A Sensitive Data Access Model in Support of Learning Health Systems
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 cycle...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Computers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-431X/10/3/25 |
| _version_ | 1797395144540946432 |
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| author | Thibaud Ecarot Benoît Fraikin Luc Lavoie Mark McGilchrist Jean-François Ethier |
| author_facet | Thibaud Ecarot Benoît Fraikin Luc Lavoie Mark McGilchrist Jean-François Ethier |
| author_sort | Thibaud Ecarot |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T00:30:11Z |
| format | Article |
| id | doaj.art-1e95d85f15594bc8a9e3a93b70ab3b13 |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-03-09T00:30:11Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-1e95d85f15594bc8a9e3a93b70ab3b132023-12-11T18:33:10ZengMDPI AGComputers2073-431X2021-02-011032510.3390/computers10030025A Sensitive Data Access Model in Support of Learning Health SystemsThibaud Ecarot0Benoît Fraikin1Luc Lavoie2Mark McGilchrist3Jean-François Ethier4Centre Interdisciplinaire de Recherche en Informatique de la Santé, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, CanadaCentre Interdisciplinaire de Recherche en Informatique de la Santé, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, CanadaCentre Interdisciplinaire de Recherche en Informatique de la Santé, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, CanadaCentre Interdisciplinaire de Recherche en Informatique de la Santé, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, CanadaCentre Interdisciplinaire de Recherche en Informatique de la Santé, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, CanadaGiven 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.https://www.mdpi.com/2073-431X/10/3/25healthcareprotocolsnetwork securitycommunication system securitydata security |
| spellingShingle | Thibaud Ecarot Benoît Fraikin Luc Lavoie Mark McGilchrist Jean-François Ethier A Sensitive Data Access Model in Support of Learning Health Systems Computers healthcare protocols network security communication system security data security |
| title | A Sensitive Data Access Model in Support of Learning Health Systems |
| title_full | A Sensitive Data Access Model in Support of Learning Health Systems |
| title_fullStr | A Sensitive Data Access Model in Support of Learning Health Systems |
| title_full_unstemmed | A Sensitive Data Access Model in Support of Learning Health Systems |
| title_short | A Sensitive Data Access Model in Support of Learning Health Systems |
| title_sort | sensitive data access model in support of learning health systems |
| topic | healthcare protocols network security communication system security data security |
| url | https://www.mdpi.com/2073-431X/10/3/25 |
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