MSc (Res) Opportunity – Understanding Symptom–Diagnosis Alignment in the Last Year of Life: A Digital Health Study Using National Emergency Care Data
Project Title:
Understanding Symptom–Diagnosis Alignment in the Last Year of Life: A Digital Health Study Using National Emergency Care Data
Supervisor(s):
Primary Supervisor: Professor Colin McCowan (University of St Andrews, School of Medicine)
Secondary Supervisor: Dr Benedict Leonard-Hawkhead (University of St Andrews, School of Medicine)
Deadline:
Tuesday 19 May 2026
Project Description:
Emergency Department (ED) visits are common in the last year of life, yet we know surprisingly little about for what reasons and how well patients’ own descriptions of their symptoms align with the diagnoses clinicians assign during these encounters. This MSc (Res) project offers an exciting opportunity to explore this question using one of the richest and most comprehensive health datasets available in the UK.
Working at the intersection of digital health, emergency medicine, and end‑of‑life research, the project investigates the relationship between “what the patient says is wrong” and “what the doctor thinks is wrong” for people attending the ED in the final 12 months of life. You will analyse large‑scale, routinely collected clinical data to quantify how closely patient‑reported presenting complaints match clinician‑assigned diagnoses, and explore how this alignment varies across demographic, clinical, and temporal factors.
You will work with a fully cleaned, analysis‑ready dataset, with all major governance approvals already secured (including HSC‑PBPP Tier 1 and 2 and Stats PBPP approvals).
The project is ideally suited to students interested in digital health, health data science, emergency care, palliative care, or health inequalities. You will gain hands‑on experience with natural language processing (NLP), statistical modelling, and the analysis of real‑world clinical data—skills that are highly sought after in academia, the NHS, and industry.
Supervision will be provided by a primary and secondary supervisors with complementary expertise in clinical care, digital health, and advanced analytics. You will also be embedded within a vibrant Health Data Science group, offering layers of support, methodological guidance, and access to a community of researchers working on cutting‑edge applied data science.
This is a rare opportunity to contribute to an innovative study, generating insights that could meaningfully improve the quality and equity of emergency care for people nearing the end of life.
School of Medicine Research Division:
Population and Behavioural Science
Funding Details:
The home fee for this opportunity is funded. Please see the university website for fee information.
How to Apply:
If you are interested in applying for this opportunity, please submit your application via the University’s online portal.
Please make sure your application is complete by Tuesday 19 May 2026.
Eligibility Criteria:
- Applicants should normally hold, or expect to obtain, a 2:1 Honours degree (or equivalent) in a relevant subject.
- Part time study for this project would be considered.
Contact:
Enquiries about the application process can be directed to Sandra Fleming at [email protected].