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

Tópicos

{ record(1888) medic(1808) patient(1693) }
{ decis(3086) make(1611) patient(1517) }
{ perform(999) metric(946) measur(919) }
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{ framework(1458) process(801) describ(734) }
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{ data(3963) clinic(1234) research(1004) }
{ ehr(2073) health(1662) electron(1139) }
{ age(1611) year(1155) adult(843) }
{ result(1111) use(1088) new(759) }
{ patient(2315) diseas(1263) diabet(1191) }
{ problem(2511) optim(1539) algorithm(950) }
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Resumo

CKGROUND: 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.

Resumo Limpo

ckground correl data within electron health record necessari implement various clinic decis support function includ patient summar key type correl link medic clinic problem databas problemmed link avail robust depend problem medic encod particular terminolog crowdsourc repres one approach generat robust knowledg base across varieti terminolog sophist approach necessari improv accuraci reduc manual data review requirementsobject sought develop evalu clinician reput metric facilit identif appropri problemmed pair crowdsourc without requir extens manual reviewapproach retriev medic clinic data warehous prescrib manual link one problem clinician eprescrib june may identifi measur like associ percentag accur problemmed link made clinician use logist regress creat metric identifi clinician made greater equal appropri link evalu accuraci approach compar link made physician identifi appropri link previous manual valid subset problemmed pairsresult clinician assert total problemmed link studi period reput metric predict percentag appropri link greater equal clinician assert total link problemmed pair distinct pair compar previous valid set problemmed pair reput metric achiev specif margin improv sensit previous describ knowledg basesconclus reput metric may valuabl measur identifi high qualiti clinicianent crowdsourc data

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