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

C Coleman B, Finch D, Wang R, L Luther S, Heapy A, Brandt C, J Lisi A. Extracting Pain Care Quality Indicators from U.S. Veterans Health Administration Chiropractic Care Using Natural Language Processing. Applied clinical informatics. 2023 May 1; 14(3):600-608.

Dimensions for VA is a web-based tool available to VA staff that enables detailed searches of published research and research projects.

If you have VA-Intranet access, click here for more information vaww.hsrd.research.va.gov/dimensions/

VA staff not currently on the VA network can access Dimensions by registering for an account using their VA email address.    Search Dimensions for VA for this citation
* Don't have VA-internal network access or a VA email address? Try searching the free-to-the-public version of Dimensions

Search for Abstract from PubMed

---

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 (EandM) 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 EandM (mean?? = ?13.9, SD?? = ?1.2) compared to follow-up visits with (mean?? = ?9.1, SD?? = ?1.4) or without EandM (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.