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Using statistical and data mining models to support clinical processes

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  • Full or part time
    Dr J Briggs
    Dr P Scott
  • Application Deadline
    No more applications being accepted
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Start date: October 2015

The Centre for Healthcare Modelling and Informatics (CHMI) is a long-established health informatics research and innovation group and has acknowledged expertise in telemedicine/telecare and in clinical outcome modelling. Work in the latter area has supported the development of the VitalPAC vital signs collection system and also the National Early Warning Score (NEWS) recommended by the Royal College of Physicians, among many other projects.
The models we have developed so far enable clinicians to predict which patients are at risk of deterioration, and medically intervene. Our published work has shown that:
• biochemistry and haematology outcome models (BHOM) can be used to identify patients at risk of mortality with very high discrimination (Prytherch, Sirl, Schmidt, Featherstone, et al, 2005; Prytherch, Briggs, Weaver, Schmidt and Smith, 2005).
• vital signs data can be used to devise an early warning score (EWS) system that can both identify patients whose condition is deteriorating and minimise unnecessary false alarms (Smith, Prytherch, Schmidt, Featherstone, et al, 2006)
• an EWS devised from vital signs data (ViEWS) performs better than any of the 33 other EWS systems in the literature (Prytherch, Smith, Schmidt, Featherstone, 2010)
• an EWS can be devised from blood test data (Jarvis, Kovacs, Badriyah, Briggs, et al, 2013)
• decision tree data mining techniques can be used to develop new early warning score systems (DT-EWS) quickly (Badriyah, Briggs, Meredith, Jarvis, et al,, 2013)
• aggregate National Early Warning Score (NEWS) values are more important than high scores for a single vital signs parameter for discriminating the risk of adverse outcomes (Jarvis et al, 2014)
Our EWS models can be applied to any patient under clinical care, but are increasingly used to allow nurses to determine which of their patients are deteriorating and when to summon assistance (e.g. a doctor), without causing too many false alarms (which would overburden hospital resources).
This project will build on our unique dataset of vital signs data, coupled with information drawn from other clinical information systems. The aim of the project is to develop new models to support clinical processes. The precise process(es) to be addressed will be decided in consultation with the successful applicant, but may relate to specific clinical conditions, specific operational issues, or specific aspects of our dataset. Only processes that are likely to have an impact on clinical functions or the safety, efficiency and efficacy with which healthcare organisations deliver care will be considered.

Special features
The successful candidate must be extremely numerate and possess good programming and (ideally) database skills. Training and support in these topics will be provided, but it is expected that the candidate will be starting from a reasonably high base. It is not necessary for the candidate to have a first degree in any particular subject – any subject (or equivalent work experience) that provides these skills will be considered. Good communication skills in English (both written and verbal) are essential.

This work will build on our long-term relationship with Portsmouth Hospitals NHS Trust and our collaborators at other major universities in the UK and overseas.

Arrangements
Full-time students preferred, though we will consider highly-qualified part-time students.

Further information:
http://www.chmi.port.ac.uk/
http://www.chmi.port.ac.uk/outcomes/index.htm

Funding Notes

Student must self-fund or arrange their own financial support.

Please include CCTS1031015 in the 'Project Code' section of the online application form.


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