Protocol for a Real-Time Electronic Health Record Implementation of a Natural Language Processing and Deep Learning Clinical Decision Support Tool: A Use-Case for an Opioid Misuse Screener in Hospitalized Adults

The clinical narrative in the electronic health record (EHR) carries valuable information for predictive analytics, but its free-text form is difficult to mine and analyze for clinical decision support (CDS). Large-scale clinical natural language processing (NLP) pipelines have focused on data warehouse applications for retrospective research efforts. There remains a paucity of evidence for implementing open-source NLP engines to provide interoperable and standardized CDS at the bedside. This clinical protocol describes a reproducible workflow for a cloud service to ingest, process, and store clinical notes as Health Level 7 messages from a major EHR vendor in an elastic cloud computing environment. We apply the NLP CDS infrastructure to a use-case for hospital-wide opioid misuse screening using an open-source deep learning model that leverages clinical notes mapped to standardized medical vocabularies. The resultant NLP and deep learning pipeline can process clinical notes and provide decision support to the bedside within minutes of a provider entering a note into the EHR for all hospitalized patients. The protocol includes a human-centered design and an implementation framework with a cost-effectiveness and patient outcomes analysis plan.