Managing Chronic Pain in Primary Care: It Really Does Take a Village
Abstract: Some healthcare systems are relieving primary care providers (PCPs) of “the burden” of managing chronic pain and opioid prescribing, instead offloading chronic pain management to pain specialists. Last year the Centers for Disease Control and Prevention recommended a biopsychosocial approach to pain management that discourages opioid use and promotes exercise therapy, cognitive behavioral therapy and non-opioid medications as first-line patient-centered, multi-modal treatments best delivered by an interdisciplinary team. In the private sector, interdisciplinary pain management services are challenging to assemble, separate from primary care and not typically reimbursed. In contrast, in a fully integrated health care system like the Veterans Health Administration (VHA), interdisciplinary clinics already exist, and one such clinic, the Integrated Pain Team (IPT) clinic, integrates and co-locates pain-trained PCPs, a psychologist and a pharmacist in primary care. The IPT clinic has demonstrated significant success in opioid risk reduction. Unfortunately, proposed legislation threatens to dismantle aspects of the VA such that these interdisciplinary services may be eliminated. This Perspective explains why it is critical not only to maintain interdisciplinary pain services in VHA, but also to consider disseminating this model to other health care systems in order to implement patient-centered, guideline-concordant care more broadly.
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.