ArticleHealth services research2025
Extracting Housing and Food Insecurity Information From Clinical Notes Using cTAKES.
Article in Health services research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of Natural Language Processing and Large Language Models for Social Determinants of Health: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Article
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
objectiveTo assess the utility and challenges of using natural language processing (NLP) in electronic health records (EHRs) to ascertain health-related social needs (HRSNs) among older adults. STUDY SETTING AND
designWe extracted HRSN information using the NLP system Clinical Text Analysis and Knowledge Extraction System (cTAKES), combined with Concept Unique Identifiers and Systematized Nomenclature for Medicine codes. We validated cTAKES performance, via manual chart review, on two HRSNs: food insecurity, which was included in the healthcare system's HRSN screening tool, and housing insecurity, which was not. DATA SOURCES AND ANALYTIC SAMPLE: De-identified EHRs in a large California healthcare system (January 2013 through October 2022) from 119,127 patients aged 55+ in primary and emergency care settings (n = 1,385,259 clinical notes). PRINCIPAL
findingsAlthough cTAKES had a moderate positive predictive value (77.5%) for housing insecurity, housing challenges among older adults frequently did not align with the concepts the algorithm recognized. cTAKES performed poorly for food insecurity (positive predictive value: 18.5%) because this NLP system incorrectly flagged structured fields from the screening tool.
conclusionUnstandardized terminology and poor integration of HRSN screeners in EHR remain important barriers to identifying older adults' food and housing insecurity using cTAKES.
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Registered trials
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