01 | Medical Informatics

First floor | Winter Garden 1

Examples of work from across the Usher Building that either highlight our leadership roles in supporting and developing the health and care research data landscape across the UK and beyond, or of outputs from projects that have utilised routinely-collected health and care data for patient benefit.

This poster shares the story of how we used data science and modelling to help understand and address NHS waiting lists — a critical challenge affecting millions of people across the UK.

Visitors will see how we worked with large NHS datasets from England and Scotland to uncover patterns in patient demand, treatment capacity, and system-wide delays. We'll explain how we applied data science methods — including statistical models and machine learning — to translate raw data into meaningful insights.The display takes you through the full journey: from recognising the problem, gathering and analysing the data, to sharing the findings with policymakers and the public. This work has reached beyond academic circles — featuring in media interviews and even being cited in the House of Commons.

You’ll see how data-driven research can inform real-world decisions in healthcare. The poster is designed to be accessible to all backgrounds and will be of interest to anyone curious about how data and technology can improve public services.

Exhibitor: Ahmar Shah


People with severe mental illness (SMI), including conditions such as schizophrenia, bipolar disorder and depression, live 10-20 years less than the general population. This excess risk of death is largely due to poorer physical health, and in particular a greater burden of cardiovascular disease and diabetes. These mental health disparities in physical health are a major health inequality issue. 

People with SMI have a higher risk of, and poor outcomes from, these physical health conditions. Reasons for this are multifactorial and complex, but include disparities in receipt of clinical care for physical health.

Over the past 10 years, this inter-disciplinary research programme has focused on using large-scale linked electronic health data from across the UK to investigate the links between SMI and:

  1. the occurrence of physical diseases;
  2. the outcomes from these physical diseases; and
  3. the receipt of clinical care for physical disease.

This poster will present a high-level summary of headline findings from projects, which stem from research using national data resources, including the Scottish Diabetes Research Network-National Diabetes Dataset and national Public Health Scotland datasets, and the CVD-COVID-UK/COVID-Impact resource. These will include:

  1. SMI and life expectancy compared to the general population of Scotland over the past 20 years;
  2. SMI and diabetes, including receipt of diabetes care;
  3. SMI and cardiovascular disease, including care following heart attack.

It will signpost areas of expansion, including a workstream within the UK Mental Health Platform’s Hub for Metabolic Psychiatry, and investigation of SMI and cancer care and survival.

Exhibitor: Caroline Jackson


People with severe mental illness (SMI) have a higher risk of premature mortality than the general population. Our aim was to investigate whether the mortality gap for people with SMI is widening, by determining time-trends in excess life-years lost.

We performed a population-based study, including people with SMI (schizophrenia, bipolar disorder and major depression) alive on 1 January 2000. We ascertained SMI from psychiatric hospital admission records (1981-2019), and deaths via linkage to the national death register (2000-2019). We used the life-years lost (LYL) method to estimate LYL by SMI and sex, compared LYL to the Scottish population and assessed trends over 18 3-year rolling periods.

We included 28,797 people with schizophrenia, 16,657 with bipolar disorder and 72,504 with major depression.  Between 2000 and 2019, life expectancy increased in the Scottish population but the gap widened for people with schizophrenia. For 2000-2002, men and women with schizophrenia lost an excess 9.4 (95% CI 8.5-10.3) and 8.2 (7.4-9.0) life years, respectively, compared to the general population. In 2017-2019, this increased to 11.8 (10.9-12.7) and 11.1 (10.0-12.1). The life expectancy gap was lower for bipolar disorder and depression and unchanged over time.

The life expectancy gap in people with SMI persisted or widened from 2000 to 2019. Addressing this entrenched disparity requires equitable social, economic and health policies; healthcare re-structure and improved resourcing, and investment in interventions for primary and secondary prevention of SMI and associated comorbidities.

Exhibitor: Kelly Fleetwood


The retina is a uniquely accessible portal permitting direct observation of the neurovascular tissue bed in-vivo through non-invasive, high resolution, multimodal digital imaging. Harnessing this retinal readout as a platform for disease prediction is coming of age through the rapid evolution of Artificial Intelligence (AI)-enabled computational tools and technologies. However, realising the potential of this novel pathway to disease stratification and prediction modelling relies on robust validation through longitudinal, representative, curated, linked datasets which are currently lacking.

Many diseases - ocular, systemic and neurodegenerative – are associated with changes in the retina. As retinal signs may manifest years before the emergence of symptoms, retinal imaging provides a prospective means for earlier disease detection. Retinal changes may also be an important prognostic sign which could improve risk stratification and disease trajectory prediction.

