Objectives: Clinical text processing offers a promising avenue for improving multiple aspects of healthcare, though operational deployment remains a substantial challenge. This case report details the implementation of a national clinical text processing infrastructure within the Department of Veterans Affairs (VA).
Methods: Two foundational use cases, cancer case management and suicide and overdose prevention, illustrate how text processing can be practically implemented at scale for diverse clinical applications using shared services.
Results: Insights from these use cases underline both commonalities and differences, providing a replicable model for future text processing applications.
Conclusions: This project enables more efficient initiation, testing, and future deployment of text processing models, streamlining the integration of these use cases into healthcare operations. This project implementation is in a large integrated health delivery system in the United States, but we expect the lessons learned to be relevant to any health system, including smaller local and regional health systems in the United States.
Keywords: delivery of health care; machine learning; mental health; natural language processing.
© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.