Severe mental illness is a significant cause of early death and disability. People with severe mental illness are at greater risk of a range of illnesses, including cardiovascular disease and smoking-related conditions. They often have poorer physical health outcomes than the general population. There is also significant variation in the treatment patients receive and the services available to them locally.
The ARC’s applied informatics researchers are carrying out research to improve treatment and health outcomes for patients with severe mental illness by visualising patient data from electronic health records for clinicians to help inform more targeted care.
The project, led by Professor Richard Dobson, professor of medical bioinformatics at King’s College London and Dr Rob Harland, consultant psychiatrist at South London and Maudsley NHS Foundation Trust, enables clinicians to review patients at risk of poor outcomes. It does this by presenting interactive data to the clinician to help them analyse the needs of their patients and provide more targeted care. It also allows clinicians to see where there is variation and inequality in care.
The project aims to support more patient-centred, efficient, targeted and proactive care that is focused on patients’ particular needs.
It will enable clinicians to be more proactive in addressing the determinants of poor health rather than simply treating illness.
The research team are using data from the Clinical Records Interactive Search system, analysing it with Cogstack clinical decision support tools, and then visualising insights for clinicians. This visualised data will ultimately be available for patients themselves. An initial study piloted the approach. Further research will expand piloting for different needs and evaluate implementation.
The project is a collaboration between South London and Maudsley NHS Foundation Trust and King’s College London. The project brings together clinicians, including doctors, nurses, psychologists, as well as informaticians and computer scientists.