Marfeo Elizabeth, Sacco Maryanne, Maldonado Jona Camacho, Coale Kathleen, Silva Rafael Jimenez, Parks Rebecca, Rasch Elizabeth K
Department of Community Health, Tufts University, Medford, MA, USA; National Institutes of Health Rehabilitation Medicine Department, Bethesda, MD, USA.
National Institutes of Health Rehabilitation Medicine Department, Bethesda, MD, USA.
Disabil Health J. 2025 May 24:101888. doi: 10.1016/j.dhjo.2025.101888.
Clinical 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.
We 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.
Apply 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.
Conceptual 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.
Overall, 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.
临床记录通常会提供有关个人功能（活动）的信息，反映其健康的生活体验。使用临床自然语言处理（cNLP）进行自动提取可以帮助医疗服务提供者进行临床决策、治疗规划、预测健康结果以及为医疗保健政策提供信息。
我们旨在（1）描述世界卫生组织的《国际功能、残疾和健康分类》（ICF）在cNLP工具开发中的适用性，（2）确定ICF应用中的关键挑战，以及（3）提供改进这一过程的建议。
将ICF作为框架，使用针对ICF每个活动领域的cNLP工具，对手动注释的来自美国社会保障管理局（SSA）和美国国立卫生研究院（NIH）临床中心的自由文本电子健康记录（EHR）进行注释。
在四个主要领域中遇到了概念和内容问题：移动性、自我护理和家庭生活、人际互动和关系以及沟通和认知。随后提供了关于更新ICF的建议。
总体而言，ICF应用于一个它最初并未开发的用例（SSA残疾判定）时表现良好，该用例评估了其有效性，并突出了ICF概念化与记录的现实世界功能观察之间的优势和不足。这项工作为改进ICF并将其与cNLP模型集成奠定了基础，以便为临床医生、研究人员和政策制定者提供强大的信息学工具，能够快速识别功能信息以用于临床决策和政策制定目的。
