ArticleArchives of physical medicine and rehabilitation2020
Challenges of Developing a Natural Language Processing Method With Electronic Health Records to Identify Persons With Chronic Mobility Disability.
Article in Archives of physical medicine and rehabilitation, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
What it found
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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
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 40 citations in OpenAlex.
- Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.Perspectives in health information management · 2024Pooled it
- Natural Language Processing to Identify Advance Care Planning Documentation in a Multisite Pragmatic Clinical Trial.Journal of pain and symptom management · 2022Trial
- The promise and peril of AI for disability health equity: A justice-centered narrative review.PLOS digital health · 2026Review
- Review
- Development and Validation of a Natural Language Processing Algorithm Using Electronic Health Record Data to Identify Breast Cancer Patients With Low Social Support.JCO oncology advances · 2025Article
- Applying NLP methods to code functional performance in electronic health records using the international classification of functioning, disability, and health.Disability and health journal · 2025Article
- Artificial Intelligence and Its Revolutionary Role in Physical and Mental Rehabilitation: A Review of Recent Advancements.BioMed research international · 2024Review
- Delirium and Healthcare Utilization in Patients Undergoing Hematopoietic Stem Cell Transplantation.Transplantation and cellular therapy · 2023Article
- The Use of Natural Language Processing to Assess Social Support in Patients With Advanced Cancer.The oncologist · 2023Article
- Gross motor function prediction using natural language processing in cerebral palsy.Developmental medicine and child neurology · 2023Article
- A roadmap to reduce information inequities in disability with digital health and natural language processing.PLOS digital health · 2022Review
- Clinical Informatics Approaches to Understand and Address Cancer Disparities.Yearbook of medical informatics · 2022Review
- Assessment of Electronic Health Record for Cancer Research and Patient Care Through a Scoping Review of Cancer Natural Language Processing.JCO clinical cancer informatics · 2022Article
- Implications of Physical Access Barriers for Breast Cancer Diagnosis and Treatment in Women with Mobility Disability.Journal of disability policy studies · 2022Article
- Information Extraction Framework for Disability Determination Using a Mental Functioning Use-Case.JMIR medical informatics · 2022Article
- Linking Free Text Documentation of Functioning and Disability to the ICF With Natural Language Processing.Frontiers in rehabilitation sciences · 2021Article
- Perspectives of Patients with Pre-existing Mobility Disability on the Process of Diagnosing Their Cancer.Journal of general internal medicine · 2021Article
- Exploring attitudes about developing cancer among patients with pre-existing mobility disability.Psycho-oncology · 2021Article
- Automated Coding of Under-Studied Medical Concept Domains: Linking Physical Activity Reports to the International Classification of Functioning, Disability, and Health.Frontiers in digital health · 2021Article
- Exploring the Process of Cancer Care for Patients With Pre-Existing Mobility Disability.JCO oncology practice · 2021Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveTo assess the utility of applying natural language processing (NLP) to electronic health records (EHRs) to identify individuals with chronic mobility disability.
designWe used EHRs from the Research Patient Data Repository, which contains EHRs from a large Massachusetts health care delivery system. This analysis was part of a larger study assessing the effects of disability on diagnosis of colorectal cancer. We applied NLP text extraction software to longitudinal EHRs of colorectal cancer patients to identify persons who use a wheelchair (our indicator of mobility disability for this analysis). We manually reviewed the clinical notes identified by NLP using directed content analysis to identify true cases using wheelchairs, duration or chronicity of use, and documentation quality.
settingEHRs from large health care delivery system
participantsPatients (N=14,877) 21-75 years old who were newly diagnosed with colorectal cancer between 2005 and 2017.
interventionsNot applicable.
main outcome measuresConfirmation of patients' chronic wheelchair use in NLP-flagged notes; quality of disability documentation.
resultsWe identified 14,877 patients with colorectal cancer with 303,182 associated clinical notes. NLP screening identified 1482 (0.5%) notes that contained 1+ wheelchair-associated keyword. These notes were associated with 420 patients (2.8% of colorectal cancer population). Of the 1482 notes, 286 (19.3%, representing 105 patients, 0.7% of the total) contained documentation of reason for wheelchair use and duration. Directed content analysis identified 3 themes concerning disability documentation: (1) wheelchair keywords used in specific EHR contexts; (2) reason for wheelchair not clearly stated; and (3) duration of wheelchair use not consistently documented.
conclusionsNLP offers an option to screen for patients with chronic mobility disability in much less time than required by manual chart review. Nonetheless, manual chart review must confirm that flagged patients have chronic mobility disability (are not false positives). Notes, however, often have inadequate disability documentation.
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Registered trials
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.