Abstract
Objective
To assess the utility of applying natural language processing (NLP) to electronic
health records (EHRs) to identify individuals with chronic mobility disability.
Design
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.
Setting
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.
Interventions
Not applicable.
Main Outcome Measures
Confirmation of patients’ chronic wheelchair use in NLP-flagged notes; quality of
disability documentation.
Results
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.
Conclusions
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.
Keywords
List of abbreviations:
EHR (electronic health record), ICD-9-CM (International Classification of Diseases–9th Revision–Clinical Modifications), ICD-10-CM (International Classification of Diseases–10th Revision–Clinical Modifications), NLP (natural language processing)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: May 20, 2020
Footnotes
Supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development, R21 HD095240-01.
Disclosures: none.
Identification
Copyright
© 2020 by the American Congress of Rehabilitation Medicine
