Development of a clinician reputation metric to identify appropriate problem-medication pairs in a crowdsourced knowledge base

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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. © 2013 Elsevier Inc.

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APA

McCoy, A. B., Wright, A., Rogith, D., Fathiamini, S., Ottenbacher, A. J., & Sittig, D. F. (2014). Development of a clinician reputation metric to identify appropriate problem-medication pairs in a crowdsourced knowledge base. Journal of Biomedical Informatics, 48, 66–72. https://doi.org/10.1016/j.jbi.2013.11.010

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