Abstract
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.
Highlights
Despite extraordinary research successes, many patients receive sub-optimal care.This includes cases where required action is evident, like for peripheral vascular disease patients and cholesterol lowering drugs even when there are publications in high “impact” journals
We present the Sensitive Data Access
Published approaches supporting health data access were surveyed to evaluate if one was fulfilling all high-level requirements and could serve as a basis for the development of Sensitive Data Access Model (SDAM). We reviewed both largely implemented approaches, like Health Level 7 (HL7) standards, as well as more recent technics, like those based on Computers 2021, 10, 25 blockchains
Summary
This includes cases where required action is evident, like for peripheral vascular disease patients and cholesterol lowering drugs (example in [1]) even when there are publications in high “impact” journals. There is, an urgent need for integrated knowledge transfer tools, like decision support systems and audit-feedback tools, to maximally increase care quality for patients [2]. It is to fill this gap that the Learning Health System (LHS). In a LHS, the focus is not on a research protocol, but rather on a learning cycle. The cycle workflow starts by looking at data naturally produced during care delivery (increasing pertinence), includes the research activities, and structures knowledge transfer actions right from the planning stage
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