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Documentation in Long-Term and Home Healthcare Setting01:29

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Documentation in long-term care facilities and home healthcare settings is crucial for ensuring continuous, coordinated, and comprehensive care for patients. Each setting has its specific documentation processes and tools:
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Updated: Apr 11, 2026

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
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Combining Token Classification With Large Language Model Revision for Age-Friendly 4M Entity Recognition From Nursing

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    Summary
    This summary is machine-generated.

    This study developed a 4M Entity Recognition (4M-ER) pipeline to extract crucial patient information from nursing home text messages (TMs). The pipeline accurately captures What Matters, Medication, Mentation, and Mobility data, improving care quality reporting.

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    Area of Science:

    • Natural Language Processing in Healthcare
    • Clinical Informatics
    • Long-Term Care Technology

    Background:

    • Nursing home text messages (TMs) contain valuable clinical data aligned with the Age-Friendly Health Systems 4Ms (What Matters, Medication, Mentation, Mobility).
    • Current methods fail to capture this unstructured information for systematic monitoring or quality reporting.
    • Automated extraction is challenging due to informal language, abbreviations, and fragmented syntax in TMs.

    Purpose of the Study:

    • To develop and evaluate a multi-stage 4M Entity Recognition (4M-ER) pipeline for improved 4M information extraction from clinical TMs.
    • To utilize only locally deployed, open-source models, combining a fine-tuned token classifier with large language model (LLM) revision.
    • To enhance the accuracy and efficiency of extracting 4M data from nursing home communications.

    Main Methods:

    • A 4M-ER pipeline was created using an expert-annotated dataset of 1,169 TMs from 16 nursing homes.
    • The pipeline employed a fine-tuned Bio-ClinicalBERT token classifier for initial candidate span identification.
    • LLM revision (Gemma, Phi, Qwen, Mistral) refined spans, guided by semantic similarity retrieval, with performance compared against baselines and ablation studies.

    Main Results:

    • The 4M-ER pipeline achieved superior performance across all 4M domains compared to prior fine-tuned LLMs, with F1 improvements of +2 to +11 percentage points.
    • It demonstrated enhanced accuracy over single-stage Bio-ClinicalBERT for Mobility, Mentation, and What Matters.
    • LLM revision reduced false positives by 25-35%, while Bio-ClinicalBERT's recall captured subtle entities; silver data augmentation further boosted performance in challenging domains.

    Conclusions:

    • The 4M-ER pipeline offers an accurate and scalable method for extracting 4M entities from clinical TMs using open-source models.
    • The structured 4M data facilitates taxonomy/ontology development and supports downstream applications like clinical surveillance and predictive modeling.
    • This approach provides a foundation for compliance with age-friendly quality measures and enhances long-term care quality improvement.