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

Brian C. Coleman; Dezon Finch; Rixin Wang; Stephen L. Luther; Alicia Heapy; Cynthia Brandt; Anthony J. Lisi

doi:10.1055/a-2091-1162

Applied Clinical Informatics, Inhaltsverzeichnis

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

Autor*innen

Brian C. Coleman

¹Pain Research, Informatics, Multimorbidities, and Education (PRIME) Center, VA Connecticut Healthcare System, West Haven, Connecticut, United States

²Yale Center for Medical Informatics, Yale School of Medicine, Yale University, New Haven, Connecticut, United States
Dezon Finch

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

¹Pain Research, Informatics, Multimorbidities, and Education (PRIME) Center, VA Connecticut Healthcare System, West Haven, Connecticut, United States

²Yale Center for Medical Informatics, Yale School of Medicine, Yale University, New Haven, Connecticut, United States
Stephen L. Luther

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

⁴College of Public Health, University of South Florida, Tampa, Florida, United States
Alicia Heapy

¹Pain Research, Informatics, Multimorbidities, and Education (PRIME) Center, VA Connecticut Healthcare System, West Haven, Connecticut, United States

⁵Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, Connecticut, United States
Cynthia Brandt

¹Pain Research, Informatics, Multimorbidities, and Education (PRIME) Center, VA Connecticut Healthcare System, West Haven, Connecticut, United States

²Yale Center for Medical Informatics, Yale School of Medicine, Yale University, New Haven, Connecticut, United States
Anthony J. Lisi

¹Pain Research, Informatics, Multimorbidities, and Education (PRIME) Center, VA Connecticut Healthcare System, West Haven, Connecticut, United States

²Yale Center for Medical Informatics, Yale School of Medicine, Yale University, New Haven, Connecticut, United States

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

Volltext

Referenzen

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