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First published June 23, 2006 as JAMIA PrePrint; doi:10.1197/jamia.M2107
Journal of the American Medical Informatics Association 2006;13(5):508-515
© 2006 American Medical Informatics Association

A more recent version of this article appeared on September 1, 2006
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Submitted on March 2, 2006
Accepted on June 12, 2006

Human and automated coding of rehabilitation discharge summaries according to the International Classification of Functioning, Disability, and Health

Rita Kukafka DrPH, MA1*, Michael E. Bales MPH2, Ann Burkhardt OTD, OTR/L, BCN3, and Carol Friedman PhD4

Affiliation of the authors: 1 Department of Biomedical Informatics, Columbia University in New York, NY, USA; Mailman School of Public Health, Columbia University in New York, NY, USA ; 2 Department of Biomedical Informatics, Columbia University in New York, NY, USA ; 3 Island University, Brooklyn Campus, Brooklyn, NY, USA (currently); Department of Occupational Therapy, Columbia University Medical Center, New York, NY, USA (at time of research) ; 4 Mailman School of Public Health, Columbia University in New York, NY, USA

* To whom correspondence should be addressed.

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 We extended an existing natural language processing (NLP) system to encode rehabilitation discharge summaries according to the ICF.

Measurements We 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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