Trust and Provenance in Communication to eHealth Consumers

  • Grant P. Cumming
  • Tara French
  • Jamie Hogg
  • Douglas McKendrick
  • Heidi Gilstad
  • David Molik
  • Joanne S Luciano
Chapter

Abstract

The future of medicine is shifting to a patient-centric model. One aspect of this model involves an increasing utility to triangulate health-related data. These data sources will be of variable quality and new ways of kitemarking and weighting the data is required. The exemplar of I-Choose may provide a framework for health-related searches with the end user providing the weighting of the criteria used in the search algorithms. NHS Grampian’s No Delays concept uses video on demand to provide clinician-made personalised patient postcards to the patient. This chapter explores trust and provenance issues arising from health care delivered via the Internet and how the end user engages with the technology rather than the wishful hope of “building it and they will use it”.

References

  1. Abaidoo, Benjamin, and Benjamin Teye Larweh. 2014. “Consumer Health Informatics: The Application of ICT in Improving Patient-Provider Partnership for a Better Health Care,” Online Journal of Public Health Informatics 6: 2, doi:  10.5210/ojphi.v6i2.4903. CrossRefGoogle Scholar
  2. Anderson, Janna, and Lee Rainie. 2014. “The Internet of Things Will Thrive by 2025,” Pew Research Internet Project: 14.Google Scholar
  3. Arthur, W. Brian. 2009. The Nature of Technology: What It Is and How It Evolves. New York: Free Press.Google Scholar
  4. Ashton, Kevin. 2009. “That ‘Internet of Things’ Thing,” RFiD Journal 22, no. 7: 97–114Google Scholar
  5. Bergmo T. S. 2015. “How to Measure Costs and Benefits of eHealth Interventions: An Overview of Methods and Frameworks,” Journal of Medical Internet Research 17, no. 11:e254. doi:  10.2196/jmir.4521.CrossRefGoogle Scholar
  6. Bowen, S. 2010. “Critical theory and participatory design,” Proceedings of CHI. https://www.cl.cam.ac.uk/events/experiencingcriticaltheory/Bowen-ParticipatoryDesign.pdf
  7. Buneman, Peter, Sanjeev Khanna, and Wang-Chiew Tan. 2000. “Data Provenance: Some Basic Issues,” FST TCS 2000: Foundations of Software Technology and Theoretical Computer Science, ed. Sanjiv Kapoor and Sanjiva Prasad, Vol. 1974 (Berlin, Heidelberg: Springer Berlin Heidelberg), 87–93. http://link.springer.com/10.1007/3-540-44450-5_6 CrossRefGoogle Scholar
  8. Chan, Connie V., and David R. Kaufman. 2011. “A Framework for Characterizing eHealth Literacy Demands and Barriers,” Journal of Medical Internet Research 13, no. 4: e94, doi:  10.2196/jmir.1750. CrossRefGoogle Scholar
  9. Cluster of European Research Projects on the Internet of Things (CERP-IoT). 2009. “Internet of Things, Strategic Research Roadmap,” European Commission. http://www.internet-of-things-research.eu/pdf/IoT_Cluster_Strategic_Research_Agenda_2009.pdf. Accessed August 28, 2016.
  10. Cumming, Grant P. 2014 “Connecting & Collaborating—Healthcare for the 21st Century,” (Proceedings of the Second European Workshop on Practical Aspects of Health Informatics [PAHI], Trondheim, Norway, May 19–20, 2014), http://ceur-ws.org/Vol-1251/abstract1.pdf. Accessed August 28, 2016.
  11. De La Torre-Díez, Isabel et al. 2015. “Cost-Utility and Cost-Effectiveness Studies of Telemedicine, Electronic, and Mobile Health Systems in the Literature: A Systematic Review,” Telemedicine and E-Health 21, no. 2: 81–85, doi:  10.1089/tmj.2014.0053. CrossRefGoogle Scholar
  12. Delamothe, Tony. 2000. “Quality of Websites: Kitemarking the West Wind,” BMJ 321, no. 7265: 843–844, doi:  10.1136/bmj.321.7265.843. CrossRefGoogle Scholar
  13. Devlin AM, et al. 2016. “Delivering digital health and well-being at scale: lessons learned during the implementation of the dallas program in the United Kingdom,” Journal of the American Medical Informatics Association 23, no. 1: 48–59, doi: 10.1093/jamia/ocv097.CrossRefGoogle Scholar
