Funding

Self-funded

Project code

CCTS4570219

Department

School of Computing

Start dates

February and October

Closing date

Applications accepted all year round

Applications are invited for a self-funded 3-year  full-time or 6-year part-time PhD project, to commence in October or February.

The project will be based in the School of Computing and will be supervised by Professor Jim Briggs and Professor David Prytherch.

The work on this project will aim to:

  • develop a new mortality prediction tool that can be used to identify changes in a hospital’s clinical outcomes and to compare the performance of hospitals
  • build on our unique dataset of vital signs data and laboratory results, coupled with information drawn from other clinical information systems

The Centre for Healthcare Modelling and Informatics (CHMI) is a long-established health informatics research and innovation group. In collaboration with Portsmouth Hospitals and others, our work in clinical outcome modelling has supported the development of the VitalPAC vital signs collection system and the National Early Warning Score (NEWS) recommended by the Royal College of Physicians, among many other projects.

The aim of the project is to develop a new mortality prediction tool that can be used to identify changes in a hospital’s clinical outcomes and to compare the performance of hospitals. The expected mortality is based on the number of patients and the seriousness of their condition (obviously, very sick people are more likely to die than those who are not). A hospital where significantly more patients die than expected could be underperforming.

Current tools used for this (e.g. HSMR and SHMI) are based on the administrative data that records the diagnoses associated with a patient's hospital stay. They adjust for various factors such as the patient's age, sex and their existing medical problems.

However, the data can be "gamed" and the quality of the data is variable – for example, it is often recorded long after the patient has left the hospital and only analysed much later.

This project would start from the position that clinical performance should be measured by clinical data. Actual data, recorded as part of the normal delivery of care (such as the results of laboratory tests on blood and vital signs taken at the start of a patient's stay) are less prone to error or gaming, and should provide an objective (physiological) ruler with which to measure a patient's degree of sickness.

More and more of this sort of data is now available in NHS hospitals. Several aspects of the problem are open to investigation including how the tool could be calibrated initially and adjusted according to changing practice.

This project will build on our unique dataset of vital signs data and laboratory results, coupled with information drawn from other clinical information systems. Data from a second hospital may be available for comparison purposes.

A student undertaking this work could expect to find employment in healthcare data analytics (either in the NHS or in industry) or health service management, as well as in academia.

Fees and 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 UK Government Doctoral Loan (UK and EU students only).

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

  • You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in an appropriate subject.
  • In exceptional cases, we may consider equivalent professional experience and/or Qualifications.
  • English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

How to apply

Please contact Professor Jim Briggs (jim.briggs@port.ac.uk) to discuss your interest before you apply, quoting the project code CCTS4570219. 

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.