Morrisville, N.C (PRWEB) May 17, 2013
d-Wise Technologies Inc., a provider of technology solutions for life science and health care organizations, today launched REVEAL 2.0, an enterprise search platform built to search the growing amount of structured and unstructured data that typically resides inside a pharmaceutical company’s disparate systems and databases as well as outside the clinical environment.
Reveal, the only clinically aligned enterprise search product developed specifically for the data intensive, siloed pharmaceutical and medical device industry, is a web-based platform to access and integrate all clinical data across multiple sources and multiple formats into a single, unified environment for analysis by senior management, researchers, regulatory experts and biostatisticians. Search queries are returned rapidly in an intuitive GUI to enable real-time decision making in the areas most critical to clinical research, including regulatory compliance, validation of study endpoints for efficacy, and establishing patterns within therapeutic areas or patient populations.
“As regulatory requirements increase for drug safety, pharmaceutical companies must now search and access more data than ever before, ranging from structured data derived in controlled clinical trials to unstructured data in the real world on health outcomes,” explains Bud Whitmeyer, CEO of d-Wise. “Because the data pool in clinical research is expanding Reveal was built as a robust, vertical solution to simplify the pharmaceutical industry's ability to access critical information wherever it resides.”
The Reveal 2.0 platform includes the following new features and optimizations:
- Access to hard-to-search, controlled data sets in complex SAS, Oracle and other clinical data repository environments
- Integration with BI applications and other reporting tools
- Robust metadata search capabilities included in results
- Real time dashboard views of results in a user-friendly GUI
- Enhanced security integrated into Reveal’s index and document structures
- Compliance with 21 CFR Part 11
- Connectivity to real world data sources ( EMRs, HIEs, etc.)
- Ease of installation and multiple deployment options within a clinical enterprise
- Easy integration with Open Source tools (OpenCDISC Validator).
Webinar: Using the Reveal Clinical Search Platform with OpenCDISC to Validate SDTM and ADaM Data.
To avoid delays in the FDA’s review process and lost time to market, pharmaceutical and medical device companies are working now to ensure clinical data is correctly converted and validated to CDISC standards.
On May 22 at 1:00PM EDT, d-Wise is hosting a webinar to demonstrate one use case for enterprise search. In this use case Reveal is used to find and access clinical data, including the content of SAS datasets and then paired with OpenCDISC Validator to validate SDTM and AdaM data to CDISC standards.
To attend this free, one-hour webinar, click here to register https://attendee.gotowebinar.com/register/5664543518584775424
Webinar ID: 134-843-915
d-Wise Technologies, Inc. is a technology leader with the expertise to empower world-class life science and healthcare organizations to resolve their business optimization challenges, helping them rapidly harness and leverage data, systems and processes to gain competitive advantage.
d-Wise’s ten year history of tailoring solutions to meet individual client needs as well as delivering data integration, data warehousing and standards solutions within highly-regulated industries is rooted in extensive domain knowledge of SAS software, clinical drug development and clinical data standards like CDISC.
d-Wise solutions for clinical trial optimization, metadata management and clinical data standards provide a solid foundation for extracting accurate business analytics to enable critical business decisions to be made rapidly and based on all the data.
Within the healthcare arena, d-Wise provides data optimization for actuarial, quality, medical-management, and operational data marts and data warehouses as well as support for fraud detection using data-driven and repeatable processes.