A Multiagent AI Framework for Parallelised Prescription Decision Support in Primary Care
TL;DR
It is argued 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.
Abstract
: 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