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.
Please use our online application form and state the project code (CCTS3380217) and title in the personal statement section.
References to recent published articles:
• Prytherch, David, Sirl, J. S., Schmidt, P, Featherstone, Peter I., Weaver, P. C. and Smith, Gary B. (2005) The use of routine laboratory data to predict in-hospital death in medical admissions. Resuscitation, 66 (2). pp. 203-207. ISSN 0300-9572
• Jarvis, Stuart, Kovacs, Caroline, Badriyah, Tessy, Briggs, Jim, Mohammed, Mohammed A., Meredith, Paul, Schmidt, Paul E., Featherstone, Peter I., Prytherch, David and Smith, Gary B. (2013) Development and validation of a decision tree early warning score based on routine laboratory test results for the discrimination of hospital mortality in emergency medical admissions. Resuscitation, 84 (11). pp. 1494-1499. ISSN 0300-9572 10.1016/j.resuscitation.2013.05.018
• Badriyah, Tessy, Briggs, Jim, Meredith, Paul, Jarvis, Stuart, Schmidt, Paul E., Featherstone, Peter I., Prytherch, David and Smith, Gary B. (2014) Decision-tree early warning score (DTEWS) validates the design of the National Early Warning Score (NEWS). Resuscitation, 85 (3). pp. 418-423. ISSN 0300-9572 10.1016/j.resuscitation.2013.12.011
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