Andrius Budrionis
Norwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway
Johan Gustav Bellika
Norwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway / Department of Clinical Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway
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Ingår i: Proceedings from The 15th Scandinavian Conference on Health Informatics 2017 Kristiansand, Norway, August 29–30, 2017
Linköping Electronic Conference Proceedings 145:8, s. 50-54
Publicerad: 2018-01-04
ISBN: 978-91-7685-364-1
ISSN: 1650-3686 (tryckt), 1650-3740 (online)
The learning healthcare system ideas brought novel approaches to knowledge generation and adoption in healthcare. High focus on data created in routine practice highlighted the underexplored potential for quality improvement, cost reduction and personalized care. While the high-level goals promise major improvement in healthcare delivery worldwide, it is less clear how such practices should be implemented and maintained. Technical infrastructure to ensure data access and processing capabilities is the initial building block enabling agile learning from practice. Selection between distributed data processing network and centralized repository as a fundament for the learning healthcare system has implications to further development of the system and its utility. This paper contrasts two architectural patterns and highlights their advantages and limitation to serve data access and processing needs in the learning healthcare. It provides comparative insights in decision making and assists in selecting an approach for implementation.
Learning healthcare systems, centralized, distributed, architecture, data reuse, infrastructure, primary care
[1] Asfaw Hailemichæl, M., Yitbarek Yigzaw, K., Bellika, J.G., 2015. Emnet: a System for Privacy-Preserving Statistical Computing on Distributed Health Data, in: Proceedings from The 13th Scandinavien Conference on Health Informatics. Presented at the The 13th Scandinavien Conference on Health Informatics, Linköping University Electronic Press, Linköpings universitet, Tromsø, Norway.
[2] Bellika, J.G., Henriksen, T.S., Yigzaw, K.Y., 2015. The snow system: A decentralized medical data processing system. Methods Mol. Biol. Clifton NJ 1246, 109–122. doi:10.1007/978-1-4939-1985-7_7
[3] Brown, J.S., Holmes, J.H., Shah, K., Hall, K., Lazarus, R., Platt, R., 2010. Distributed Health Data Networks: A Practical and Preferred Approach to Multi-Institutional Evaluations of Comparative Effectiveness, Safety, and Quality of Care. Med. Care 48, S45. doi:10.1097/MLR.0b013e3181d9919f
[4] Budrionis, A., Bellika, J.G., 2016. The Learning Healthcare System: Where are we now? A systematic review. J. Biomed. Inform. 64, 87–92. doi:10.1016/j.jbi.2016.09.018
[5] Greenhalgh, T., Howick, J., Maskrey, N., 2014. Evidence based medicine: a movement in crisis? The BMJ 348, g3725. doi:10.1136/bmj.g3725
[6] Institute of Medicine (US) Roundtable on Evidence-Based Medicine, 2007. The Learning
Healthcare System: Workshop Summary, The National Academies Collection: Reports funded by National Institutes of Health. National Academies Press (US), Washington (DC).
[7] Morris, Z.S., Wooding, S., Grant, J., 2011. The answer is 17 years, what is the question:
understanding time lags in translational research. J. R. Soc. Med. 104, 510–520. doi:10.1258/jrsm.2011.110180
[8] Tucker, K., Branson, J., Dilleen, M., Hollis, S., Loughlin, P., Nixon, M.J., Williams, Z., 2016. Protecting patient privacy when sharing patientlevel data from clinical trials. BMC Med. Res. Methodol. 16. doi:10.1186/s12874-016-0169-4
[9] U.S. Department of Health and Human Services, Office for Civil Rights, n.d. Breach Portal: Notice to the Secretary of HHS Breach of Unsecured Protected Health Information. Breaches Affecting 500 or More Individuals [WWW Document]. URL https://ocrportal.hhs.gov/ocr/breach/breach_report.jsf (accessed 5.16.17).
[10] Yigzaw, K.Y., Bellika, J.G., Andersen, A., Hartvigsen, G., Fernandez-Llatas, C., 2013.
Towards privacy-preserving computing on distributed electronic health record data. ACM Press, pp. 1–6. doi:10.1145/2541534.2541593
