1), 104 (2014)CrossRefGoogle Scholar
  21. 21.
    Son, D., Lee, J., Qiao, S., Ghaffari, R., Kim, J., Lee, J.E., Song, C., Kim, S.J., Lee, D.J., Jun, S.W., et al.: Multifunctional wearable devices for diagnosis and therapy of movement disorders. Nat. Nanotechnol. 9(5), 397–404 (2014)CrossRefGoogle Scholar
  22. 22.
    Triantafyllidis, A.K., Koutkias, V.G., Chouvarda, I., Maglaveras, N.: A pervasive health system integrating patient monitoring, status logging, and social sharing. IEEE J. Biomed. Health Inform. 17(1), 30–37 (2013)CrossRefGoogle Scholar
  23. 23.
    van der Veen, J.S., van der Waaij, B., Meijer, R.J.: Sensor data storage performance: Sql or nosql, physical or virtual. In: 2012 IEEE 5th International Conference on Cloud Computing (CLOUD), pp. 431–438. IEEE (2012)Google Scholar
  24. 24.
    Yang, C.C., Hsu, Y.L.: A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors 10(8), 7772–7788 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Andrés Neyem
    • 1
  • Guillermo Valenzuela
    • 1
  • Nicolas Risso
    • 1
  • Juan S. Rojas-Riethmuller
    • 1
  • José I. Benedetto
    • 1
  • Marie J. Carrillo
    • 2
  1. 1.Computer Science DepartmentPontificia Universidad Católica de ChileSantiagoChile
  2. 2.Nursing SchoolUniversidad Católica de ChileSantiagoChile

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