Digitally Enabled PrevenTative Health (DEPTH) Research Group
The Digitally Enabled PrevenTative Health (DEPTH) Research Group is focused on high-impact research in digital health. Our team is centred on issues affecting the engineering and evaluation of mobile digital solutions and integrated health data ecosystems. This work is complemented by research into critical enabling infrastructure including novel regulatory pathways, regulation, standards and intellectual property.
Advanced therapeutics clinical translation and cell therapy commercialisation
Advanced therapies, such as cell and gene therapies, are a new therapeutic modality which have potential to offer treatment or cures for currently untreatable diseases. This therapeutic strategy is very young in comparison to standard chemical-based drugs, and therefore few are currently established on the market.
(Pre-) adoption and abandonment of health innovations in different settings
Evaluating facilitators and barriers influencing the uptake of digitally-delivered interventions from the perspective of different stakeholders (patients, carers, health workers, clinics, hospitals, policy makers, regulators, insurers, pharma, start-ups, medical device, biotech and technology companies, etc.).
As increasing population data is accessed by automated systems and agents, the risk exposure of assets increases. The benefits of population data insights are tempered with these risks of unauthorised access, which can have direct implications to the quality and management of health.
Intellectual property landscaping
Intellectual property (IP) is of fundamental importance to medicinal product development. IP landscaping is a method by which information about IP can be gathered, and can provide valuable insight into the activity of other research groups or the progression of research areas as a whole.
The safety and quality of digitally-delivered health interventions
Regulation, standards, validation, and assessment of the efficacy, effectiveness, and costs of digital health.
Requirements and product engineering using machine learning and artificial intelligence (AI) agents, where there are potential for behaviour change for positive health outcomes, workforce efficiency opportunities via robotic process automation and capabilities for improved clinical and population health insight from predictive data analytics.
Sources of funding
SENS Research Foundation, Oxford Academic Health Sciences Centre, European Institute for Innovation and Technology (EIT), Higher Education Funding Council for England (HEFCE), NHS England, Health Education England (HEE).
Potential lifetime quality of life benefits of choroideremia gene therapy: projections from a clinically informed decision model
Halioua-Haubold C-L. et al, (2019), Eye
Biotechnology Governance 2.0: A Proposal for Minimum Standards in Biotechnology Corporate Governance.
Carter AR. et al, (2019), Rejuvenation Res
Tools for the Diagnosis of Herpes Simplex Virus 1/2: Systematic Review of Studies Published Between 2012 and 2018 (Preprint)
Arshad Z. et al, (2019)
MOOC Evaluation Methods: A Systematic Review (Preprint)
Alturkistani A. et al, (2019)