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 Institute Medicine, Learning Health System (LHS) framework emerged in early 2000s. It places focus learning cycles where care delivery is tightly coupled with research activities, which turn closely tied transfer, ultimately injecting solid improvements into medical practice. Sensitive health data access across multiple organisations therefore paramount support LHSs. While LHS vision well established, security requirements them are not. exchange approaches have been implemented (e.g., HL7 FHIR) or proposed blockchain-based methods), but none cover entire requirement spectrum. To address this, Data Access Model (SDAM) proposed. Using a representation agents processes systems, specific presented SDAM layer architecture described, an emphasis its mix-network dynamic topology approach. A clinical application benefiting from model subsequently analysis evaluates properties vulnerability mitigation strategies offered protocol suite following parallel, FHIR.