Optimising Diabetes Management in Community Pharmacy: Developing an AI-Enabled Clinical Decision Support System for Pharmacist-Led Prescribing and Medication Review

Summary

Motivation:

Diabetes is a leading cause of morbidity and mortality in the UK, placing a significant burden on the health system. Community pharmacists are increasingly central to its management, especially with the expansion of independent prescribing roles.

However, optimising complex medication regimens for diabetes—managing polypharmacy, avoiding therapeutic inertia, and personalising treatment—remains a challenge in a busy community setting.

Artificial Intelligence (AI) and Clinical Decision Support Systems (CDSS) offer a transformative opportunity to augment pharmacists' clinical judgement, analyse patient data to identify risks, and suggest evidence-based treatment optimisations.

While AI is being explored in hospital diabetes care, its integration into routine community pharmacy practice for structured medication review and prescribing support is underdeveloped.

This project addresses this gap by co-designing and validating a context-specific AI-CDSS to empower pharmacist-led diabetes management.

Underlying aim:

To develop, and preliminarily validate, a user-centred AI-enabled Clinical Decision Support System (AI-CDSS) prototype to assist community pharmacist independent prescribers in conducting structured medication reviews and optimising treatment for patients with Type 2 Diabetes.

Specific Objectives:

1. To conduct a needs assessment to identify the specific requirements, workflow integration points, and perceived barriers/enablers for an AI-CDSS among community pharmacist independent prescribers managing diabetes.
2. To define the core functional specifications and clinical algorithms for the prototype, based on current clinical guidelines and a review of existing technologies.
3. To co-design a low-fidelity prototype in collaboration with community pharmacists, diabetes specialists, and patients.
4. To conduct usability testing and initial validation of the prototype in simulated clinical scenarios to assess its perceived utility, usability, and impact on clinical decision-making.

Methods:

Systematic Scoping Review & Guideline Synthesis: To map existing AI-CDSS in diabetes and community pharmacy, and synthesise UK clinical guidelines to inform the algorithm development (Objectives 1 & 2).

Qualitative Study: Semi-structured interviews and focus groups with community pharmacist prescribers, diabetes specialist nurses, and patients to explore needs, workflows, and ethical considerations (Objective 1).

Co-Design Workshops: Iterative workshops with the stakeholder groups to define specifications and create the low-fidelity wireframe prototype (Objectives 2 & 3).

Usability Testing & Think-Aloud Protocols: Pharmacists will interact with the prototype in simulated patient cases. Data on usability, perceived impact on confidence and decision-making, and qualitative feedback will be collected (Objective 4).

Impact statement:

This project will produce a foundational, user-validated blueprint for an AI-CDSS tailored to the UK community pharmacy setting.

The outputs will directly provide a clear, evidence-based pathway for creating such tools. For practice, it will empower pharmacist prescribers, potentially reducing therapeutic inertia and improving patient outcomes.

The research will contribute critical insights into the practical and ethical integration of AI into frontline, patient-facing clinical roles, a key priority for the future of clinical pharmacy.

AccessNI clearance required

Please note, the successful candidate will be required to obtain AccessNI clearance prior to registration due to the nature of the project.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

• Sound understanding of subject area as evidenced by a comprehensive research proposal
• A comprehensive and articulate personal statement

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• First Class Honours (1st) Degree
• Completion of Masters at a level equivalent to commendation or distinction at Ulster
• Practice-based research experience and/or dissemination
• Experience using research methods or other approaches relevant to the subject domain
• Work experience relevant to the proposed project
• Publications record appropriate to career stage
• Experience of presentation of research findings

Equal Opportunities

The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.

Appointment will be made on merit.

Funding and eligibility

NOTE - This is a self funded research project and applicants will be required to provide evidence of funds to support their tuition fees and living expenses.

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.