Development of a clinician reputation metric to identify appropriate problem-medication pairs in a crowdsourced knowledge base.
J Biomed Inform
; 48: 66-72, 2014 Apr.
Article
em En
| MEDLINE
| ID: mdl-24321170
ABSTRACT
BACKGROUND:
Correlation of data within electronic health records is necessary for implementation of various clinical decision support functions, including patient summarization. A key type of correlation is linking medications to clinical problems; while some databases of problem-medication links are available, they are not robust and depend on problems and medications being encoded in particular terminologies. Crowdsourcing represents one approach to generating robust knowledge bases across a variety of terminologies, but more sophisticated approaches are necessary to improve accuracy and reduce manual data review requirements.OBJECTIVE:
We sought to develop and evaluate a clinician reputation metric to facilitate the identification of appropriate problem-medication pairs through crowdsourcing without requiring extensive manual review.APPROACH:
We retrieved medications from our clinical data warehouse that had been prescribed and manually linked to one or more problems by clinicians during e-prescribing between June 1, 2010 and May 31, 2011. We identified measures likely to be associated with the percentage of accurate problem-medication links made by clinicians. Using logistic regression, we created a metric for identifying clinicians who had made greater than or equal to 95% appropriate links. We evaluated the accuracy of the approach by comparing links made by those physicians identified as having appropriate links to a previously manually validated subset of problem-medication pairs.RESULTS:
Of 867 clinicians who asserted a total of 237,748 problem-medication links during the study period, 125 had a reputation metric that predicted the percentage of appropriate links greater than or equal to 95%. These clinicians asserted a total of 2464 linked problem-medication pairs (983 distinct pairs). Compared to a previously validated set of problem-medication pairs, the reputation metric achieved a specificity of 99.5% and marginally improved the sensitivity of previously described knowledge bases.CONCLUSION:
A reputation metric may be a valuable measure for identifying high quality clinician-entered, crowdsourced data.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Informática Médica
/
Sistemas Computadorizados de Registros Médicos
/
Bases de Conhecimento
/
Registros Eletrônicos de Saúde
Tipo de estudo:
Guideline
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2014
Tipo de documento:
Article