  14. Evans, Dave. 2011. “CISCO White Paper,” The Internet of Things: How the Next Evolution of the Internet Is Changing Everything. https://www.cisco.com/c/dam/en_us/about/ac79/docs/innov/IoT_IBSG_0411FINAL.pdf. Accessed August 28, 2016.
  15. Fox, Susannah. 2011. The Social Life of Health Information. Washington, DC: Pew Internet & American Life Project. http://www.pewinternet.org/files/old-media//Files/Reports/2011/PIP_Social_Life_of_Health_Info.pdf Google Scholar
  16. French, T., Teal, G., and Raman, S. 2016. “Experience Labs: co-creating health and care innovations using design tools and artefacts,” In: Lloyd, P., Bohemia, E., eds. Proceedings of DRS2016: Design + Research + Society - Future-Focused Thinking, Brighton, UK, 7: 2965–2979.Google Scholar
  17. Gilstad, Heidi. 2014. “Toward a Comprehensive Model of eHealth Literacy,” Paper for Practical Aspects of health Informatics (PAHI), CEUR Workshop Proceedings 2014, 63–72.Google Scholar
  18. Glasser, John. 2015. “How The Internet of Things Will Affect Health Care,” Hospitals & Health Networks. http://www.hhnmag.com/articles/3438-how-the-internet-of-things-will-affect-health-care. Accessed May 21, 2016.
  19. Greenhalgh T, and Russell J. 2010. “Why Do Evaluations of eHealth Programs Fail? An Alternative Set of Guiding Principles,” PLoS Med 7, no. 11:e1000360. doi: 10.1371/journal.pmed.1000360.CrossRefGoogle Scholar
  20. Guion, Lisa Ann. 2002. Triangulation: Establishing the Validity of Qualitative Studies. Gainesville, FL: University of Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, EDIS.Google Scholar
  21. Hancock, Trevor, and Clement Bezold. 1993. “Possible Futures, Preferable Futures,” The Healthcare Forum Journal 37: 23–29.Google Scholar
  22. Health On the Net Foundation. 2016. https://www.healthonnet.org/. Accessed May 21, 2016.
  23. Hendler, Jim. 2013. “Broad Data: Exploring the Emerging Web of Data,” Big Data 1, no. 1: 18–20, doi:  10.1089/big.2013.1506. CrossRefGoogle Scholar
  24. Higgins, O. et al. 2011. A Literature Review on Health Information Seeking Behaviour on the Web: A Health Consumer and Health Professional Perspective. Stockholm, Sweden: European Centre for Disease Control.Google Scholar
  25. Jarman, H. and L. F. Luna-Reyes (Eds.) 2016. Private Data and Public Value: Governance, Green Consumption, and Sustainable Supply Chains. Public Administration and Information Technology (PAIT) Series, Springer.Google Scholar
  26. Kushniruk, Andre W. 2015. “Editorial: eHealth Literacy: Emergence of a New Concept for Creating, Evaluating and Understanding Online Health Resources for the Public,” Knowledge Management & E-Learning 7, no. 4: 518–521.Google Scholar
  27. Luciano, Joanne S. et al. 2013. “The Emergent Discipline of Health Web Science,” Journal of Medical Internet Research 15, no. 8: e166, doi:  10.2196/jmir.2499.CrossRefGoogle Scholar
  28. Luciano, Joanne S. et al. 2014. “Health Web Science,” Foundations and Trends in Web Science 4, no. 4: 269–419, doi:  10.1561/1800000019.CrossRefGoogle Scholar
  29. Luciano, Joanne S. et al. 2016. “Using Ontologies to Develop and Test a Certification and Inspection Data Infrastructure Building Block,” Private Data and Public Value, ed. Holly Jarman and Luis F. Luna-Reyes (Cham: Springer International Publishing), 89–107. http://link.springer.com/10.1007/978-3-319-27823-0_5 CrossRefGoogle Scholar
  30. McHattie LS, Cumming G, and French T. 2014. “Transforming Patient Experience: Health Web Science Meets Medicine 2.0,” Medicine 2.0. 3, no. 1: e2. doi:  10.2196/med20.3128.
  31. Moreland, Julia, Tara L. French, and Grant P. Cumming. 2015. “The Prevalence of Online Health Information Seeking Among Patients in Scotland: A Cross-Sectional Exploratory Study,” JMIR Research Protocols 4, no. 3:e85, doi:  10.2196/resprot.4010. CrossRefGoogle Scholar
  32. National Network of Libraries of Medicine. 2016. http://nnlm.gov/. Accessed May 21, 2016.
  33. New Scientist. 2016. “The Algorithms That Make Sense of Your Health Data,” https://www.newscientist.com/article/dn28338-happily-healthy/. Accessed May 21, 2016.
