In response to: Method of electronic health record documentation and quality of primary care

@article{Handler2012InRT,
  title={In response to: Method of electronic health record documentation and quality of primary care},
  author={Jonathan A. Handler and James G. Adams},
  journal={Journal of the American Medical Informatics Association : JAMIA},
  year={2012},
  volume={19 6},
  pages={
          1120-1
        }
}
  • J. Handler, James G. Adams
  • Published 1 November 2012
  • Medicine
  • Journal of the American Medical Informatics Association : JAMIA
We read with interest the article by Linder, Schnipper, and Middleton comparing dictation, free-text typing, and structured data entry to quality outcomes.1 The authors conclude that using dictation appeared to provide a lower quality of care, but that conclusion seems unsupported by the reported results. Most importantly, the authors themselves note that most of the differences found were not actually quality of care or clinical outcome measures. The authors write that ‘even if documentation… 
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Method of electronic health record documentation and quality of primary care
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EHR-assessed quality is necessarily documentation-dependent, but physicians who dictated their notes appeared to have worse quality of care than physicians who used structured EHR documentation.
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In concept extraction, it is demonstrated that switching models, one of which is especially designed for telegraphic sentences, improved extraction of the treatment concept significantly and a set of features derived from a rule-based classifier were proven to be effective for the classes such as conditional and possible.
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Several variables influencing the accuracy of ICD-9 codes to identify cancer patients including: the identification algorithm, kind of cancer to be identified, presence of other conditions similar to cancer, and presence of conditions that are precancerous are identified.
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The authors describe the design and performance of three state-of-the-art text-mining applications from the National Research Council of Canada on evaluations within the 2010 i2b2 challenge, finding that the introduction of a wide range of features was crucial to success.
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The cTAKES annotations are the foundation for methods and modules for higher-level semantic processing of clinical free-text, and its components, specifically trained for the clinical domain, create rich linguistic and semantic annotations.
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TLDR
The experimental results showed that ensemble classifiers that combine individual classifiers into a voting system could achieve better performance than a single classifier in recognizing medication information from clinical text, suggesting that simple strategies that can be easily implemented such as majority voting could have the potential to significantly improve clinical entity recognition.
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