Characterising trauma-informed aged care: An appreciative inquiry approach
Abstract: Objective: While Trauma-informed care (TIC) has the potential to improve the quality of aged and dementia care, the challenge remains in translating the principles of TIC into practice. This study aimed to characterise what trauma-informed aged care looks like in practice, by learning from an aged care service acknowledged as delivering trauma-informed aged care effectively. Method: We conducted an appreciative inquiry study within a residential aged care service catering for veterans and others with trauma histories. Observation of care behaviours, interviews with staff and residents, and organisational policy mapping were used to identify elements that maximised care safety and accessibility for trauma survivors. Data were analysed and triangulated using a framework analysis approach. Results: The aged care provider embedded the principles of TIC into its staff training (i) to promote understanding of how trauma may affect experiences in care, and (ii) to adapt care when appropriate to promote safety. The service promoted a calm atmosphere where residents could make choices and felt safe. Uniforms and signage provided consistency, clarity, and transparency for residents. Staff behaviours demonstrated respect, fostered trust, and anticipated needs without unnecessarily imposing care. Staff consistently offered choices, used residents' names, sought permission before providing care, and offered reassurance. Staff reported high morale with a commitment to delivering high quality care, and feedback to management. Effective communication promoted information sharing and trust among staff. Conclusion: Trauma-informed practice was facilitated through organisational policy, a dignified environment, and thoughtful staff behaviour creating safety, choice, and control for residents.
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.