Faculty, Staff and Student Publications
Publication Date
6-1-2023
Journal
Journal of Biomedical Informatics
Abstract
Clinical documentation in electronic health records contains crucial narratives and details about patients and their care. Natural language processing (NLP) can unlock the information conveyed in clinical notes and reports, and thus plays a critical role in real-world studies. The NLP Working Group at the Observational Health Data Sciences and Informatics (OHDSI) consortium was established to develop methods and tools to promote the use of textual data and NLP in real-world observational studies. In this paper, we describe a framework for representing and utilizing textual data in real-world evidence generation, including representations of information from clinical text in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), the workflow and tools that were developed to extract, transform and load (ETL) data from clinical notes into tables in OMOP CDM, as well as current applications and specific use cases of the proposed OHDSI NLP solution at large consortia and individual institutions with English textual data. Challenges faced and lessons learned during the process are also discussed to provide valuable insights for researchers who are planning to implement NLP solutions in real-world studies.
Keywords
Humans, Data Science, Electronic Health Records, Medical Informatics, Natural Language Processing, Narration, Real-world study, Natural language processing
DOI
10.1016/j.jbi.2023.104343
PMID
36935011
PMCID
PMC10428170
PubMedCentral® Posted Date
6-1-2024
PubMedCentral® Full Text Version
Author MSS
Published Open-Access
yes
