Cessation of group battlefield acupuncture visits due to COVID-19: a pilot study
Abstract: Background: Prior to the COVID-19 pandemic, battlefield acupuncture (BFA) was offered to veterans with chronic pain in multidisciplinary group visits. Objective: We aimed to assess the impact of cessation of BFA due to COVID-19 and to determine the utility of different aspects of the group visits for chronic pain management. Methods: Participants who had attended at least three BFA group visits completed questionnaires assessing the impact of treatment interruption on pain, overall function and desire to resume treatment. Results: Thirty-nine veterans were surveyed; 49% responded to the questionnaire. Ninety percent (17/19) agreed that BFA was an important part of pain management and that their pain had worsened after treatment interruption. Seventy-four percent (14/19) responded that they were taking more pain medications since BFA had ended. Ninety-five percent (18/19) responded that BFA improved daily function; 79% (15/19) agreed that BFA improved their sleep. Ninety-five percent (18/19) were interested in resuming BFA. Camaraderie was mentioned as the most helpful aspect of the group by 8/19 (42%) of participants. Participation of health psychology and nutrition were each mentioned as a most helpful aspect of the group by 5/19 (26%) of participants. Conclusion: Our results suggest that participants may have believed that BFA, camaraderie, and input from nutrition and health psychology services were important contributors to their pain control. The results also suggest that veterans may have suffered worsening pain, used more pain medications, and had worsening quality of sleep and daily function during the COVID-related clinic disruption, and that they were interested in resumption of the program.
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.