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

Background: Automated de-identification methods for removing protected health information (PHI) from the source notes of the electronic health record (EHR) rely on building systems to recognize mentions of PHI in text, but they remain inadequate at ensuring perfect PHI removal. As an alternative to relying on de-identification systems, we propose the following solutions: (1) Mapping the corpus of documents to standardized medical vocabulary (concept unique identifier [CUI] codes mapped from the Unified Medical Language System) thus eliminating PHI as inputs to a machine learning model; and (2) training character-based machine learning models that obviate the need for a dictionary containing input words/n-grams. We aim to test the performance of models with and without PHI in a use-case for an opioid misuse classifier. Methods: An observational cohort sampled from adult hospital inpatient encounters at a health system between 2007 and 2017. A case-control stratified sampling (n = 1000) was performed to build an annotated dataset for a reference standard of cases and non-cases of opioid misuse. Models for training and testing included CUI codes, character-based, and n-gram features. Models applied were machine learning with neural network and logistic regression as well as expert consensus with a rule-based model for opioid misuse. The area under the receiver operating characteristic curves (AUROC) were compared between models for discrimination. The Hosmer-Lemeshow test and visual plots measured model fit and calibration. Results: Machine learning models with CUI codes performed similarly to n-gram models with PHI. The top performing models with AUROCs > 0.90 included CUI codes as inputs to a convolutional neural network, max pooling network, and logistic regression model. The top calibrated models with the best model fit were the CUIbased convolutional neural network and max pooling network. The top weighted CUI codes in logistic regression has the related terms 'Heroin' and 'Victim of abuse'.

Objectives: Even where treatment is available, people who use drugs (PWUD) may not seek help. Few published studies examine beliefs, experiences, and perceptions of evidence-based treatment among PWUD who are not actively engaged in care. This study aimed to explore the experiences of PWUD in considering or accessing treatment and gauge receptiveness to low-threshold treatment models. Methods: A purposeful sample of participants actively using opioids and with previous interest in or experience with treatment was recruited from a harm reduction program in Chicago. Semistructured interviews were conducted to explore key phenomena while allowing for unanticipated themes. The instrument included questions about historical drug use, treatment experience, and perceptions of how to improve treatment access and services. Private interviews were audio recorded, transcribed, and double coded by 2 analysts. Queries of coded data were analyzed using issue-focused analysis to identify themes. Results: The sample (N = 40) approximated groups at highest risk of fatal overdose in Chicago, with more than 80% between the ages of 45 to 64 years, 65% African American, and 62% male identified. The majority had prior treatment experience, although all resumed use after completing or leaving treatment. The most prevalent barriers to treatment included structural barriers related to social determinants, lack of readiness for abstinence, burdensome intake procedures, and regulatory/programmatic requirements. Most participants expressed interest in low-threshold treatment. Conclusions: Existing treatment barriers may be addressed by shifting to lower-threshold intake processes and/or outreach-based delivery of opioid agonist treatment. Engaging PWUD in efforts to create lowerthreshold treatment programs is necessary to ensure that needs are met.