A Sensitive Data Access Model in Support of Learning Health Systems
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Centre Interdisciplinaire de Recherche en Informatique de la Santé, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
2
Department of Health and Clinical Services, University of Dundee, Dundee DD1 4HN, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Antonio Celesti, Ivanoe De Falco, Antonino Galletta and Giovanna Sannino
Computers 2021, 10(3), 25; https://doi.org/10.3390/computers10030025
Received: 26 January 2021
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Revised: 19 February 2021
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Accepted: 23 February 2021
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Published: 26 February 2021
(This article belongs to the Special Issue Artificial Intelligence for Health)
Given the ever-growing body of knowledge, healthcare improvement hinges more than ever on efficient knowledge transfer to clinicians and patients. Promoted initially by the Institute of Medicine, the Learning Health System (LHS) framework emerged in the early 2000s. It places focus on learning cycles where care delivery is tightly coupled with research activities, which in turn is closely tied to knowledge transfer, ultimately injecting solid improvements into medical practice. Sensitive health data access across multiple organisations is therefore paramount to support LHSs. While the LHS vision is well established, security requirements to support them are not. Health data exchange approaches have been implemented (e.g., HL7 FHIR) or proposed (e.g., blockchain-based methods), but none cover the entire LHS requirement spectrum. To address this, the Sensitive Data Access Model (SDAM) is proposed. Using a representation of agents and processes of data access systems, specific security requirements are presented and the SDAM layer architecture is described, with an emphasis on its mix-network dynamic topology approach. A clinical application benefiting from the model is subsequently presented and an analysis evaluates the security properties and vulnerability mitigation strategies offered by a protocol suite following SDAM and in parallel, by FHIR.
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MDPI and ACS Style
Ecarot, T.; Fraikin, B.; Lavoie, L.; McGilchrist, M.; Ethier, J.-F. A Sensitive Data Access Model in Support of Learning Health Systems. Computers 2021, 10, 25. https://doi.org/10.3390/computers10030025
AMA Style
Ecarot T, Fraikin B, Lavoie L, McGilchrist M, Ethier J-F. A Sensitive Data Access Model in Support of Learning Health Systems. Computers. 2021; 10(3):25. https://doi.org/10.3390/computers10030025
Chicago/Turabian StyleEcarot, Thibaud, Benoît Fraikin, Luc Lavoie, Mark McGilchrist, and Jean-François Ethier. 2021. "A Sensitive Data Access Model in Support of Learning Health Systems" Computers 10, no. 3: 25. https://doi.org/10.3390/computers10030025
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