ºÝºÝߣshows by User: CKod / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: CKod / Tue, 18 Sep 2012 13:33:18 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: CKod E clinical solutions irug 2012 12sep2012 /slideshow/e-clinical-solutions-irug-2012-12sep2012/14335401 eclinicalsolutionsirug201212sep2012-120918133320-phpapp01
This presentation will provide an overview on how reconciliation and/or validation rules can be defined and trial data can be checked against these rules. By utilizing JReview’s built-in browser and advanced functionalities, objects can be defined with drill-down capabilities to perform these data validation checks. For e.g. reconciliation checks between EDC data and external lab data can be easily performed by reviewing a summary object with discrepant information such as subject id, discrepancy category and discrepancy message with an ability to drill down to detailed discrepant data listings. This approach should support pro-active data management for ongoing trials increasing the overall data quality. Similar approach can be applied to review data against sponsor defined data standards checks.]]>
This presentation will provide an overview on how reconciliation and/or validation rules can be defined and trial data can be checked against these rules. By utilizing JReview’s built-in browser and advanced functionalities, objects can be defined with drill-down capabilities to perform these data validation checks. For e.g. reconciliation checks between EDC data and external lab data can be easily performed by reviewing a summary object with discrepant information such as subject id, discrepancy category and discrepancy message with an ability to drill down to detailed discrepant data listings. This approach should support pro-active data management for ongoing trials increasing the overall data quality. Similar approach can be applied to review data against sponsor defined data standards checks.]]> Tue, 18 Sep 2012 13:33:18 GMT /slideshow/e-clinical-solutions-irug-2012-12sep2012/14335401 CKod@slideshare.net(CKod) E clinical solutions irug 2012 12sep2012 CKod This presentation will provide an overview on how reconciliation and/or validation rules can be defined and trial data can be checked against these rules. By utilizing JReview’s built-in browser and advanced functionalities, objects can be defined with drill-down capabilities to perform these data validation checks. For e.g. reconciliation checks between EDC data and external lab data can be easily performed by reviewing a summary object with discrepant information such as subject id, discrepancy category and discrepancy message with an ability to drill down to detailed discrepant data listings. This approach should support pro-active data management for ongoing trials increasing the overall data quality. Similar approach can be applied to review data against sponsor defined data standards checks. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/eclinicalsolutionsirug201212sep2012-120918133320-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This presentation will provide an overview on how reconciliation and/or validation rules can be defined and trial data can be checked against these rules. By utilizing JReview’s built-in browser and advanced functionalities, objects can be defined with drill-down capabilities to perform these data validation checks. For e.g. reconciliation checks between EDC data and external lab data can be easily performed by reviewing a summary object with discrepant information such as subject id, discrepancy category and discrepancy message with an ability to drill down to detailed discrepant data listings. This approach should support pro-active data management for ongoing trials increasing the overall data quality. Similar approach can be applied to review data against sponsor defined data standards checks.
E clinical solutions irug 2012 12sep2012 from Chandi Kodthiwada
]]> 285 6 https://cdn.slidesharecdn.com/ss_thumbnails/eclinicalsolutionsirug201212sep2012-120918133320-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-CKod-48x48.jpg?cb=1527711138 Chandi is an experienced product leader and a strategic thinker, he has spent the first half of his career helping pharma & biotech with various data challenges primarily focusing on how to better harness the power of clinical data and unleash it to help simplify the complexity around clinical data for Pharma He is passionate about introducing innovative methods to consume information and derive insight out of what we already know to help drive the day to day decisions and where applicable, the future He is a Coffee aficionado - always up for a conversation when it comes to solving complex business problems which remotely have anything to do with harnessing data *... about.me/chandi