TranSenda and Harvard Clinical Research Institute Announce Office-Smart Deployment
News Jun 22, 2009
TranSenda International, LLC, announced the deployment of a clinical trial software solution for Harvard Clinical Research Institute (HCRI) that expands the products and services already under contract between the two parties.
HCRI was seeking to continue the process of simplifying its existing systems to manage and monitor various spreadsheets, information and documents related to their clinical trials. To accomplish this, HCRI has migrated from initially using TranSenda’s Clinical Trial Manager product to Office-Smart Clinical Trial Manager™ and Office-Smart Clinical Payment Manager™.
This subsequent deployment also included the purchase of ClinBUS Connector™ along with professional services to consolidate existing data sets into a single repository for clinical trial management and related operational data.
By adding Office-Smart solutions to its organization, HCRI can now take advantage of advanced capabilities that allow integration between Clinical Trial Manager and the Microsoft® Office System. Office-Smart solutions also use TranSenda’s innovative ClinBUS® data interchange technology to make data available through Microsoft SharePoint Server.
Among other advantages, ClinBUS enables the use of SharePoint data grids to connect Microsoft Excel spreadsheets and databases, including Microsoft Access, to Office-Smart solutions for data import or integration. With this new level of connectivity, HCRI can now use data for the creation of SharePoint websites and dashboards; and to make better use of their existing Microsoft Office tools, including Excel, Access, Outlook and Word.
In addition to consolidating Microsoft Excel spreadsheet data, HCRI will also be integrating Office-Smart Clinical Trial Manager with a number of Clinical Trial Data Management Systems, including Electronic Data Capture (EDC) system(s) as well as their web-based learning management system.
MIT researchers have developed a cryptographic system that could help neural networks identify promising drug candidates in massive pharmacological datasets, while keeping the data private. Secure computation done at such a massive scale could enable broad pooling of sensitive pharmacological data for predictive drug discovery.
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