Modelling clinical processes using clinical data

PROJECT REF: CCTS3380217

The Centre for Healthcare Modelling and Informatics (CHMI) is a long-established health informatics research and innovation group. Work in the clinical outcome modelling 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, among other things, that:

• biochemistry and haematology outcome models (BHOM) can be used to identify patients at risk of mortality (Prytherch, Sirl, Schmidt, Featherstone, et al, 2005; Prytherch, Briggs, Weaver, Schmidt and Smith, 2005)
• 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 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)
• introducing an electronic physiological surveillance system (employing NEWS) reduces hospital mortality (Schmidt, Meredith, Prytherch, Watson et al, 2015)
• NEWS performs better than the other EWSs in discriminating risk of death within 24 h of an observation set, irrespective of the method used to select the observations (Jarvis, Kovacs, Briggs, Meredith et al, 2015a)
• a simplified version of NEWS (binary NEWS) works almost as effectively at discriminating patients at risk of adverse outcome, but may result in a higher workload for clinical staff (Jarvis, Kovacs, Briggs, Meredith et al, 2015b)

Our EWS models 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.