Funding

Self-funded

Project code

COMP6421025

Department

School of Computing

Start dates

October, February and April

Application deadline

Applications accepted all year round

Applications are invited for a self-funded, 3 year full-time or 6 year part-time PhD project.

The PhD will be based in the School of Computing and will be supervised by Dr Elisavet Andrikopoulou.

The work on this project could involve:

  • Develop and evaluate a new computable knowledge artifact
  • Work with NICE and MCBK - UK
  • Mixed methods study including qualitative and quantitative data

Context

The necessity for active, continuous knowledge management efforts in biomedicine to keep track of what the world knows about human biology and health has long been recognised; nonetheless, that requirement has recently increased and changed dramatically. One would expect that the healthcare sector is focusing on identifying and applying best practices in informatics, though this is not often the case due to socio-cultural factors, information barriers and overall complexity (Friedman & Rigby, 2013).

Computable knowledge unleashes the potential of information technology to generate and deliver relevant health advice to individuals and organizations with great speed on a worldwide scale. 

In this light, this project focuses on developing and evaluating the applicability of the World Health Organization (WHO) Digital Adaptation Kit (DAK) model in the development of computable knowledge artifacts in the UK  (Scott et al., 2023). 

The supervisory team has excellent connections with NICE and the ÍÃ×ÓÏÈÉú Hospital NHS Trust.

References: 

Friedman, C.,& Rigby, M. (2013). Conceptualising and creating a global learning health system. International Journal of Medical Informatics, 82(4), e63–e71.

Scott, P.,Heigl, M.,McCay, C.,Shepperdson, P.,Lima-Walton, E.,Andrikopoulou, E.,Brunnhuber, K.,Cornelius, G.,Faulding, S.,McAlister, B.,Rowark, S.,South, M.,Thomas, M. R.,Whatling, J.,Williams, J.,Wyatt, J. C.,& Greaves, F. (2023). Modelling clinical narrative as computable knowledge: The NICE computable implementation guidance project. Learning Health Systems, 7(4), e10394.

Funding

Visit the research subject area page for fees and funding information for this project.

Funding availability: Self-funded PhD students only. 

PhD full-time and part-time courses are eligible for the (conditions apply).

Bench fees

Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.

Entry requirements

The entry requirements for a PhD or MPhil include an upper second class honours degree or equivalent in a relevant subject or a master's degree in an appropriate subject. Exceptionally, equivalent professional experience and/or qualifications will be considered. All applicants are subject to interview.

If English is not your first language, you'll need English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

If you don't meet the English language requirements yet, you can achieve the level you need by successfully completing a pre-sessional English programme before you start your course.

 

The candidate needs a Computer science degree or similar qualification and would benefit from experience working with the NHS.

 

How to apply

We’d encourage you to contact Dr Elisavet Andrikopoulou (elisavet.andrikopoulou@port.ac.uk) to discuss your interest before you apply, quoting the project code.

When you are ready to apply, please follow the 'Apply now' link on the Health informatics PhD subject area page and select the link for the relevant intake. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV.  Our ‘How to Apply’ page offers further guidance on the PhD application process. 

When applying please quote project code: COMP6421025