ArticleApplied clinical informatics2023
Extracting Pain Care Quality Indicators from U.S. Veterans Health Administration Chiropractic Care Using Natural Language Processing.
Article in Applied clinical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- Moving towards the use of artificial intelligence in pain management.European journal of pain (London, England) · 2025Pooled it
- A Delphi panel consensus study validating the appropriateness, relevance, and clarity of quality measures for chiropractic care.BMC health services research · 2025Article
- Identifying Patient-Reported Outcome Measure Documentation in Veterans Health Administration Chiropractic Clinic Notes: Natural Language Processing Analysis.JMIR medical informatics · 2025Article
- A descriptive analysis of the contents of Care Response, an international data set of patient-reported outcomes for chiropractic patients.Chiropractic & manual therapies · 2023Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundMusculoskeletal 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.
objectiveOur purpose was to identify pain care quality indicators and assess patterns across different clinic visit types using NLP on VHA chiropractic clinic documentation.
methodsNotes 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.
resultsThere 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.
conclusionVHA chiropractors frequently document pain care quality indicators, identifiable using NLP, with variability across different visit types.
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Registered trials
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