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WHO-Collaborat ing Centre in Calgary

WHO-Collaborat ing Centre in Calgary. Thursday May 14 th , 2015 in Calgary, Alberta, Canada Dr. Bedirhan Ustun from the WHO attended and presented Dr. Hude Quan with a ceremonial flag and plaque. WHO CC Members.

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WHO-Collaborat ing Centre in Calgary

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  1. WHO-Collaborating Centre in Calgary • Thursday May 14th, 2015 in Calgary, Alberta, Canada • Dr. BedirhanUstun from the WHO attended and presented Dr. Hude Quan with a ceremonial flag and plaque. • WHO CC Members

  2. 1. Development and maintenance of WHO classification, terminology and standards products.2. Continuous quality improvement of WHO classification, terminology and standards products, specifically in the areas of ICD 11 implementation, fields trials and training.3. Research and networking on WHO classification, terminology and standards products through conducting translational research, and initiating collaborative research networks. Term of Reference

  3. Vision: To develop novel methods for the analysis of big data and the improvement of data quality to enable their optimal use for health research, disease surveillance and healthcare system performance assessment.(Hude Quan at IMECCHI meeting 2015)

  4. 1.1 To develop and validate automated methods of detecting data errors based on clinical logic. 1.2 To utilize data mining and machine learning methods to feature and identify high and low quality data and create a data quality matrix/index/score.1.3 To create statistical modeling methods to refine findings from big data to account for potentially misclassified or missing cases. Theme 1: Data Quality Assessment.

  5. 2.1 To standardize data terminologies through linking commonly used classifications (e.g., Systematized Nomenclature of Medicine [SNOMED], Read clinical codes, ICD).2.2 To revise ICD classification and coding guidelines for capturing better data.2.3 To develop a computerized alert intervention program for the accuracy of data extraction and entry by medical chart coders in hospitals. Theme 2: Data Quality Improvement.

  6. 3.1 To develop automated detection methods (such as natural language processing) of clinical conditions (e.g. hypertension) from unstructured free text in EMR for statistical analysis. 3.2 To validate and optimize these methods through the linkage of coded health data, EMR, survey data, clinical registry, medication data and chart data.3.3 To develop surveillance and healthcare system performance assessment methods using linked coded data and EMR. Theme 3: Data Processing & Analytics

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