Heart failure (HF) is an irreversible clinical syndrome characterised by comorbidities, declining health status, episodic exacerbation of symptoms and frequent unplanned hospitalisations. With over 1 million people living with HF in the UK, there are an estimated 1 million hospitalisations per year, costing the NHS up to £2 billion. Early identification of people with HF who are at high risk of hospitalisation is a critical challenge. Prior evidence shows that, whilst patients will often experience deterioration in health or increasing symptoms prior to admission, clinical deterioration is poorly recognised by clinicians, patients and carers. Symptom monitoring by patients is ad hoc and poor symptom perception is compounded by comorbidities and low health literacy in up to half of HF patients, particularly those who are older, deprived or from ethnically diverse groups. Poor symptom monitoring and recognition is important because epidemiological studies show that (i) hospitalisations are associated with poor outcomes and (ii) up to half of these admissions may be avoidable.
This PhD project will combine (i) systematic review and (ii) epidemiological analysis with (iii) in-depth qualitative analysis and computer informatics, to investigate symptom trajectories leading to admission from multiple patient and healthcare dimensions. In particular, the post holder will work using the Clinical Practice Research Datalink (CPRD; one of the largest longitudinal databases of anonymised primary care records in the world) to identify key symptom trajectories for different patient groups (i.e., in relation to comorbidities, gender, ethnicity and socioeconomic status) and with patients, to gain valuable insights into patient home-based technologies to improve symptom monitoring. The role of electronic technology and user-centred system design in supporting connections between lived symptom experiences and self-care and self-management will be explored. Mobile diaries will be used to explore concepts such as ‘serendipity’ and connection-making in the context of recommender systems (e.g., timing, context, content and perception), to inform the design of future technological solutions and explore the use of modern technologies (e.g. mixed-reality) to reduce hospital admissions. Furthermore, iterative Participatory Design (PD) and Co-design sessions with relevant stakeholders will be conducted to elicit patient needs and design proposals for future innovative symptom-tracking technologies.