A national study of clinical discussions about cannabis use among Veteran patients prescribed opioids

Abstract: BACKGROUND: The Veterans Health Administration tracks urine drug tests (UDTs) among patients on long-term opioid therapy (LTOT) and recommends discussing the health effects of cannabis use. OBJECTIVE: To determine the occurrence of cannabis-related discussions between providers and patients on LTOT during six months following UDT positive for cannabis, and examine factors associated with documenting cannabis use. DESIGN: We identified patients prescribed LTOT with a UDT positive for cannabis in 2019. We developed a text-processing tool to extract discussions around cannabis use from their charts. SUBJECTS: Twelve thousand seventy patients were included. Chart review was conducted on a random sample of 1,946 patients. MAIN MEASURES: The presence of a cannabis term in the chart suggesting documented cannabis use or cannabis-related discussions. Content of those discussions was extracted in a subset of patients. Logistic regression was used to examine the association between patient factors, including state of residence legal status, with documentation of cannabis use. KEY RESULTS: Among the 12,070 patients, 65.8% (N = 7,948) had a cannabis term, whereas 34.1% (N = 4,122) of patients lacked a cannabis term, suggesting that no documentation of cannabis use or discussion between provider and patient took place. Among the subset of patients who had a discussion documented, 47% related to cannabis use for medical reasons, 35% related to a discussion of VA policy or legal issues, and 17% related to a discussion specific to medical risks or harm reduction strategies. In adjusted analyses, residents of states with legalized recreational cannabis were less likely to have any cannabis-related discussion compared to patients in non-legal states [OR 0.73, 95% CI 0.64-0.82]. CONCLUSIONS: One-third of LTOT patients did not have documentation of cannabis use in the chart in the 6 months following a positive UDT for cannabis. Discussions related to the medical risks of cannabis use or harm reduction strategies were uncommon.

Read the full article
Report a problem with this article

Related articles

  • More for Researchers

    Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data

    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.

    Mental Health Peer reviewed