Bridging the gap: Leveraging natural language processing to identify reasons for buprenorphine discontinuation in Electronic Health Records
Description
Life-saving treatment for opioid use disorder (OUD), such as the FDA-approved medication buprenorphine, remains underutilized. Buprenorphine has been shown to reduce illicit opioid use and risk of overdose mortality. Understanding treatment barriers can offer us opportunities for improved recovery. The PanTher Electronic Health Records (EHR) data from OptumLabs are a unique and important data asset, containing structured variables, such as diagnoses and procedures, laboratory measures, and medication records, as well as semi-structured data derived from clinical notes through natural language processing (NLP). The NLP-derived data contain helpful contextual information but have been difficult to use thus far.
Detailed example
Through participation in the Data Science Upskilling Program (DSU), the DOP-DSU team was able to extract actionable insights from EHR, contextualized further by supplementing with NLP-derived data from clinical notes. They developed an algorithm identifying patients with OUD who discontinued buprenorphine and used it to characterize discontinuation reasons using EHR. They were also able to better understand limitations of NLP-derived data from provider notes in EHRs. However, despite the limitations of EHR, findings from this project can complement claims data and surveys from a patient care management perspective, and close the loop in our understanding of patients’ medication access journey.
AI / analytics pattern
Natural Language Processing: AI that processes, interprets, and shares information in human language.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
The Data Science Upskilling Program advances a key focus of the agency’s Data Modernization Initiative, i.e., that CDC's mission is to give all people the information they need for decision-making and wellbeing. Through participation in the Data Science Upskilling Program (DSU), the DOP-DSU team was able to extract actionable insights from EHR, contextualized further by supplementing with NLP-derived data from clinical notes. They developed an algorithm identifying patients with OUD who discontinued buprenorphine and used it to characterize discontinuation reasons using EHR. This has helped provide a fuller understanding of the what and the why surrounding discontinuation of this life-saving treatment, underscoring the need for strategies that improve retention in treatment. The team also built important DOP capacity in working with EHR data and NLP-derived data, including assessing data quality, and linking, processing, analyzing, visualizing and interpreting these data.
Controls / human review
ATO: Not reported; PIA: Not published