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Challenges of Developing a Natural Language Processing Method With Electronic Health Records to Identify Persons With Chronic Mobility Disability.

  • Agaronnik, Nicole D1
  • Lindvall, Charlotta2
  • El-Jawahri, Areej3
  • He, Wei4
  • Iezzoni, Lisa I5
  • 1 Health Policy Research Center, Mongan Institute, Massachusetts General Hospital, Boston, MA.
  • 2 Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, MA; Division of Palliative Medicine, Brigham and Women's Hospital, Boston, MA.
  • 3 Department of Medicine, Harvard Medical School, Boston, MA; Division of Hematology and Oncology, Massachusetts General Hospital, Boston, MA.
  • 4 Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA.
  • 5 Health Policy Research Center, Mongan Institute, Massachusetts General Hospital, Boston, MA; Department of Medicine, Harvard Medical School, Boston, MA. Electronic address: [email protected]
Published Article
Archives of physical medicine and rehabilitation
Publication Date
Oct 01, 2020
DOI: 10.1016/j.apmr.2020.04.024
PMID: 32446905


To assess the utility of applying natural language processing (NLP) to electronic health records (EHRs) to identify individuals with chronic mobility disability. We 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. EHRs from large health care delivery system PARTICIPANTS: Patients (N=14,877) 21-75 years old who were newly diagnosed with colorectal cancer between 2005 and 2017. Not applicable. Confirmation of patients' chronic wheelchair use in NLP-flagged notes; quality of disability documentation. We 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. NLP 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. Copyright © 2020 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

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