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1.
J Biomed Inform ; 40(2): 174-82, 2007 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-16901760

RESUMO

Methods for surveillance of adverse events (AEs) in clinical settings are limited by cost, technology, and appropriate data availability. In this study, two methods for semi-automated review of text records within the Veterans Administration database are utilized to identify AEs related to the placement of central venous catheters (CVCs): a Natural Language Processing program and a phrase-matching algorithm. A sample of manually reviewed records were then compared to the results of both methods to assess sensitivity and specificity. The phrase-matching algorithm was found to be a sensitive but relatively non-specific method, whereas a natural language processing system was significantly more specific but less sensitive. Positive predictive values for each method estimated the CVC-associated AE rate at this institution to be 6.4 and 6.2%, respectively. Using both methods together results in acceptable sensitivity and specificity (72.0 and 80.1%, respectively). All methods including manual chart review are limited by incomplete or inaccurate clinician documentation. A secondary finding was related to the completeness of administrative data (ICD-9 and CPT codes) used to identify intensive care unit patients in whom a CVC was placed. Administrative data identified less than 11% of patients who had a CVC placed. This suggests that other methods, including automated methods such as phrase matching, may be more sensitive than administrative data in identifying patients with devices. Considerable potential exists for the use of such methods for the identification of patients at risk, AE surveillance, and prevention of AEs through decision support technologies.


Assuntos
Cateterismo Venoso Central/efeitos adversos , Sistemas de Gerenciamento de Base de Dados , Armazenamento e Recuperação da Informação/métodos , Erros Médicos , Sistemas Computadorizados de Registros Médicos , Processamento de Linguagem Natural , Reconhecimento Automatizado de Padrão/métodos , Inteligência Artificial , Humanos
2.
Stud Health Technol Inform ; 107(Pt 1): 540-4, 2004.
Artigo em Inglês | MEDLINE | ID: mdl-15360871

RESUMO

Veterans Health Administration (VHA) is now evaluating use of SNOMED-CT. This paper reports the first phase of this evaluation, which examines the coverage of SNOMED-CT for problem list entries. Clinician expressions in VA problem lists are quite diverse compared to the content of the current VA terminology Lexicon. We selected a random set of 5054 narratives that were previously "unresolved" against the Lexicon. These narratives were mapped to SNOMED-CT using two automated tools. Experts reviewed a subset of the tools' matched, partly matched, and un-matched narratives. The automated tools produced exact or partial matches for over 90% of the 5054 unresolved narratives. SNOMED-CT has promise as a coding system for clinical problems. In subsequent studies, VA will examine the coverage of SNOMED for other clinical domains, such as drugs, allergies, and physician orders.


Assuntos
Sistemas Computadorizados de Registros Médicos/classificação , Systematized Nomenclature of Medicine , United States Department of Veterans Affairs , Controle de Formulários e Registros , Humanos , Sistemas Computadorizados de Registros Médicos/normas , Registros Médicos Orientados a Problemas , Estados Unidos , Vocabulário Controlado
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