Measuring pain care quality in the Veterans Health Administration primary care setting

Luther, Stephen L.; Finch, Dezon; Bouayad, Lina; McCart, James A.; Han, Ling; Dobscha, Steven K.; Skanderson, Melissa; Fodeh, Samah Jamal; Hahm, Bridget; Lee, Allison; Goulet, Joseph L.; Brandt, Cynthia; Kerns, Robert D.

doi:10.1097/j.pain.0000000000002477

Cited by 7 publications

(7 citation statements)

References 26 publications

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“…We applied a previously developed and described rule-based NLP algorithm using Python 3.5 to extract PCQ indicators from the corpus. 20 The algorithm performed strongly in a study of 1 year of primary care notes from pain visits (Fmeasure ¼ 91.9%, Precision ¼ 93.0%, Recall ¼ 90.9%). 20 The algorithm uses a line-by-line analysis of text documents paired with rule-based token regular expression matching to identify annotation spans-or tagged snippets of text-mapped to PCQ indicator classes defined by a vocabulary.…”

Section: Discussionmentioning

confidence: 97%

“…20 The algorithm performed strongly in a study of 1 year of primary care notes from pain visits (Fmeasure ¼ 91.9%, Precision ¼ 93.0%, Recall ¼ 90.9%). 20 The algorithm uses a line-by-line analysis of text documents paired with rule-based token regular expression matching to identify annotation spans-or tagged snippets of text-mapped to PCQ indicator classes defined by a vocabulary. The vocabulary was reviewed by the study team to review appropriateness for application in chiropractic clinic documentation.…”

Section: Discussionmentioning

confidence: 97%

“… 14 Information extraction from unstructured text using NLP has been successful across multiple sources, including physician reports, 15 radiology reports, 16 and patient experience surveys, 17 and across different settings, including VHA. 18 NLP has been used to evaluate pain care quality (PCQ) in VHA primary care clinics, including developing an extraction tool, 19 a PCQ indicator score, 20 and artificial intelligence methods to detect instances of pain assessment. 20 21 The defined PCQ indicators represent essential elements of guideline-concordant, comprehensive pain assessment, treatment planning, and reassessment, and their documentation in the clinical text note may offer a measurable representation of the quality of pain care being provided.…”

Section: Background and Significancementioning

confidence: 99%

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

et al. 2023

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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 (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.

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Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 97%

Section: Background and Significancementioning

confidence: 99%

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

et al. 2023

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“…The Million Veteran Program (MVP) 38 , an observational cohort study and mega-biobank implemented in the U.S. Department of Veterans Affairs (VA) health care system, includes data on routine pain screening. Pain ratings in the MVP use an 11-point ordinal Numeric Rating Scale (NRS), which has been a standard practice in VA primary care for more than a decade 39 . The NRS has been shown to be a consistent, valid measure of reported pain 40-42 and is particularly informative for a GWAS of pain, as over 50% of VA patients experience chronic pain 43 .…”

Section: Introductionmentioning

confidence: 99%

The genetic architecture of pain intensity in a sample of 598,339 U.S. veterans

Toikumo

Vickers‐Smith

Jinwala³

et al. 2023

Preprint

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Chronic pain is a common problem, with more than one-fifth of adult Americans reporting pain daily or on most days. It adversely affects quality of life and imposes substantial personal and economic costs. Efforts to treat chronic pain using opioids played a central role in precipitating the opioid crisis. Despite an estimated heritability of 25-50%, the genetic architecture of chronic pain is not well characterized, in part because studies have largely been limited to samples of European ancestry. To help address this knowledge gap, we conducted a cross-ancestry meta-analysis of pain intensity in 598,339 participants in the Million Veteran Program, which identified 125 independent genetic loci, 82 of which are novel. Pain intensity was genetically correlated with other pain phenotypes, level of substance use and substance use disorders, other psychiatric traits, education level, and cognitive traits. Integration of the GWAS findings with functional genomics data shows enrichment for putatively causal genes (n = 142) and proteins (n = 14) expressed in brain tissues, specifically in GABAergic neurons. Drug repurposing analysis identified anticonvulsants, beta-blockers, and calcium-channel blockers, among other drug groups, as having potential analgesic effects. Our results provide insights into key molecular contributors to the experience of pain and highlight attractive drug targets.

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“…The Million Veteran Program (MVP) 38 , an observational cohort study and mega-biobank implemented in the U.S. Department of Veterans Affairs (VA) health care system, includes data on routine pain screening. Pain ratings in the MVP use an 11-point ordinal Numeric Rating Scale (NRS), which has been a standard practice in VA primary care for more than a decade 39 . The NRS has been shown to be a consistent, valid measure of reported pain [40][41][42] and is particularly informative for a GWAS of pain, as over 50% of VA patients experience chronic pain 43 .…”

Section: Introductionmentioning

confidence: 99%

The genetic architecture of pain intensity in a sample of 598,339 U.S. veterans

Kranzler

Toikumo

Vickers-Smith

et al. 2023

Preprint

View full text Add to dashboard Cite

Chronic pain is a common problem, with more than one-fifth of adult Americans reporting pain daily or on most days. It adversely affects quality of life and imposes substantial personal and economic costs. Efforts to treat chronic pain using opioids played a central role in precipitating the opioid crisis. Despite an estimated heritability of 25–50%, the genetic architecture of chronic pain is not well characterized, in part because studies have largely been limited to samples of European ancestry. To help address this knowledge gap, we conducted a cross-ancestry meta-analysis of pain intensity in 598,339 participants in the Million Veteran Program, which identified 125 independent genetic loci, 82 of which are novel. Pain intensity was genetically correlated with other pain phenotypes, level of substance use and substance use disorders, other psychiatric traits, education level, and cognitive traits. Integration of the GWAS findings with functional genomics data shows enrichment for putatively causal genes (n = 142) and proteins (n = 14) expressed in brain tissues, specifically in GABAergic neurons. Drug repurposing analysis identified anticonvulsants, beta-blockers, and calcium-channel blockers, among other drug groups, as having potential analgesic effects. Our results provide insights into key molecular contributors to the experience of pain and highlight attractive drug targets.

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Measuring pain care quality in the Veterans Health Administration primary care setting

Abstract: Natural language processing was used to extract measures of pain care quality from primary care progress notes filling an important scientific knowledge and practice gap.

Cited by 7 publications

References 26 publications

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

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

The genetic architecture of pain intensity in a sample of 598,339 U.S. veterans

The genetic architecture of pain intensity in a sample of 598,339 U.S. veterans

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