Digital Health Expert Group

My research aims to explore approaches used by family members to encourage and support older people to engage with digital technology during the pandemic. The project will use mixed methodology and findings will help to inform and develop ‘lessons learned’ resources that can be shared with primary care services to reduce digital disadvantage in older people.

Prostate cancer, identified as the most common type of cancer among the males in the UK, is strongly affected by inequalities related to age, ethnicity, and deprivation. This study investigates disparities in adherence to clinical guidelines to identify which groups of men do not receive the recommended, high standard of healthcare, and to understand the underlying factors affecting these disparities in diagnosis and treatment.

Currently many people with pancreatic cancer are diagnosed too late for curative treatment and therefore we need data driven approaches to support early diagnosis. In the DEFEND PRIME study, we will investigate whether it is possible to calculate a risk score for pancreatic cancer using anonymised health data from GP practices.

My PhD project focuses on combining psychometric methods with data science techniques to facilitate early identification of cognitive impairment (CI) in non-clinical settings, such as communities. The project aims to develop behavioural markers of early cognitive deficits by identifying latent inconsistent responses in large-scale surveys, and employing them to establish a deep learning-based prediction tool.

Arsene's project examines remote consultations for Type 2 Diabetes care in rural Rwanda. Using a mixed-methods approach, including a systematic review and a case study of a former telemedicine platform, the research explores facilitators and barriers to remote care from multiple perspectives. As Rwanda considers integrating remote consultations into its health system, this project will develop evidence-based guidelines to support this initiative, aiming to improve access, efficiency, and quality of non-communicable diseases care for rural populations.

In this project, routinely collected NHS data from one if the UK’s largest primary care databases Orchid will be used to develop and validate predictive models for more accurate diagnoses of cancers. This will improve the understanding of the predictive value of tumour markers in relation to clinically relevant outcomes – e.g. the need for treatment. In the future, this could improve the usage of NHS resources and patient care.