Deploying a national clinical text processing infrastructure

Author:

McManus Kimberly F1ORCID,Stringer Johnathon Michael2,Corson Neal3,Fodeh Samah45,Steinhardt Steven6,Levin Forrest L6,Shotqara Asqar S7,D’Auria Joseph8,Fielstein Elliot M910,Gobbel Glenn T10,Scott John11,Trafton Jodie A12,Taddei Tamar H45,Erdos Joseph45,Tamang Suzanne R212

Affiliation:

1. Department of Veterans Affairs, Office of the CTO , Washington, DC 20571, United States

2. Division of Immunology and Rheumatology, Department of Medicine, Stanford University , Stanford, CA 94304, United States

3. Department of Veterans Affairs , San Diego, CA 92108, United States

4. Department of Veterans Affairs , West Haven, CT 06516, United States

5. Yale School of Medicine , New Haven, CT 06510, United States

6. Evergreen Design LLC , Guilford, CT 06437, United States

7. Department of Veterans Affairs, Center for Innovation to Implementation (Ci2i) , Palo Alto, CA 94304, United States

8. Product Engineering, Department of Veterans Affairs , Austin, TX 78741, United States

9. Department of Veterans Affairs, Office of Mental Health and Suicide Prevention, Veterans Health Administration , Nashville, TN 37212, United States

10. Department of Biomedical Informatics, Vanderbilt University Medical Center , Nashville, TN 37203, United States

11. Department of Veterans Affairs, Clinical Informatics and Data Management Office, Veterans Health Administration , Washington, DC 20571, United States

12. Department of Veterans Affairs, Office of Mental Health and Suicide Prevention, Program Evaluation Resource Center , Palo Alto, CA 94304, United States

Abstract

Abstract 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.

Publisher

Oxford University Press (OUP)

Subject

Health Informatics

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