Implementation of Big Data infrastructure enables faster data processing, which, in-turn, allows organizations to support scientific analytics and derive more focused business outcomes for next-gen research.
Big Data architecture includes a radical integrated repository, along with scalable collaborative interfaces and advanced analytics with flexible deployment options.
Big Data architecture includes a radical integrated repository, along with scalable collaborative interfaces and advanced analytics with flexible deployment options. Big Data architecture includes a radical integrated repository, along with scalable collaborative interfaces and advanced analytics with flexible deployment options.
According to CBinsights, healthcare investments in Big Data totaled $274.5 million in 2012, and it went to $371.5 million in 2013.
Ref:- The CenterWatch Monthly, October 2013, A CenterWatch Article Reprint, Volume 20, Issue 10
Preparing for Next-Gen R&D
The current research is driven by securing regulatory safety and efficacy and mostly managed in silos. Focus is shifting towards real-world, integrated, connected research and care model, to support more complex disciplines.
The pharma industry is becoming more patient centric and realizing value of patient outcomes, improved safety and efficacy through better data insights. The data processing capabilities provided by Big Data and analytics ensure that insights are applied effectively.
In the traditional R&D model, internal and external generation of information was mostly managed in silos and separately validated for distinct analyses. Also there was significant professional and institutional misalignment between stakeholders such as biopharma, payers, providers and patients.
Whereas present integrated R&D model provides for more complex disciplines such as translational and evidence-based medicine, comparative effectiveness where stakeholders are largely aligned in their need to understand what interventions work for which individual patients and at what cost to offer better and economical care.
The new data processing and analysis paradigm such as Big Data technology is becoming inevitable for next-gen research.
Pharma companies would be interested in adapting next-gen research techniques by:
- Exploring historical and clinical data of prior trials of similar agents to identify adverse effects on participants
- Cross leveraging the data shared by other players in the field and the FDA’s data sharing program, OpenFDA
- Identifying outliers and patients who will benefit the most by connecting genotypes from Next Generation Sequencing (NGS) with patient population demographics that are available from clinical trial results
- Identifying clinical trial population on social media and sentiment analyses
- Developing insights for failing late-stage compounds
- Doing a network pathway analysis, connecting targets to effects on other proteins and cells in network. Linking one target to multiple disease areas with high throughput data
The next-gen R&D requires new outlook and ways of analysis to derive value from data generated from several stakeholders. Managing the entire data on a single, integrated platform is very essential in today’s world to make smart, cost-effective decisions to move faster in the value chain.