Abstract: Diabetes and hypertension represent the two most prevalent and co-occurring chronic conditions managed by the National Health Service (NHS), collectively generating millions of annual outpatient encounters and prescriptions, with substantial associated medication error rates. Existing prescribing systems are constrained by physician availability, cognitive workload, and an inability to adapt medication regimens dynamically to real-time patient data. This paper proposes an AI-powered prescription optimisation system (AI-POS) for NHS patients with diabetes and hypertension, presenting a comprehensive research framework, system architecture, and ethical analysis. The AI-POS integrates supervised machine learning with NICE clinical guideline rule-based logic to generate patient-specific, evidence-aligned medication recommendations from electronic health record (EHR) data. A mixed-methods research design combines quantitative machine learning model development and evaluation (targeting >90% sensitivity) with qualitative semi structured interviews with NHS clinicians to assess usability, trust, and ethical acceptability. The proposed system employs a human-in-the-loop design philosophy, ensuring clinician override capability and algorithmic transparency at every decision point. Ethical considerations—encompassing algorithmic bias, patient data privacy, clinician trust calibration, and regulatory compliance with MHRA and NHS AI Lab frameworks—are systematically addressed. Early simulation results indicate guideline adherence exceeding 90% and clinician feedback identifies transparency and override mechanisms as critical acceptance factors. The paper contributes a replicable research framework and system design for AI integration into NHS chronic disease prescribing workflows.
Keywords: artificial intelligence, prescription optimisation, NHS, diabetes, hypertension, machine learning, NICE guidelines, electronic health records, human-in-the-loop, clinical decision support.
Title: AI-Driven Prescription Optimisation for Diabetic and Hypertensive Patients in the NHS: A Mixed-Methods Research Framework, System Design, and Ethical Analysis
Author: Chinonso Job, Ifesinachi Ignatius Nwankwo, Onwe Festus Chijioke
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
Vol. 14, Issue 2, April 2026 - June 2026
Page No: 108-113
Research Publish Journals
Website: www.researchpublish.com
Published Date: 25-May-2026
