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Healthcare Systems: An Overview of the Most Important Aspects of Current and Future m-Health Applications

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Connected Health in Smart Cities

Abstract

This chapter explores the most relevant aspects in relation to the outcomes and performance of the different components of a healthcare system with a particular focus on mobile healthcare applications. In detail, we discuss the six quality principles to be satisfied by a generic healthcare system and the main international and European projects, which have supported the dissemination of these systems. This diffusion has been encouraged by the application of wireless and mobile technologies, through the so-called m-Health systems. One of the main fields of application of an m-Health system is telemedicine, for which reason we will address an important challenge encountered during the realization of an m-Health application: the analysis of the functionalities that an m-Health app has to provide. To achieve this latter aim, we will present an overview of a generic m-Health application with its main functionalities and components. Among these, the use of a standardized method for the treatment of a massive amount of patient data is necessary in order to integrate all the collected information resulting from the development of a great number of new m-Health devices and applications. Electronic Health Records (EHR), and international standards, like Health Level 7 (HL7) and Fast Healthcare Interoperability Resources (FHIR), aims at addressing this important issue, in addition to guaranteeing the privacy and security of these health data. Moreover, the insights that can be discerned from an examination of this vast repository of data can open up unparalleled opportunities for public and private sector organizations. Indeed, the development of new tools for the analysis of data, which on occasions may be unstructured, noisy, and unreliable, is now considered a vital requirement for all specialists who are involved in the handling and using of information. These new tools may be based on rule, machine or deep learning, or include question answering, with cognitive computing certainly having a key role to play in the development of future m-Health applications.

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References

  1. G. Eysenbach, What is e-health? J. Med. Internet Res. 3, e20 (2001)

    Article  Google Scholar 

  2. World Health Organization, Everybody’s Business—Strengthening Health Systems to Improve Health Outcomes: WHO’s Framework for Action (World Health Organization, Geneva, 2007)

    Google Scholar 

  3. T.A. Sanner, L.K. Roland, K. Braa, From pilot to scale: Towards an mHealth typology for low-resource contexts. Health Policy Technol. 1, 155–164 (2012)

    Article  Google Scholar 

  4. S. Agarwal, L. Rosenblum, T. Goldschmidt, M. Carras, A. Labrique, Mobile Technology in Support of Frontline Health Workers. A Comprehensive Overview of the Landscape Knowledge Gaps and Future Directions (Johns Hopkins University Global mHealth Initiative, Baltimore, MA, 2016)

    Google Scholar 

  5. L.T. Kohn, J. Corrigan, M.S. Donaldson, To Err Is Human: Building a Safer Health System, vol 6 (National Academy Press, Washington, DC, 2000)

    Google Scholar 

  6. World Health Organization, Global Diffusion of eHealth: Making Universal Health Coverage Achievable: Report of the Third Global Survey on eHealth (World Health Organization, Geneva, 2016)

    Google Scholar 

  7. World Bank, Mobile Phone Access Reaches Three Quarters of planet's Population (The World Bank, Washington, DC, 2012)

    Google Scholar 

  8. M. Kumar, S. Wambugu, A Primer on the Security, Privacy, and Confidentiality of Electronic Health Records (MEASURE Evaluation, University of North Carolina, Chapel Hill, NC, 2015)

    Google Scholar 

  9. M. Kay, J. Santos, M. Takane, mHealth: New Horizons for Health through Mobile Technologies, vol 64 (World Health Organization, Geneva, 2011), pp. 66–71

    Google Scholar 

  10. R.S. Istepanian, E. Jovanov, Y. Zhang, Guest editorial introduction to the special section on m-health: Beyond seamless mobility and global wireless health-care connectivity. IEEE Trans. Inf. Technol. Biomed. 8, 405–414 (2004)

    Article  Google Scholar 

  11. A.B. Labrique, L. Vasudevan, E. Kochi, R. Fabricant, G. Mehl, mHealth innovations as health system strengthening tools: 12 common applications and a visual framework. Glob. Health Sci. Pract. 1, 160–171 (2013)

    Article  Google Scholar 

  12. WHO Toolkit (2019). http://www.dhis2.org/

  13. R. Wootton, Twenty years of telemedicine in chronic disease management—An evidence synthesis. J. Telemed. Telecare 18, 211–220 (2012)

    Article  Google Scholar 

  14. S. McLean, A. Sheikh, K. Cresswell, U. Nurmatov, M. Mukherjee, A. Hemmi, et al., The impact of telehealthcare on the quality and safety of care: A systematic overview. PLoS One 8, e71238 (2013)

    Article  Google Scholar 

  15. A.G. Ekeland, A. Bowes, S. Flottorp, Effectiveness of telemedicine: A systematic review of reviews. Int. J. Med. Inform. 79, 736–771 (2010)

    Article  Google Scholar 

  16. E.A. Krupinski, History of telemedicine: Evolution, context, and transformation. Telemed. J E Health 15, 804–805 (2009)

    Article  Google Scholar 

  17. R. Rayman, Telemedicine: Military applications. Aviat. Space Environ. Med. 63, 135–137 (1992)

    Google Scholar 

  18. E.M. Brown, The Ontario telemedicine network: A case report. Telemed. J E Health 19, 373–376 (2013)

    Article  Google Scholar 

  19. L. Uscher-Pines, A. Mehrotra, Analysis of Teladoc use seems to indicate expanded access to care for patients without prior connection to a provider. Health Aff. 33, 258–264 (2014)

