|Responsible KEG Investigator:
Dr Christopher Buckingham
- Birmingham & Solihull Mental Health Trust
Type of Project: Case Award
Date Commissioned: 10/2006
EPSRC CASE PhD Studentship
Background: The CASE PhD studentship will be linked with and use data from an
ongoing research project funded by the Department of Health for 275,000 pounds.
The goal is to produce a decision support system (DSS) that assesses risks
associated with mental-health problems using a psychological model of
clinicians' risk-assessment expertise.
Aims of proposed project: The main aim is to enhance the DSS by providing
mathematical tools for analysing risk data that complement the risk assessments
based on human expertise. A secondary aim is to improve the quality of risk
assessments within the collaborating organisation, Birmingham and Solihull
Mental Health Trust (BSMHT). Specific objectives are to investigate: how
Bayesian belief networks might be both informed by and inform expert knowledge
structures; the creation of optimum belief-net structures that complement the
mental-health expertise; how changes in patient data over time affect event
probabilities; the relationship between the DSS judgements and those given
directly by mental-health practitioners; pattern recognition tools that do not
rely on domain structure inherent within the patient data set; how the DSS can
validate higher-level judgements given by clinicians, with the intention of
detecting inconsistent judgements for patients; matching of patient information
profiles, both for data validation purposes and to simulate case-based
reasoning; current methods of risk assessment within the Trust, including the
influence of workplace procedures and the influence of information technology
(IT); and how the analyses of risk data should best be presented to clinicians
to maximise the chances of the DSS being adopted.
Outcomes: The general outcome will be a web-based risk-assessment DSS that
includes both human expertise and sophisticated mathematical prediction tools.
This combination will greatly enhance confidence of clinicians in the outputs,
encouraging both mental-health Trusts and front-line services to adopt the DSS,
and increasing the public's access to mental-health advice and help. The
research will help develop innovative ways of processing and disseminating
mental-health risk information, making it readily available through IT and
easily accessible to both clinicians and those without a mental-health
background. The proposed work will inform best practice for IT tools, including
empirical evidence for the data that should be collected. It will provide
crucial input to the National Programme for Information Technology currently
being implemented within the NHS.