ArticleJMIR medical informatics2014
Use of expert relevancy ratings to validate task-specific search strategies for electronic medical records.
Article in JMIR medical informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As electronic medical records (EMRs) grow in size and complexity, there is increasing need for automated EMR tools that highlight the medical record items most germane to a practitioner's task-specific needs. The development of such tools would be aided by gold standards of information relevance for a series of different clinical scenarios. We have previously proposed a process in which exemplar medical record data are extracted from actual patients' EMRs, anonymized, and presented to clinical experts, who then score each medical record item for its relevance to a specific clinical scenario. In this paper, we present how that body of expert relevancy data can be used to create a test framework to validate new EMR search strategies.
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