| Content and structure of clinical problem lists: a corpus analysis. | |
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MedLine Citation:
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PMID: 18999284 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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In the interest of designing an automated high-level, longitudinal clinical summary of a patient record, we analyze traditional ways in which medical problems pertaining to the patient are summarized in the electronic health record. The patient problem list has become a commonly used proxy for a summary of patient history and automated methods have been proposed to generate it. However, little research has been conducted on how to structure the problem list in a manner most effective for supporting clinical care. This study analyzes the structure and content of the Past Medical History (PMH) sections of a large corpus of clinical notes, as a proxy for problem lists. Findings show that when listing patients history, physicians convey several semantic types of information, not only problems. Furthermore, they often group related concepts in a single line of the PMH. In contrast, traditional problem lists allow only a simple enumeration of coded terms. Content analysis goes on to reiterate the value of more complex representations as well as provide valuable data and guidelines for automated generation of a clinical summary. |
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Authors:
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Tielman T Van Vleck; Adam Wilcox; Peter D Stetson; Stephen B Johnson; No??mie Elhadad |
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Publication Detail:
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Type: Journal Article; Research Support, N.I.H., Extramural Date: 2008-11-06 |
Journal Detail:
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Title: AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium Volume: - ISSN: 1942-597X ISO Abbreviation: - Publication Date: 2008 |
Date Detail:
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Created Date: 2008-11-12 Completed Date: 2010-01-08 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101209213 Medline TA: AMIA Annu Symp Proc Country: United States |
Other Details:
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Languages: eng Pagination: 753-7 Citation Subset: IM |
Affiliation:
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Department of Biomedical Informatics, Columbia University, New York, NY, USA. |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms Artificial Intelligence Clinical Protocols Information Storage and Retrieval / methods* Medical History Taking / methods* Medical Records Systems, Computerized / statistics & numerical data* Medical Records, Problem-Oriented / statistics & numerical data* Natural Language Processing* New York Pattern Recognition, Automated / methods* Subject Headings |
| Grant Support | |
ID/Acronym/Agency:
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K22-LM008805/LM/NLM NIH HHS; N01-LM07079/LM/NLM NIH HHS; R01 LM007659/LM/NLM NIH HHS; R01 LM008635/LM/NLM NIH HHS |
| Comments/Corrections | |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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