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1.
AMIA Annu Symp Proc ; : 1131, 2008 Nov 06.
Artigo em Inglês | MEDLINE | ID: mdl-18999233

RESUMO

Test non-completion decreases quality of care and accounts for many diagnosis-related malpractice claims. Currently, clinicians using Partners' electronic Longitudinal Medical Record (LMR) can track results but lack a mechanism for tracking non-completed tests. This pilot intervention will study an "order tracking" functionality that flags newly-ordered tests and will lead to generation of written patient reminders if tests are not completed within pre-specified timeframes. If test completion rates improve, we will pursue development of a dedicated LMR application.


Assuntos
Assistência Ambulatorial/métodos , Sistemas de Informação em Laboratório Clínico , Controle de Formulários e Registros , Sistemas de Registro de Ordens Médicas , Registro Médico Coordenado , Sistemas Computadorizados de Registros Médicos , Medical Subject Headings , Massachusetts
2.
AMIA Annu Symp Proc ; : 732-6, 2008 Nov 06.
Artigo em Inglês | MEDLINE | ID: mdl-18998827

RESUMO

Medication non-adherence is common and the physicians awareness of it may be an important factor in clinical decision making. Few sources of data on physician awareness of medication non-adherence are available. We have designed an algorithm to identify documentation of medication non-adherence in the text of physician notes. The algorithm recognizes eight semantic classes of documentation of medication non-adherence. We evaluated the algorithm against manual ratings of 200 randomly selected notes of hypertensive patients. The algorithm detected 89% of the notes with documented medication non-adherence with specificity of 84.7% and positive predictive value of 80.2%. In a larger dataset of 1,000 documents, notes that documented medication non-adherence were more likely to report significantly elevated systolic (15.3% vs. 9.0%; p = 0.002) and diastolic (4.1% vs. 1.9%; p = 0.03) blood pressure. This novel clinically validated tool expands the range of information on medication non-adherence available to researchers.


Assuntos
Armazenamento e Recuperação da Informação/métodos , Anamnese/estatística & dados numéricos , Sistemas Computadorizados de Registros Médicos/estatística & dados numéricos , Processamento de Linguagem Natural , Cooperação do Paciente/estatística & dados numéricos , Reconhecimento Automatizado de Padrão/métodos , Descritores , Algoritmos , Anti-Hipertensivos/administração & dosagem , Inteligência Artificial , Hipertensão/tratamento farmacológico , Hipertensão/epidemiologia , Massachusetts
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