P.285 The substance use intervention team: Preliminary analysis of a population-level strategy addressing the opioid crisis at an academic medical center

Thompson, H.; Hill, Kristin; Jadhav, Rahul; Webb, Thomas H.; Pollack, Mark P; Karnik, Niranjan S.

doi:10.1016/j.euroneuro.2019.09.314

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“…Despite positive urine drug screens, it is not possible to determine whether these patients actually have an OUD that would necessitate treatment, or whether they were occasional opioid users with subsequent trauma, injury or accidental poisoning. Screening, brief intervention, and referral to treatment (SBIRT) is one approach that could be used to identify which patients may benefit from motivational interviewing for unhealthy but infrequent use, versus others being identified with an OUD and needing initiation and/or referral to treatment [42]. Patients identified with OUD in the ED or hospital setting could be offered opioid agonist treatment, such as buprenorphine or methadone prior to discharge with linkage to community treatment.…”

Section: Discussionmentioning

confidence: 99%

Subtypes in patients with opioid misuse: A prognostic enrichment strategy using electronic health record data in hospitalized patients

et al. 2019

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Background Approaches are needed to better delineate the continuum of opioid misuse that occurs in hospitalized patients. A prognostic enrichment strategy with latent class analysis (LCA) may facilitate treatment strategies in subtypes of opioid misuse. We aim to identify subtypes of patients with opioid misuse and examine the distinctions between the subtypes by examining patient characteristics, topic models from clinical notes, and clinical outcomes. Methods This was an observational study of inpatient hospitalizations at a tertiary care center between 2007 and 2017. Patients with opioid misuse were identified using an operational definition applied to all inpatient encounters. LCA with eight class-defining variables from the electronic health record (EHR) was applied to identify subtypes in the cohort of patients with opioid misuse. Comparisons between subtypes were made using the following approaches: (1) descriptive statistics on patient characteristics and healthcare utilization using EHR data and census-level data; (2) topic models with natural language processing (NLP) from clinical notes; (3) association with hospital outcomes. Findings The analysis cohort was 6,224 (2.7% of all hospitalizations) patient encounters with opioid misuse with a data corpus of 422,147 clinical notes. LCA identified four subtypes with differing patient characteristics, topics from the clinical notes, and hospital outcomes. Class 1

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Section: Discussionmentioning

confidence: 99%