Evidence map›Paper›PMID 40442018›Full record

ArticleDisability and health journal2025

Applying NLP methods to code functional performance in electronic health records using the international classification of functioning, disability, and health.

Elizabeth Marfeo, Maryanne Sacco, Jona Camacho Maldonado, Kathleen Coale, Rafael Jimenez Silva, Rebecca Parks, Elizabeth K Rasch

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Article in Disability and health journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
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1 · What the graph read from 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.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Elizabeth MarfeoDepartment of Community Health, Tufts University, Medford, MA, USA; National Institutes of Health Rehabilitation Medicine Department, Bethesda, MD, USA. Electronic address: elizabeth.marfeo@tufts.edu.
Maryanne SaccoNational Institutes of Health Rehabilitation Medicine Department, Bethesda, MD, USA.
Jona Camacho MaldonadoNational Institutes of Health Rehabilitation Medicine Department, Bethesda, MD, USA.
Kathleen CoaleNational Institutes of Health Rehabilitation Medicine Department, Bethesda, MD, USA.
Rafael Jimenez SilvaNational Institutes of Health Rehabilitation Medicine Department, Bethesda, MD, USA.
Rebecca ParksNational Institutes of Health Rehabilitation Medicine Department, Bethesda, MD, USA.
Elizabeth K RaschNational Institutes of Health Rehabilitation Medicine Department, Bethesda, MD, USA.

Funding

Analyzing the SSA Disability Evaluation ProcessZIACL060065 · CLC · CLINICAL CENTER · PI CHAN, LEIGHTON · 2009 to 2025
$0k
Intramural NIH HHS ZIA CL060065
6 · The paper itself

Abstract

backgroundClinical records often provide information on a person's functioning (activities), reflecting their lived experience of health. Automated extraction using clinical natural language processing (cNLP) can assist providers with clinical decision-making, treatment planning, predicting health outcomes, and informing health care policy.

objectiveWe aim to (1) describe the applicability of the World Health Organization's International Classification of Functioning, Disability and Health (ICF) to development of cNLP tools, (2) identify key challenges in application of the ICF, and (3) offer recommendations to improve this process.

methodsApply the ICF as a framework to manually annotate free-text electronic health records (EHRs) from the United States (US) Social Security Administration (SSA) and the National Institutes of Health (NIH) Clinical Center using cNLP tools for each activity domain of the ICF.

resultsConceptual and content issues were encountered within four primary domains: Mobility, Self-Care and Domestic Life, Interpersonal Interactions and Relationships, and Communication and Cognition. Subsequent recommendations for ICF updates were provided.

conclusionOverall, the ICF performed well applied to a use case for which it was not originally developed (SSA disability determination), which assessed its effectiveness, and highlighted both strengths and weaknesses between ICF conceptualizations and documented real-world functioning observations. This work provides a foundation upon which to improve the ICF and integrate it with cNLP models in order to give clinicians, researchers, and policy makers robust informatics tools that quickly identify functioning information for clinical decision and policy making purposes.

Indexed as

Activities of Daily LivingDisability EvaluationElectronic Health RecordsInternational Classification of Functioning, Disability and HealthNatural Language ProcessingPersons with DisabilitiesHumansNational Institutes of Health (U.S.)Self CareUnited StatesWorld Health OrganizationDisability assessmentElectronic health recordsNatural language processing

Identifiers

PMID40442018
PMCPMC12272488

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.