People with serious mental illness (SMI) such as schizophrenia, schizoaffective disorder and
bipolar affective disorder have a significantly reduced life expectancy in comparison to the
general population. Improvements to the primary prevention of physical health illnesses like
diabetes in the general population have not been mirrored to the same extent in people with
SMI.
Diabetes is a group of metabolic disorders characterized by a high blood sugar level over a
prolonged period of time. If left untreated or poorly managed, diabetes can lead to various
long term health complications including cardiovascular disease, stroke, chronic kidney
disease, foot ulcers, damage to the nerves and damage to the eyes. Diabetes accounts for
approximately 10% of healthcare resources in the UK, and this is set to rise to 17% with an
estimated cost of £39.8billion by 2035 when direct healthcare costs and indirect costs on
productivity are taken into account.
People with SMI have higher rates of cardiovascular disease (CVD) risk factors such as
central obesity, high blood pressure, raised cholesterol levels, and raised blood sugar
levels compared to the general population. Local rates of diabetes in people with a diagnosis
of established psychosis are 20% with a further 30% evidencing dysglycaemia (raised blood
sugar levels). Again locally, rates of glucose dysregulation (indicator for high risk of
developing diabetes) doubles in the first year after a first psychotic episode, creating a
unique window for prevention strategies to address these risks as early as possible.
A key inequality in healthcare provision in people with SMI is the less than adequate
assessment and treatment of physical health conditions such as diabetes in secondary mental
healthcare settings. There is therefore a need for more targeted and clinically informed
interventions, that improve the standard of physical healthcare screening and interventions
offered to people with SMI across both primary and secondary care settings.
Globally, studies evaluating the provision of care by clinicians reveal that there is a
sub-optimal uptake of guidelines into actual practice. The underlying factors for this are
complex and occur at a combination of patient, clinician and system levels.
Adoption of digital technology to improve physical health in people with a diagnosis of SMI
presents a unique opportunity, but requires evidence of acceptability, feasibility and
effectiveness. Given the rising disease burden from diabetes in SMI, and deficits in
providing evidence-based care for diabetes prevention and treatment, there is a pressing need
to identify more systems-focused solutions.
Electronic clinical decision support systems (eCDSS) are well established as a strategic
method of improving care for prevention and management of chronic conditions. eCDSS is
defined as "any electronic information system based on a software algorithm designed to aid
directly in clinical decision making, in which characteristics of individual patients are
used to generate patient-specific assessments or recommendations that are then presented to
clinicians for consideration".
Clinical guidelines remain under-utilized in clinical practice, thus eCDSS has the potential
to overcome problems associated with the use of traditional paper-based guidelines. However,
the existing evidence base for eCDSSs improving clinical performance and patient outcomes in
mental healthcare settings remains sparse. In addition, electronic systems that are not
accepted by their users cannot be expected to contribute to improving quality of care, hence
facilitators, barriers and other consequences need to be understood for successful
implementation of novel digital tools and could also serve as a basis for future system
re-engineering. Hence there is call for research to include evaluating its implementation for
successful future scalability.
The key digital tool to be used for eCDSS in this study is CogStack, a software platform
developed by the National Institute for Health Research Maudsley Biomedical Research Centre
(NIHR Maudsley BRC) and PhiDataLab. CogStack is an open source information retrieval and
extraction system with the capability to offer near real-time natural language processing
(NLP) of electronic health records. CogStack implements new data mining techniques,
specifically the ability to search any clinical data source (unstructured and structured),
and NLP applications developed to automate information extraction of medical concepts. The
platform has shown early potential to be of value to clinicians in monitoring, intervention
and follow up for their patients.
The primary objective of this study is to establish the feasibility and acceptability of an
eCDSS (Cogstack@Maudsley) compromising a real-time computerised alerting and clinical
decision support system for dysglycaemia management in secondary mental healthcare.
Our secondary objectives are to assess whether the system leads to changes in screening and
follow-up testing rates for dysglycaemia, and subsequent clinician-led evidence-based
interventions for dysglycaemia and diabetes (this will be measured using pseudonymised group
observational data gathered from the South London and Maudsley NHS Foundation Trust (SLaM)
Biomedical Research Centre (BRC) Clinical Records Interactive Search (CRIS) system once ward
access to the eCDSS has ended). Since 2006, South London and Maudsley NHS Trust has operated
fully electronic health records. The Clinical Record Interactive Search (CRIS) system,
established in 2008, is an ethically approved electronic health records interface system that
allows researchers to access deidentified electronic health records from this Trust for
research purposes.
We will conduct a process evaluation to assess the barriers, facilitators, unintended
consequences, and indicative costs of implementing the system onto inpatient general adult
psychiatry wards.
Data gathered from this study will allow the research team to refine the system, address
potential problems with future successful implementation, and inform a larger and more
definitive effectiveness trial which will examine for hypothesised improvements in;
Rates of clinician-delivered evidence-based interventions for patients with dysglycaemia
Clinical outcomes relating to diabetes care