The Scottish Collaborative Optometry-Ophthalmology Network e-research (SCONe) retinal image repository is a big data resource comprising more than 15 years of community-acquired retinal images securely held within the Scottish National Safe Haven, a Trusted Research Environment owned by Public Health Scotland (PHS).

These digital, colour fundus photographs, captured during regular eye examinations in optometry practices represent an unprecedented resource containing large numbers of images from healthy and diseased individuals. Images are linked (via the Community Health Index (CHI), the unique patient identifier used in Scotland) to longitudinal, coded, national health data facilitating the assignment of diagnostic labels at specific time points.

Exhibitors: Claire Tochel, Ana Paula Rubio & Samuel Gibbon


We will be on hand to discuss the HDR UK Scotland Programme, including:

  • Active research within HDR UK Scotland
  • Our role in the Safe Data Haven Network with RDS
  • Work improving data linkage across scotland
  • Previous training (associated with HDR UK Futures and Applied Analytics Programmes)
  • Our programme of events
  • Information about the Black internship Programme
  • Further information about HDR in general

Exhibitors: Serena Tricarico, Katie Wilde, Honghan Wu & Cata Vallejos


The display will present the findings of the first ever study using routinely collected electronic health care records for the entire UK population. The study reports on COVID-19 vaccination coverage and the risks associated with being undervaccinated—defined as receiving fewer than the recommended number of vaccine doses. Drawing on anonymised, harmonised health data from across England, Northern Ireland, Scotland, and Wales, the study analysed the records of nearly 65 million people.

Visitors will see an accessible visual summary of key findings: who was most likely to be undervaccinated (including patterns by age, ethnicity, deprivation, and health status), and how this affected the risk of hospitalisation or death from COVID-19 during the summer of 2022. The display will highlight that undervaccination was common—affecting up to half the population in some nations—and that even missing one or two doses significantly increased the risk of severe outcomes, especially in people aged 75 and older.

The display will also present estimates from a counterfactual scenario, showing how many severe outcomes might have been avoided across the UK if everyone had been fully vaccinated by June 2022. This includes nearly 5,500 severe cases prevented in the 75+ age group alone.


DataLoch is a data service developed in partnership by the University of Edinburgh and NHS Lothian. Our aim is to put data at the centre of responses to health and care system challenges and support developments to front-line services through research, innovation, and planning.

Our service brings together health and social care data from across South-East Scotland (900,000 current population and 1.6 million people over time), including data from 90% of GP practices, which is currently unique in the nation. Through comprehensive linkage of these data, an holistic overview of health and wellbeing is possible.

Within our poster series, discover:

  • how we have implemented HDR UK CALIBER phenotypes to harmonise codes across primary and secondary care;
  • the new DataLoch Heart Disease Registry developed in collaboration with clinicians and researchers that accelerates cardiovascular research while preserving the security of the data;
  • how we are integrating non-medical data to support exploration of social and other factors on health and wellbeing; the latest on our Natural Language Processing developments which will enable access to unstructured (free-text) health data for research without compromising confidentiality; and
  • the collaborative steps taken with members of the public to develop and refine our Public Value Assessment which ensures that all applications we receive have public benefit in mind from the outset

Through a variety of activities, DataLoch plays a crucial role in enabling access to data to the right people for the right reasons, and actively develops new opportunities to support research and innovation that will improve population-wide health and wellbeing.

Exhibitors: Stuart Dunbar, Franz Gruber and Zengyi Huang


ICU-HEART is an exciting clinician led inter-disciplinary project with ambitious aims to directly improve patient care using routinely collected data. This poster provides an overview of the ICU-HEART programme with an emphasis on its multi-disciplinary and collaborative nature.

We outline the main goals and visions of our research; to use routine data to transform research in intensive care units (ICU) and to improve care for critically ill patients with cardiovascular disease. This poster highlights how each component of the research programme is working towards achieving these aims, and how we are working together to do so.  

By integrating real-time healthcare data science into healthcare systems, we aim to improve the survival and quality of life of critically unwell patients. Specifically, we aim to develop and deliver the systematic diagnosis, prediction and prevention of heart attacks for the first time in critically ill patients, not only saving lives, but also improving their quality of life. To do this we have built a multi-disciplinary team, incorporating clinicians, data scientists, sociologists, philosophers, and patient representatives. This broad mix of skills will allow us to view the challenges we face from all angles, helping to improve the efficiency and inclusivity of our research.

Throughout this ambitious initial project our programme will develop the infrastructure and analytical approaches for subsequent research using patient-centred real-time data science in ICU environments. We believe this area of research has the potential transform the provision of critical care, ultimately allowing us to save critically ill patients’ lives. 

Exhibitor: Steph Burns, Annemarie Docherty and Craig Nicolson


This article was published on