Health and social care needs of the Armed Forces Community in Northamptonshire
Abstract: The Armed Forces Covenant is a promise from the nation that those who serve or have served in the British Armed Forces, and their families, are treated fairly. In Northamptonshire the covenant is administered by nineteen partners from across the county, who work closely together to ensure the covenant aims are upheld. Currently, there is a lack of information about the Armed Forces community in Northamptonshire, including their health and social care needs. Therefore, Healthwatch Northamptonshire carried out a survey on behalf of the Armed Forces Covenant Northamptonshire to find out more about these needs so they can be addressed by commissioners and service providers, and help highlight potential projects going forward. We also asked people for demographic data that will enable the University of Northampton to further explore any links between this and health and care needs.
Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.