Development of the traumatic brain injury Rehabilitation Needs Survey: a Veterans Affairs TBI Model Systems study
Abstract: Purpose: To describe the development of the Rehabilitation Needs Survey (RNS) for persons in the chronic phase of traumatic brain injury (TBI) recovery. Materials and methods: RNS items were generated following a literature review (January – March 2015) on the topic of rehabilitation needs and revised via consensus from an expert panel of TBI clinicians and researchers. The RNS was added to the VA TBI Model Systems longitudinal study; data collection occurred between 2015–2019. Needs were classified as current (if endorsed) or absent; if current, needs were classified as unmet if no help was received. Need frequency and association with rehabilitation outcomes were presented. Results: Eight studies examined rehabilitation needs and formed the initial item pool of 42 needs. This was reduced to form the 21-item RNS which was administered at year 1 (n = 260) and year 2 (n = 297) post-TBI. Number of needs endorsed was 8–9, and number of unmet needs was 1–2, on average. Number of needs was correlated with functional status, neurobehavioral symptoms, and mental health symptoms (p < 0.05) suggesting support for convergent validity of the RNS. Conclusion: The RNS is a new measure of rehabilitation needs following TBI. Further investigation into its psychometrics and clinical utility is recommended.
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.