    Article  Google Scholar 

  20. M. Hawkins, Physician appointment wait times and Medicaid and Medicare acceptance rates, in Report of Merritt Hawkins (2014)

    Google Scholar 

  21. K.N. Ray, A.V. Chari, J. Engberg, M. Bertolet, A. Mehrotra, Disparities in time spent seeking medical care in the United States. JAMA Intern. Med. 175, 1983–1986 (2015)

    Article  Google Scholar 

  22. NIHCM Foundation, in Health Care Is Big Spenders: The Characteristics behind the Curve (2016). http://www.nihcm.org/topics/cost-quality/health-cares-big-spenders-chart-story

    Google Scholar 

  23. S. Naddeo, L. Verde, M. Forastiere, G. De Pietro, G. Sannino, A real-time m-health monitoring system: An integrated solution combining the use of several wearable sensors and Mobile devices, in HEALTHINF (2017), pp. 545–552

    Google Scholar 

  24. M. Forastiere, G. De Pietro, G. Sannino, An mHealth application for a personalized monitoring of one’s own wellness: Design and development, in International Conference on Innovation in Medicine and Healthcare, (Springer, Cham, 2016), pp. 269–278

    Google Scholar 

  25. L. Verde, G. De Pietro, P. Veltri, G. Sannino, An m-health system for the estimation of voice disorders, in IEEE International Conference on Multimedia & Expo Workshops (ICMEW), (IEEE, 2015), pp. 1–6

    Google Scholar 

  26. M.S. Hossain, G. Muhammad, A. Alamri, Smart healthcare monitoring: A voice pathology detection paradigm for smart cities. Multimedia Systems 32, 1–11 (2017)

    Google Scholar 

  27. M.S. Hossain, Cloud-supported cyber–physical localization framework for patients monitoring. IEEE Syst. J. 11, 118–127 (2017)

    Article  Google Scholar 

  28. S.R. Levine, M. Gorman, Telestroke. Stroke 30(2), 464–469 (1999)

    Article  Google Scholar 

  29. K.M. McConnochie, N.E. Wood, H.J. Kitzman, N.E. Herendeen, J. Roy, K.J. Roghmann, Telemedicine reduces absence resulting from illness in urban child care: Evaluation of an innovation. Pediatrics 115, 1273–1282 (2005)

    Article  Google Scholar 

  30. T. Daschle, E.R. Dorsey, The return of the house CallThe return of the house call. Ann. Intern. Med. 162, 587–588 (2015)

    Article  Google Scholar 

  31. P.T. Courneya, K.J. Palattao, J.M. Gallagher, HealthPartners’ online clinic for simple conditions delivers savings of $88 per episode and high patient approval. Health Aff. (Millwood) 32, 385–392 (2013)

    Article  Google Scholar 

  32. G.F. Anderson, Chronic Care: Making the Case for Ongoing Care (Robert Wood Johnson Foundation, Princeton, NJ, 2010)

    Google Scholar 

  33. D. Bender, K. Sartipi, HL7 FHIR: An agile and RESTful approach to healthcare information exchange, in Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems (2013), pp. 326–331

    Google Scholar 

  34. R.L. Garrie, P.E. Paustian, Mhealth regulation, legislation, and cybersecurity, in mHealth, ed. by R. S. H. Istepanian, S. Laxminarayan, C. S. Pattichis, (Springer, Boston, MA, 2014), pp. 45–63

    Google Scholar 

  35. H. Okhravi, T. Hobson, D. Bigelow, W. Streilein, Finding focus in the blur of moving-target techniques. IEEE Secur. Priv. 12, 16–26 (2014)

    Article  Google Scholar 

  36. A. Eldosouky, W. Saad, On the cybersecurity of m-health IoT systems with LED bitslice implementation. in 2018 IEEE International Conference on Consumer Electronics (ICCE), 1–6 (2018)

    Google Scholar 

  37. D. Ferebee, V. Shandilya, C. Wu, J. Ricks, D. Agular, K. Cole, et al., A secure framework for mHealth data analytics with visualization, in 2016 IEEE 35th International Performance Computing and Communications Conference (IPCCC), (2016), pp. 1–4

    Google Scholar 

  38. G. Pravettoni, R. Folgieri, C. Lucchiari, Cognitive science in telemedicine: From psychology to artificial intelligence, in Tele-Oncology, ed. by G. Gatti, G. Pravettoni, F. Capello, (Springer, Cham, 2015), pp. 5–22

    Chapter  Google Scholar 

  39. S. Pouriyeh, S. Vahid, G. Sannino, G. D. Pietro, H. Arabnia, J. Gutierrez, A comprehensive investigation and comparison of Machine Learning Techniques in the domain of heart disease," in 2017 IEEE Symposium on Computers and Communications (ISCC) (2017), pp. 204–207

    Google Scholar 

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Correspondence to Giovanna Sannino .

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Sannino, G., De Pietro, G., Verde, L. (2020). Healthcare Systems: An Overview of the Most Important Aspects of Current and Future m-Health Applications. In: El Saddik, A., Hossain, M., Kantarci, B. (eds) Connected Health in Smart Cities. Springer, Cham. https://doi.org/10.1007/978-3-030-27844-1_11

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  • DOI: https://doi.org/10.1007/978-3-030-27844-1_11

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