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

  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},
  volume={19 6},
  • 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… 
Deploying Cloud Computing to Implement Electronic Health Record in Indian Healthcare Settings
The results of this study suggest that the use of cloud computing for the EHR is likely to emerge in the future, however, given the current ICT infrastructure and healthcare system of India, several concerns were raised.


Method of electronic health record documentation and quality of primary care
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.
Viewpoint Paper: Identifying Patient Smoking Status from Medical Discharge Records
A Natural Language Processing (NLP) challenge on automatically determining the smoking status of patients from information found in their discharge records and analysis of the results highlighted the fact that discharge summaries express smoking status using a limited number of textual features.
Realizing the full potential of electronic health records: the role of natural language processing
This issue of the journal displays several solutions to this problem that are based on natural language processing (NLP) techniques, and the need to steer current NLP research efforts so that new developments can be accelerated and research products can become readily usable in healthcare applications.
Extracting medication information from clinical text
Although rule-based systems dominated the top 10, the best performing system was a hybrid and durations and reasons were the most difficult for all systems to detect.
Feature engineering combined with machine learning and rule-based methods for structured information extraction from narrative clinical discharge summaries
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.
Comparing methods for identifying pancreatic cancer patients using electronic data sources.
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.
Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010
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.
Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications
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.
Recognition of medication information from discharge summaries using ensembles of classifiers
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.