TY - JOUR
T1 - A sensitive data access model in support of learning health systems
AU - Ecarot, Thibaud
AU - Fraikin, Benoît
AU - Lavoie, Luc
AU - McGilchrist, Mark
AU - Ethier, Jean François
N1 - Funding Information:
Funding: This work was supported in part by the Unité de Soutien SRAP du Québec, Health Data Research Network Canada with the Canadian Data Platform and the Centre interdisciplinaire de recherche en informatique de la santé de l’Université de Sherbrooke (CIRIUS).
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/2/26
Y1 - 2021/2/26
N2 - 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.
AB - 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.
KW - Communication system security
KW - Data security
KW - Healthcare
KW - Network security
KW - Protocols
UR - http://www.scopus.com/inward/record.url?scp=85102727470&partnerID=8YFLogxK
U2 - 10.3390/computers10030025
DO - 10.3390/computers10030025
M3 - Article
AN - SCOPUS:85102727470
VL - 10
JO - Computers
JF - Computers
IS - 3
M1 - 25
ER -
