Machine Learning (ML) is a branch of Artificial Intelligence (AI) that allows computer systems to learn directly from examples, data, and experience. In the world of clinical trials, ML can be used to improve the quality, reliability, and availability of data by utilizing historical data to train ML algorithms and provide automated insights. This post provides a quick overview of non-clinical and clinical examples of how ML is transforming the production and consumption of...
This post provides an overview of the newest release of Oracle Data Management Workbench (DMW) a single, clinical platform that quickly and easily aggregates, cleans, and transforms data from multiple sources to create standardized datasets for downstream analysis and submission. It is the only industry solution that provides organizations with a scalable, secure, regulatory compliant, single source of truth for the entirety of clinical R&D.
Imagine a research world in which you have worked tirelessly to finalize a clinical trial protocol and need to build a randomization and trial management system (RTSM) for that protocol to achieve a critical first patient in milestone. Typically the build of such a system would be a two to three month process, potentially putting important study milestone dates at risk. Now imagine that same world in which the RTSM system is available a short 29 calendar days later.
Artificial Intelligence (AI) can detect conditions faster and with more accuracy than ever before. With each passing year, the number of clinical trial adverse event reports grows as much as 50% annually, taxing an already constrained safety process. The vast amount of safety data analysis lends itself perfectly to AI.
With financial resources becoming constrained, studies becoming more complex, and pressures to speed drugs to market intensifying, leveraging manual processes and spreadsheets for trial projections is no longer feasible. Here are six strategies for more accurate clinical trial forecasting and budgeting.
Just what is General Data Protection Regulation (GDPR) and how will it impact clinical trials? This article explores one aspect of GDPR, specifically, the right to be forgotten, and how it is implemented for the collection and use of clinical trial subject data.
A deep dive into why IT projects don’t always deliver the business benefits initially envisioned with complex, strategic business transformation initiatives, and how Oracle is addressing this delivery gap with a mechanism to measure customer success and satisfaction with our products.
As changes in safety regulations worldwide generate a significant increase in the number of clinical trial adverse event cases, the variety of big data sources that can be mined for safety signals is also rising. These trends translate into huge, new pressures on safety organizations as they continue to carry out their mission of multivigilance, a process covering the entire lifecycle of a medical product, from clinical trials through post-marketing surveillance. Currently,...
Publicly available DNA data and other personal medical information in the healthcare system can reveal highly sensitive knowledge about an individual. . As the volume of the data grows, the healthcare industry must address the data security challenges that arise to protect patient privacy, while maintaining the value that the information can offer to the healthcare system, itself.