Sharing of Big Data in Healthcare: Public Opinion, Trust, and Privacy Considerations for Health Informatics Researchers

Laura Moss, Martin Shaw, Ian Piper, Christopher Hawthorne, John Kinsella

Abstract

Advances in technology has transformed clinical medicine; electronic patient records routinely store clinical notes, internet-enabled mobile apps support self-management of chronic diseases, point-of-care testing enables laboratory tests to be performed outside of hospital environments, patient treatment can be delivered over wide geographic areas and wireless sensor networks are able to collect and send physiological data. Increasingly, this technology leads to the development of large databases of sensitive electronic patient information. There is public interest into the secondary use of this data; many concerns are voiced about the involvement of private companies and the security and privacy of this data, but at the same time, these databases present a valuable source of clinical information which can drive health informatics and clinical research, leading to improved patient treatment. In this position paper, we argue that for health informatics projects to be successful, public concerns over the secondary use of patient data need to be addressed in the design and implementation of the technology and conduct of the research project.

References

  1. A.Ferguson (2012). The evolution of confidentiality in the united kingdom and the west. AMA Journal of Ethics, 14(9):738-742.
  2. Aitken, M., Cunningham-Burley, S., and Pagliari, C. (2016a). Moving from trust to trustworthiness: Experiences of public engagement in the scottish health informatics programme. Science and Public Policy, 43(5):713-723.
  3. Aitken, M., de St Jorre, J., Pagliari, C., Jepson, R., and Cunningham-Burley, S. (2016b). Public responses to the sharing and linkage of health data for research purposes: a systematic review and thematic synthesis of qualitative studies. BMC Medical Ethics, 17(73).
  4. Apache a (2016). Apache Spark. https://spark.apache.org/. Accessed: Nov 2016.
  5. Apache b (2016). Apache Hadoop. https://hadoop.apache.org/. Accessed: Nov 2016.
  6. BMA (2016). Secondary Uses of Data, Public Workshop. https://www.bma.org.uk/collective-voice/policyand-research/ethics/secondary-uses-of-data/publicworkshop. Accessed: Nov 2016.
  7. Caldicott (2016). Information: To share or not to share? The Information Governance Review. https://www.gov.uk/government/uploads/system/ uploads /attachment data/file/192572/2900774 InfoGovernance accv2.pdf. Accessed: Nov 2016.
  8. CHART-ADAPT (2016). CHART-ADAPT. http://www.chartadapt.org. Accessed: Nov 2016.
  9. DeepMind a (2016). DeepMind Acute Kidney Injury. Royal Free London. Google DeepMind: Q&A. https://www.royalfree.nhs.uk/newsmedia/news/google-deepmind-qa/. Accessed: Nov 2016.
  10. DeepMind b (2016). DeepMind Moorfields Eye Hospital. Moorfields announces research partnership. http://www.moorfields.nhs.uk/news/moorfieldsannounces-research-partnership. Accessed: Nov 2016.
  11. Fillmore, C., Braye, C., and Kawamoto, K. (2013). Systematic review of clinical decision support interventions with potential for inpatient cost reduction. BMC Med Inform Decis Mak, 13(135).
  12. Focus Groups, Stevenson, F., Lloyd, N., Harrington, L., and Wallace, P. (2013). Use of electronic patient records for research: views of patients and staff in general practice. Fam Pract, 30(2):227-23.
  13. GDPR (2016). GDPR: Regulation (EU) 2016/679. http://ec.europa.eu/justice/dataprotection/reform/files/regulation oj en.pdf. Accessed: Nov 2016.
  14. Big Data. Extracting the 4 V's of big http://www.ibmbigdatahub.com/infographic/extracting -business-value-4-vs-big-data. Accessed: Nov 2016. b (2016). The 4 V's of big data.
  15. http://www.ibmbigdatahub.com/infographic/fourvs-big-data. Accessed: Nov 2016.
  16. IBM c (2016). IBM's Watson supercomputer to speed up cancer care. http://www.bbc.co.uk/news/technology32607688. Accessed: Nov 2016.
  17. Jaspers, M., Smeulers, M., Vermeulen, H., and Peute, L. (2011). Effects of clinical decision-support systems on practitioner performance and patient outcomes: a synthesis of high-quality systematic review findings. J Am Med Inform Assoc, 18(3):327-34.
  18. Kaye, J., Whitley, E., Lund, D., Morrison, M., Teare, H., and Melham, K. (2015). Dynamic consent: a patient interface for twenty-first century research networks. Eur J Hum Genet, 23(2):141-6.
  19. Kinsella, J., Hawthorne, C., Shaw, M., Piper, I., Healthcare, P., Aridhia, and L.Moss (2017). Public perception of the collection and use of critical care patient data beyond treatment: a pilot study. In Proceedings of the Society of Critical Care Medicine Congress (SCCM). SCCM.
  20. Kitchen, R. and McArdle, G. (2016). What makes big data, big data? exploring the ontological characteristics of 26 datasets. Big Data & Society, Jan-June 2016(3):1- 10.
  21. Scientist (2016). Revealed: Google has access to huge haul of NHS tient data. New Scientist 2016 Apr https://www.newscientist.com/article/\2086454- revealed-google-\ai-has-access-to-\huge-haul-ofnhs-patient-data/. Accessed: Nov 2016.
  22. Presser, L., Hruskova, M., Rowbottom, H., and Kancir, J. (2015). Care.data and access to uk health records: patient privacy and public trust. Technology Science, 2015081103(Aug 11).
  23. Riordan, F., Papoutsi, C., Reed, J., Marston, C., Bell, D., and Majeed, A. (2015). Patient and public attitudes towards informed consent models and levels of awareness of electronic health records in the uk. Int J Med Inform, 84(4):237-347.
  24. (2016). Scala Programming Language.
  25. http://www.scala-lang.org/. Accessed: Nov 2016.
  26. van Staa, T.-P., Goldacre, B., Buchan, I., and Smeeth, L. (2016). Big health data: the need to earn public trust. BMJ, 354:i3636.
  27. Williams, H., Spencer, K., Sanders, K., Lund, D., Whitley, E., Kaye, J., and Dixon, W. (2015). Dynamic consent: A possible solution to improve patient confidence and trust in how electronic patient records are used in medical research. JMIR Med Inform, 3(1).
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Paper Citation


in Harvard Style

Moss L., Shaw M., Piper I., Hawthorne C. and Kinsella J. (2017). Sharing of Big Data in Healthcare: Public Opinion, Trust, and Privacy Considerations for Health Informatics Researchers . In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017) ISBN 978-989-758-213-4, pages 463-468. DOI: 10.5220/0006251504630468


in Bibtex Style

@conference{healthinf17,
author={Laura Moss and Martin Shaw and Ian Piper and Christopher Hawthorne and John Kinsella},
title={Sharing of Big Data in Healthcare: Public Opinion, Trust, and Privacy Considerations for Health Informatics Researchers},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017)},
year={2017},
pages={463-468},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006251504630468},
isbn={978-989-758-213-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017)
TI - Sharing of Big Data in Healthcare: Public Opinion, Trust, and Privacy Considerations for Health Informatics Researchers
SN - 978-989-758-213-4
AU - Moss L.
AU - Shaw M.
AU - Piper I.
AU - Hawthorne C.
AU - Kinsella J.
PY - 2017
SP - 463
EP - 468
DO - 10.5220/0006251504630468