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CC BY-NC-ND 4.0 · Appl Clin Inform 2023; 14(03): 600-608
DOI: 10.1055/a-2091-1162
Research Article

Extracting Pain Care Quality Indicators from U.S. Veterans Health Administration Chiropractic Care Using Natural Language Processing

Authors

  • Brian C. Coleman

    1   Pain Research, Informatics, Multimorbidities, and Education (PRIME) Center, VA Connecticut Healthcare System, West Haven, Connecticut, United States
    2   Yale Center for Medical Informatics, Yale School of Medicine, Yale University, New Haven, Connecticut, United States

  • Dezon Finch

    3   Research Service, James A. Haley Veterans Hospital, Tampa, Florida, United States

  • Rixin Wang

    1   Pain Research, Informatics, Multimorbidities, and Education (PRIME) Center, VA Connecticut Healthcare System, West Haven, Connecticut, United States
    2   Yale Center for Medical Informatics, Yale School of Medicine, Yale University, New Haven, Connecticut, United States

  • Stephen L. Luther

    3   Research Service, James A. Haley Veterans Hospital, Tampa, Florida, United States
    4   College of Public Health, University of South Florida, Tampa, Florida, United States

  • Alicia Heapy

    1   Pain Research, Informatics, Multimorbidities, and Education (PRIME) Center, VA Connecticut Healthcare System, West Haven, Connecticut, United States
    5   Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, Connecticut, United States

  • Cynthia Brandt

    1   Pain Research, Informatics, Multimorbidities, and Education (PRIME) Center, VA Connecticut Healthcare System, West Haven, Connecticut, United States
    2   Yale Center for Medical Informatics, Yale School of Medicine, Yale University, New Haven, Connecticut, United States

  • Anthony J. Lisi

    1   Pain Research, Informatics, Multimorbidities, and Education (PRIME) Center, VA Connecticut Healthcare System, West Haven, Connecticut, United States
    2   Yale Center for Medical Informatics, Yale School of Medicine, Yale University, New Haven, Connecticut, United States

FundingThis study was funded by the Health Services Research and Development, (grant numbers: CIN-13-407 and IIR-12-118) and U.S. Department of Health and Human Services, National Institutes of Health, National Center for Complementary and Integrative Health, (grant number: K08AT011570), and NCMIC Foundation.
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Abstract

Background Musculoskeletal pain is common in the Veterans Health Administration (VHA), and there is growing national use of chiropractic services within the VHA. Rapid expansion requires scalable and autonomous solutions, such as natural language processing (NLP), to monitor care quality. Previous work has defined indicators of pain care quality that represent essential elements of guideline-concordant, comprehensive pain assessment, treatment planning, and reassessment.

Objective Our purpose was to identify pain care quality indicators and assess patterns across different clinic visit types using NLP on VHA chiropractic clinic documentation.

Methods Notes from ambulatory or in-hospital chiropractic care visits from October 1, 2018 to September 30, 2019 for patients in the Women Veterans Cohort Study were included in the corpus, with visits identified as consultation visits and/or evaluation and management (E&M) visits. Descriptive statistics of pain care quality indicator classes were calculated and compared across visit types.

Results There were 11,752 patients who received any chiropractic care during FY2019, with 63,812 notes included in the corpus. Consultation notes had more than twice the total number of annotations per note (87.9) as follow-up visit notes (34.7). The mean number of total classes documented per note across the entire corpus was 9.4 (standard deviation [SD]  =  1.5). More total indicator classes were documented during consultation visits with (mean  =  14.8, SD  =  0.9) or without E&M (mean  =  13.9, SD  =  1.2) compared to follow-up visits with (mean  =  9.1, SD  =  1.4) or without E&M (mean  =  8.6, SD  =  1.5). Co-occurrence of pain care quality indicators describing pain assessment was high.

Conclusion VHA chiropractors frequently document pain care quality indicators, identifiable using NLP, with variability across different visit types.

Keywords

musculoskeletal pain - natural language processing - Veterans health - pain care quality

Availability of Data and Materials

To maximize protection security of Veterans' data while making these data available to researchers, the U.S. Department of Veterans Affairs (VA) developed the VA Informatics and Computing Infrastructure (VINCI). VA researchers must log onto VINCI via a secure gateway or virtual private network connection (VPN) and use a virtual workspace on VINCI to access and analyze VA data. By VA Office of Research and Development policy, VINCI does not allow the transfer of any patient-level data out of its secure environment without special permission. Researchers who are not VA employees must be vetted and receive “without compensation” (WOC) employee status to gain access to VINCI. All analyses performed for this study took place on the VINCI platform. For questions about data access, contact study lead, Dr. Brian C. Coleman (Brian.Coleman2@va.gov) or the VA Office of Research and Development (VHACOORDRegulatory@va.gov).


Protection of Human and Animal Subjects

The study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects, and was reviewed and approved by the VA Connecticut Healthcare System Institutional Review Board.


Supplementary Material



Publikationsverlauf

Eingereicht: 05. Januar 2023

Angenommen: 27. April 2023

Accepted Manuscript online:
10. Mai 2023

Artikel online veröffentlicht:
02. August 2023

© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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