Big Data and Data Mining
Big data in Life/Health sciences is overwhelming not only because of its volume but also because of the diversity of data types and the speed at which it must be managed. The totality of data related to patient healthcare and well-being make up “big data” in the healthcare industry. It includes clinical data from:
- CPOE and clinical decision support systems (physician’s written notes and prescriptions, medical imaging, laboratory, pharmacy, insurance, and other administrative data)
- Patient data in electronic patient records (EPRs)
- Machine generated/sensor data, such as from monitoring vital signs
- Social media posts, including Twitter feeds (so-called tweets), blogs, status updates on Facebook, other platforms, and web pages
- Patient-specific information, including emergency care data, news feeds, and articles from various medical journals
The data landscape in life sciences is changing rapidly. At RxLogix, we have access to decipher advanced technologies, pre-competitive data sharing, and a huge amount of structured and unstructured data volumes. We are driving big data breakthroughs in our products. We at RxLogix are looking for new ways to leverage big data and turn it into actionable insights.
Our revolutionary products can help Pharma companies to accelerate drug discovery, better help patients, identify patterns, test theories, and understand the efficacy of treatments.
RxLogix uses Big Data and analytics to generate business value and drive innovation.
Making smart business decisions are not driven by how much data you have – but by how quickly you can discover insights from all that data. There is wisdom in data that is what we strongly believe in and that is the “purpose” behind our “innovation drive”.
At RxLogix, data mining plays a crucial role in the analytical discovery process and is a key to predicting future outcomes, uncovering market opportunities, increasing revenue, and improving productivity for our Pharma clients in a big way.
Our analytics techniques can be scaled up to any complex and sophisticated analytics that are necessary to accommodate data volume, data velocity, and data variety. The data will be stored in a multimedia format and structured, unstructured and semi-structured format. Structured data can be easily stored, queried, recalled, analyzed, and manipulated by a machine. Structured and semi-structured data also includes instrument readings and data generated by the ongoing conversion of paper records to electronic health and medical records.