| A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record. | |
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MedLine Citation:
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PMID: 21613643 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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Background Accurate knowledge of a patient's medical problems is critical for clinical decision making, quality measurement, research, billing and clinical decision support. Common structured sources of problem information include the patient problem list and billing data; however, these sources are often inaccurate or incomplete. Objective To develop and validate methods of automatically inferring patient problems from clinical and billing data, and to provide a knowledge base for inferring problems. Study design and methods We identified 17 target conditions and designed and validated a set of rules for identifying patient problems based on medications, laboratory results, billing codes, and vital signs. A panel of physicians provided input on a preliminary set of rules. Based on this input, we tested candidate rules on a sample of 100 000 patient records to assess their performance compared to gold standard manual chart review. The physician panel selected a final rule for each condition, which was validated on an independent sample of 100 000 records to assess its accuracy. Results Seventeen rules were developed for inferring patient problems. Analysis using a validation set of 100 000 randomly selected patients showed high sensitivity (range: 62.8-100.0%) and positive predictive value (range: 79.8-99.6%) for most rules. Overall, the inference rules performed better than using either the problem list or billing data alone. Conclusion We developed and validated a set of rules for inferring patient problems. These rules have a variety of applications, including clinical decision support, care improvement, augmentation of the problem list, and identification of patients for research cohorts. |
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Authors:
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Adam Wright; Justine Pang; Joshua C Feblowitz; Francine L Maloney; Allison R Wilcox; Harley Z Ramelson; Louise I Schneider; David W Bates |
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Publication Detail:
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Type: JOURNAL ARTICLE Date: 2011-5-25 |
Journal Detail:
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Title: Journal of the American Medical Informatics Association : JAMIA Volume: - ISSN: 1527-974X ISO Abbreviation: - Publication Date: 2011 May |
Date Detail:
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Created Date: 2011-5-26 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9430800 Medline TA: J Am Med Inform Assoc Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
Affiliation:
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Department of General Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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