  34. Norman, Cameron. 2011. “eHealth Literacy 2.0: Problems and Opportunities With an Evolving Concept,” Journal of Medical Internet Research 13, no. 4:e125, doi:  10.2196/jmir.2035.CrossRefGoogle Scholar
  35. Norman, Cameron D., and Harvey A. Skinner. 2006. “eHealth Literacy: Essential Skills for Consumer Health in a Networked World,” Journal of Medical Internet Research 8, no. 2: e9, doi:  10.2196/jmir.8.2.e9.CrossRefGoogle Scholar
  36. P4Medicine, 2016. P4 Medicine Institute. http://p4mi.org/p4medicine. Accessed May 20, 2016.
  37. Pamela, Shiao, S. et al. 2015. “Big Data Analytics on Common Gene Mutations in Epigenetics Methylation Pathways: Population Health Issues for Cancer Prevention,” presented at the 2015 AACR Computational and Systems Biology of Cancer Conference, San Francisco, California, February 8–11, 2015, doi:  10.13140/2.1.4214.1762
  38. Pentland, A. 2012. “Big Data’s Biggest Obstacles,” Harvard Business Review Blog. https://hbr.org/2012/10/big-datas-biggest-obstacles. Accessed May 21, 2016.
  39. Quaglio, Gianluca et al. 2016. “Accelerating the Health Literacy Agenda in Europe,” Health Promotion International, daw028, doi:  10.1093/heapro/daw028
  40. Sayogo, Djoko S. et al. 2016. “Ontological Modeling of Certification and Inspection Process to Support Smart Disclosure of Product Information,” International Journal of Public Administration in the Digital Age 3, no. 2: 86–108, doi:  10.4018/IJPADA.2016040106.CrossRefGoogle Scholar
  41. Shiao, S. P. K., Yu, C. A., Xie, C., Ho, S. (2015, February). Big Data Analytics on Common Gene Mutations in Epigenetics Methylation Pathways: Population Health Issues for Cancer Prevention. 2015 AACR Computational and Systems Biology of Cancer Conference, San Francisco, California. DOI:  10.13140/2.1.4214.1762.
  42. Stellefson, Michael et al. 2011. “eHealth Literacy Among College Students: A Systematic Review with Implications for eHealth Education,” Journal of Medical Internet Research 13, no. 4: e102, doi:  10.2196/jmir.1703. CrossRefGoogle Scholar
  43. Swan, Melanie. 2013. “The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery,” Big Data 1, no. 2: 85–99, doi:  10.1089/big.2012.0002. CrossRefGoogle Scholar
  44. Tiropanis, Thanassis et al. 2013. “The Web Science Observatory,” IEEE Intelligent Systems 28, no, 2: 100–104.CrossRefGoogle Scholar
  45. Topol, Eric J. 2012. The Creative Destruction of Medicine: How the Digital Revolution Will Create Better Health Care. New york: Basic Books.Google Scholar
  46. Tucker, Patrick. 2014. The Naked Future: What Happens in a World That Anticipates Your Every Move? New york: Penguin.Google Scholar
  47. Wooder, Stella. 2016. “Connected Women: How Medical Devices Are Set to Revolutionise Women’s Healthcare,” Team Consulting Ltd. https://www.teamconsulting.com/insights/connected-women-how-medical-devices-are-set-to-revolutionise-womens-healthcare/. Accessed May 21, 2016.

Copyright information

© The Author(s) 2017

Authors and Affiliations

  • Grant P. Cumming
    • 1
    • 9
  • Tara French
    • 2
  • Jamie Hogg
    • 3
  • Douglas McKendrick
    • 4
  • Heidi Gilstad
    • 5
  • David Molik
    • 6
  • Joanne S Luciano
    • 7
    • 8
  1. 1.Dr Gray’s Hospital, Elgin, Scotland, NHS GrampianNHS Grampian, University of Highlands and IslandsElginUK
  2. 2.Institute of Design InnovationThe Glasgow School of ArtGlasgowUK
  3. 3.Dr Gray’s Hospital, ElginNHS GrampianAberdeenUK
  4. 4.Dr Gray’s Hospital, ElginNHS Grampian and University of AberdeenAberdeenUK
  5. 5.Health Informatics Research Group, Department of Neuromedicine and Movement Science, NTNU Norwegian University of Science and TechnologySchool of Medicine, NTNUTrondheimNorway
  6. 6.Cold Spring Harbor LaboratoryNewYorkUSA
  7. 7.School of Informatics and ComputingIndiana UniversityBloomingtonUSA
  8. 8.Predictive Medicine, Inc.BelmontUSA
  9. 9.University of AberdeenAberdeenUK

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