: Healthcare ecosystems face persistent identity-fragmentation challenges that undermine secure, interoperable data access across Electronic Health Records (EHR), telemedicine platforms and clinical Application Programming Interfaces (APIs). Existing Identity and Access Management (IAM) solutions rely on static role assignments and reactive audit mechanisms ill-suited to dynamic clinical environments. This paper presents the Zero-Trust Patient Identity Engineering Framework for Healthcare (ZTPIF-H), which integrates decentralised identity, Artificial Intelligence (AI)-driven adaptive authorisation, smart-contract-based consent governance and blockchain-based immutable auditability. The framework is organised around four pillars — Identity Assurance, Adaptive Trust Decisioning, Consent Governance and Immutable Accountability — operationalised through an eight-phase lifecycle. Relative to prior work, this version adds a multi-dimensional comparison with state-of-the-art healthcare IAM approaches, a reproducible experimental protocol, an explicit threat model with a systematic security analysis, and an AI-trustworthiness analysis covering interpretability, calibration, robustness and fail-safe behaviour aligned with the NIST AI Risk Management Framework (AI RMF) and the EU AI Act. A proof-of-concept (PoC) evaluation in a simulated secondary-care environment shows a 42% reduction in authorisation latency, a 62% reduction in false-positive denials and a 77% reduction in standing-privilege exposure versus a conventional IAM baseline. ZTPIF-H aligns with HL7 FHIR R4, OpenID Connect (OIDC), W3C Verifiable Credentials (VC) and NIST Zero Trust Architecture (ZTA) principles, offering a vendor-neutral, incrementally adoptable pathway to modern healthcare identity engineering
: Primary care prescribing in the UK is conducted largely without access to a multidisciplinary team (MDT), exposing patients to unreviewed polypharmacy risk at scale. This paper presents a multiagent AI framework designed to simulate the parallel specialist review that an MDT provides but for every prescription request. The proposed architecture deploys five concurrent specialist agents (Pharmacist, Physiotherapist/ Occupational Therapist, Allergy Safety, Guidance, and Literature), each operating in a task-scoped context window to mitigate the accuracy degradation known to affect single-model approaches under high task volume. A compliance and timing optimisation layer identifies adherence-enhancing simplifications, and a structured safety double-check precedes GP sign-off. Agent slots expose standardised input/output contracts, allowing individual models to be retrained or upgraded independently. Evidence from multiagent systems research indicates that orchestrated multi-agent architectures sustain higher accuracy under clinical-scale workloads (90.6% versus 73.1% at low task volumes; 65.3% versus 16.6% at high volumes) than single-agent equivalents, which collapse under load. The paper argues that doubly parallelised multiagent AI — across agents within a case and across concurrent patient requests — is not merely an efficiency gain but a patient safety imperative given current UK primary care workforce constraint. Beyond drug-interaction checking, the agents apply STOPP/START criteria in older adults, screen for cross-reactivity and delayed hypersensitivity risk, and surface live drug-shortage and formulary information at the point of prescribing. The framework is designed for deployment within existing NHS infrastructure, interfacing directly with GP clinical systems, the Electronic Prescription Service, and NICE formulary data, minimising implementation and data-governance risk. We position the architecture as a credible and tractable candidate intervention whose value must be confirmed through prospective evaluation against clinician decisions
Naveen Rajagopal, Abhijay Jagini, Muthu Ramachandran· Proceedings of the Internati...· 0 citations
It is argued that AI should be framed as an augmentation of - not a replacement for - the clinical relationship, with equity, consent and explainability treated as first-order design constraints.
