Effectiveness of a Whole Health Model of Care Emphasizing Complementary and Integrative Health on Reducing Opioid Use Among Patients with Chronic Pain
Abstract: The opioid crisis has necessitated new approaches to managing chronic pain. The Veterans Health Administration (VHA) Whole Health model of care, with its focus on patient empowerment and emphasis on nonpharmacological approaches to pain management, is a promising strategy for reducing patients' use of opioids. We aim to assess whether the VHA's Whole Health pilot program impacted longitudinal patterns of opioid utilization among patients with chronic musculoskeletal pain. A cohort of 4,869 Veterans with chronic pain engaging in Whole Health services was compared with a cohort of 118,888 Veterans receiving conventional care. All patients were continuously enrolled in VHA care from 10/2017 through 3/2019 at the 18 VHA medical centers participating in the pilot program. Inverse probability of treatment weighting and multivariate analyses were used to adjust for observable differences in patient characteristics between exposures and conventional care. Patients exposed to Whole Health services were offered nine complementary and integrative health therapies alone or in combination with novel Whole Health services including goal-setting clinical encounters, Whole Health coaching, and personal health planning. The main measure was change over an 18-month period in prescribed opioid doses starting from the six-month period prior to qualifying exposure. Prescribed opioid doses decreased by -12.0% in one year among Veterans who began complementary and integrative health therapies compared to similar Veterans who used conventional care; -4.4% among Veterans who used only Whole Health services such as goal setting and coaching compared to conventional care, and -8.5% among Veterans who used both complementary and integrative health therapies combined with Whole Health services compared to conventional care. VHA's Whole Health national pilot program was associated with greater reductions in prescribed opioid doses compared to secular trends associated with conventional care, especially when Veterans were connected with complementary and integrative health therapies.
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.