About the Project
School: Bristol Medical School, University of Bristol
Supervisors: Prof GD Angelini, Dr Tim Dong, Prof Melanie Hezzell, Dr Angus Nightingale
Deadline: Sunday, 13 December 2026
Funding: Competition Funded PhD Project (Students Worldwide)
About the project
While artificial intelligence (AI) and Large Language Model (LLM) decision-support tools offer immense potential for tailoring cardiovascular care, their translation into routine NHS practice is severely bottlenecked by implementation barriers, specifically, fragmented multi-specialty pathways, clinical diagnostic delays, and a lack of standardised risk-stratification tools. These barriers are most acute in complex, multi-system conditions where the sheer volume of cross-specialty data exceeds the cognitive processing capacity of individual clinicians during routine care.
Postural Orthostatic Tachycardia Syndrome (POTS) is an extremely complex cardiovascular autonomic disorder that frequently overlaps with severe comorbidities (e.g., Ehlers-Danlos Syndrome, Mast Cell Activation Syndrome, Long-COVID, and venous compressions).[1] This overlap of co-morbidities and contraindications presents an overwhelming challenge in terms of the complexity of clinical diagnostic and care management needs. In addition, POTS is frequently misdiagnosed,[2] with currently no approved medications licensed by the MHRA, EMA, or FDA.[1] Unmanaged patients suffer continuously worsening symptoms of deconditioning-affiliated muscle loss, cerebrovascular dysfunction, injury from fainting and overall deterioration in quality of life.[3] Patients living with these conditions want new therapeutic solutions and tailored advice that is focused on personalised medicine. Additionally, it is important to understand the pathway and prescription optimisation required to bridge the complex contraindications posed by the overlapping comorbidities.
Addressing these implementation barriers is a critical health systems priority. To meet this challenge, this project aims to design an Implementation Science Framework that successfully embeds trustworthy, explainable, and regulatorily compliant AI into clinical workflows, and patient care for these complex, multi-system conditions.
Developing a robust implementation framework requires bridging health psychology, organisational methodology, regulatory governance, and computational data science. This project brings together an international, interdisciplinary network including collaboration with Prof Margaret Watson (University of Strathclyde) for implementation science and PPIE, Dr Kathya Cordova-Pozo (Radboud University) for improving health technology assessment methodologies (e.g. interpretative phenomenological approaches and ethnography) and international research partners specialising in patient-clinician interaction optimisation and data partners from autonomic neurology and cardiology fields across Europe and the South West Genomic Hub on genomic risk profiling.
This position is ideal for a candidate with a background in Psychology, Health Sciences, Social Sciences, or Implementation Science, with a strong interest in PPIE. No prior programming, medical coding, or laboratory genetic expertise is required. Training in Computational Data Science/AI, process-relational mapping, and Ontological methods will be provided.
Aims and objectives
The overarching aim is to develop an Implementation Science decision-support toolkit for healthcare practitioners that combines process-relational mapping with structured medical ontologies (formal semantic knowledge frameworks connecting clinical factors, patient-reported outcomes, genomic risk profiles, and European clinical management and AI regulatory guidelines) to optimise the adoption, explainability, safety, and acceptability of AI/LLM for POTS management in the NHS. Key Objectives include:
- Process & Relational Mapping: Map clinical and genetic consultation workflows across post-COVID, autonomic, and genomic/vascular compression pathways to identify human, relational, and structural barriers to adopting AI recommendations.
- Co-Design Explainable AI Workflows: Work alongside a Patient Advisory Group, genetic counsellors, and NHS clinicians to co-design explainable visual/conversational AI toolkits tailored to NHS workflow constraints, multi-morbidity contraindications and genetic risk communication, and patient needs.
- Genomic, Clinical & Regulatory Ontology Mapping: Translate qualitative findings, genetic counselling insights, patient-reported side effects, NHS care pathways, and regulatory governance standards into a structured AI Implementation, Contraindications and Medication Decision Support Ontology.
Methods
- Utilise established implementation science frameworks and process-relational mapping to analyse clinical/genomic workflows, pharmacy practice integration, regulatory compliance, and decision-making dynamics in primary and secondary care.
- Map ICD-10 codes using BHF Data Science Centre datasets and apply established dashboards for microsimulation to analyse changes to interventions and pathways for POTS incorporating data from published clinical trials.
- Apply established health psychology and psychometric frameworks to evaluate simulated and ethically approved patient-clinician-counsellor-toolkit communication dynamics, cognitive load, trust indicators, and behavioural responses during AI-assisted prescription consultations. Undertake surveys (utilising COMPASS-31 and Malmö scores) to categorise barriers to effective care and align clinical and patient-reported outcomes.
- Structure qualitative implementation, contraindication, genomic risk relating to POTS and comorbidities, and regulatory insights into semantic knowledge networks to ground LLM outputs in real-world clinical contexts and prevent AI hallucinations.
- Integrate co-design principles with local digital partners to optimise CDSS design for future NHS translation.
How to apply
Before applying, please check the entry requirements.
Submit your application via the University of Bristol portal. Search for and then select ‘Clinical Sciences (PhD)’. Click ‘Apply’ for September 2027 start.
You must provide the following in your application:
- A CV
- Two referees (add to the Reference section of your application form)
- A personal statement highlighting your motivation for applying for this PhD, and any relevant experience and/or skills (maximum of one side of A4, font size 11). We are keen to support applicants from minority and under-represented backgrounds (based on protected characteristics) and those who have experienced other challenges or disadvantages. Please use your personal statement to ensure we can take these factors into account.
- Certificates and transcripts
- Enter the project title and the supervisor names as they appear on this advert in the Research section.
- A research statement/proposal is not required. You should upload a blank document in this section.
- In the funding section, choose ‘University of Bristol Scholarship’.
Incomplete applications will not be considered. Read the list above carefully and ensure you have uploaded all required documents by the deadline. If you have yet to complete your degree, please supply an interim transcript where possible.
You can find more information on how to prepare your application and what we are looking for at the “PhD Application Workshop” which will be held on Teams on Monday 23rd November at 2pm (GMT): PhD Application Workshop.
Applications will be shortlisted in January 2027 with interviews in February 2027. The anticipated start date for this scholarship is September 2027.
Please visit our website for more information on PhD opportunities in the Faculty of Health and Life Sciences.
Further enquiries
Project-specific queries: G.D.Angelini@bristol.ac.uk
Application queries: uob-pgradmissions@bristol.ac.uk
Funding Notes
The University of Bristol is one of the most popular and successful universities in the UK, ranked 51st globally (QS 2026), top five in the UK for research (THE analysis of REF 2021), and 6th most targeted by UK employers (High Fliers 2026).
This is a four-year scholarship covering tuition fees, a UKRI-rate stipend (£21,805 in 2026/27, updated annually), and £2,100 annual research costs.
The scholarship is available to UK and International applicants. International candidates (including EU) will be required to cover the cost of their student visa, healthcare surcharge and other costs of moving to the UK.