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Human and Automated Coding of Rehabilitation Discharge Summaries According to the International Classification of Functioning, Disability, and Health

^a Department of Biomedical Informatics, Columbia University, New York, NY
^b Mailman School of Public Health, Columbia University, New York, NY
^c Department of Occupational Therapy, Columbia University Medical Center, New York, NY

^_* Correspondence and reprints: Rita Kukafka, DRPH, MA, Department of Biomedical Informatics, Vanderbilt Clinic, 5^th Floor, 622 West 168th Street, New York, NY 10032. (Email: rita.kukafka{at}dbmi.columbia.edu).

Received for publication: 03/20/06; accepted for publication: 06/12/06.

Objective: The International Classification of Functioning, Disability, and Health (ICF) is designed to provide a common language and framework for describing health and health-related states. The goal of this research was to investigate human and automated coding of functional status information using the ICF framework.

Design: The authors extended an existing natural language processing (NLP) system to encode rehabilitation discharge summaries according to the ICF.

Measurements: The authors conducted a formal evaluation, comparing the coding performed by expert coders, non-expert coders, and the NLP system.

Results: Automated coding can be used to assign codes using the ICF, with results similar to those obtained by human coders, at least for the selection of ICF code and assignment of the performance qualifier. Coders achieved high agreement on ICF code assignment.

Conclusion: This research is a key next step in the development of the ICF as a sensitive and universal classification of functional status information. It is worthwhile to continue to investigate automated ICF coding.

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