Natural language processing of asthma discharge summaries for the monitoring of patient care.
A technique for monitoring healthcare via the processing of routinely collected narrative documentation is presented. A checklist of important details of asthma management in use in the Glasgow Royal Infirmary (GRI) was translated into SQL queries and applied to a database of 59 GRI discharge summaries analyzed by the New York University Linguistic String Project medical language processor. Tables of retrieved information obtained for each query were compared with the text of the original documents by physician reviewers. Categories (unit = document) were: (1) information present, retrieved correctly; (2) information not present; (3) information present, retrieved with minor or major error; (4) information present, retrieved with minor or major omissions. Category 2 (physician "documentation score") could be used to prioritize manual review and guide feedback to physicians to improve documentation. The semantic structuring and relative completeness of retrieved data suggest their potential use as input to further quality assurance procedures.
Sager
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N
Courant Institute of Mathematical Sciences, New York University, NY 10012.
Lyman
M
M
Tick
L J
LJ
Nhàn
N T
NT
Bucknall
C E
CE
D001249:Asthma / Q000628:therapy*; D003628:Database Management Systems; D006801:Humans; D016247:Information Storage and Retrieval; D008485:Medical Audit / Q000379:methods*; D008499:Medical Records; D016347:Medical Records Systems, Computerized; D009323:Natural Language Processing; D010351:Patient Discharge