Oracle Introduces Oracle Health Sciences Data Management Workbench

Life sciences organizations running clinical trials are challenged to aggregate, reconcile, manage, and control a growing deluge of data from internal and external sources. In addition, clinical development is an increasingly networked and collaborative discipline across companies, which further complicates the data management landscape. These factors, as well as continued pressure to boost clinical program productivity and reduce the cost and risk of clinical trials, are driving a critical need for a new approach to clinical data management and warehousing. Oracle has introduced Oracle Health Sciences Data Management Workbench to help meet these needs.
 Using Oracle Health Sciences Data Management Workbench, researchers can integrate, reconcile and analyze clinical data faster and more accurately to automate data load, transform and cleanse processes.  As a result, study sponsors can make faster, more informed decisions that improve control, support adaptive studies and accelerate clinical trials and time to market.
 Speaking about the solution, Steve Rosenberg, senior vice president and general manager, Oracle Health Sciences said, “As the industry’s only true end-to-end data management solution, Oracle Health Sciences Data Management Workbench enables health sciences organizations to improve clinical program productivity and reduce costs and risk by reducing study cycle times, improving data quality and decision making,”.
 Oracle Health Sciences Data Management Workbench enables health sciences organizations to accelerate clinical studies and boost program productivity while helping to drive down the cost and risk of clinical trials. The solution, which is integrated with Oracle Life Sciences Data Hub, automatically aggregates, integrates and reconciles the rapidly growing volume of data that clinical trials collect from internal and external data sources, including electronic data capture, laboratory, safety/pharmavigilance, and drug supply systems. By doing so, it helps dramatically decrease the complexity of data management and reduce the cost of clinical trials.