Eliciting the mechanisms of action of care navigators in the management of type 2 diabetes in people with severe mental illness: A qualitative study.

BACKGROUND: People with severe mental illness and type 2 diabetes have a reduced life expectancy compared to the general population. One factor that contributes to this is the inability to provide optimal management, as the two conditions are typically managed by separate physical and mental health systems. The role of care navigators in coordinating diabetes care in people with severe mental illness may provide a solution to better management. AIM: To explore the views of clinicians and people with severe mental illness and type 2 diabetes on an integrated health service model with a focus on the care navigator to identify potential mechanisms of action. DESIGN: Qualitative one-to-one semi-structured interviews and part of a wider pilot intervention study. SETTING: Community Mental Health Unit in South London. METHOD: Topic guides explored the perspectives and experiences of both clinicians and people with severe mental illness and diabetes. Data analysis was conducted using Thematic Analysis. RESULTS: From the analysis of 19 participants, five main themes emerged regarding the care navigator role: administrative service; signposting to local services; adhering to lifestyle changes and medication; engaging in social activities; further skills and training needed. The key findings from this study emphasise the benefits that the role of a care navigator has in helping people with severe mental illness to better manage their diabetes i.e. through diet, exercise medication and attending essential health check-ups. CONCLUSION: This study illustrates that having a care navigator in place empowers those with severe mental illness to improve the management of their diabetes. Future research should focus on the extent to which care navigators are effective in improving specific outcomes.

Text mining occupations from the mental health electronic health record: a natural language processing approach using records from the Clinical Record Interactive Search (CRIS) platform in south London, UK.

OBJECTIVES: We set out to develop, evaluate and implement a novel application using natural language processing to text mine occupations from the free-text of psychiatric clinical notes. DESIGN: Development and validation of a natural language processing application using General Architecture for Text Engineering software to extract occupations from de-identified clinical records. SETTING AND PARTICIPANTS: Electronic health records from a large secondary mental healthcare provider in south London, accessed through the Clinical Record Interactive Search platform. The text mining application was run over the free-text fields in the electronic health records of 341 720 patients (all aged ≥16 years). OUTCOMES: Precision and recall estimates of the application performance; occupation retrieval using the application compared with structured fields; most common patient occupations; and analysis of key sociodemographic and clinical indicators for occupation recording. RESULTS: Using the structured fields alone, only 14% of patients had occupation recorded. By implementing the text mining application in addition to the structured fields, occupations were identified in 57% of patients. The application performed on gold-standard human-annotated clinical text at a precision level of 0.79 and recall level of 0.77. The most common patient occupations recorded were 'student' and 'unemployed'. Patients with more service contact were more likely to have an occupation recorded, as were patients of a male gender, older age and those living in areas of lower deprivation. CONCLUSION: This is the first time a natural language processing application has been used to successfully derive patient-level occupations from the free-text of electronic mental health records, performing with good levels of precision and recall, and applied at scale. This may be used to inform clinical studies relating to the broader social determinants of health using electronic health records.