Gain additional clinical trial support via an advanced platform for new, real world evidence insights.
Because clinical trials for new drugs face close regulatory inspection, they need the fullest measure of safety and efficacy data support available. Currently, with huge and ever-increasing amounts of real world data available to clinical trial teams, this kind of additional support is achievable. What's needed is an advanced solution that can collect, organize, source, manage, and analyze real world data, turning it into real world evidence to support beneficial clinical trial results and actionable decisions for more successful patient outcomes.
Control the Data Tsunami and Optimize Data Quality.
The data management landscape is changing dramatically due to daily increases in an explosion of new data sources including biomarkers, electronic medical records (EMRs), electronic patient-reported outcomes (ePROs), genomic and proteomic data, imaging data, labs, operational data, social media data, wearable device data, and more, along with corresponding exponential increases in big data volume and escalating data velocity. The clinical data manager's challenge is to deliver all of these data sources along with traditional, clinical trial data sources for regulatory submissions. All this data must be of the highest quality and in analysis-ready format to meet regulatory standards for new therapy approvals.
Oracle Big Data Discovery can turn massive amounts of raw data into new insights in minutes. It can take structured and unstructured data (labs, biomarkers, EHR, and additional, multiple, third-party sources, such as those mentioned in the list above) from big data frameworks, like Hadoop, a unified healthcare analytics platform, like Oracle Healthcare Foundation and an end-to-end, clinical trial data solution, like Oracle Health Sciences Data Management Workbench, to uncover new value instantaneously.
Assess trial developments in real time. Spend time analyzing data, not cleaning it.
Often, with their responsibilities for fail-fast trial interventions and key, real data, regulatory submission reports, biostatisticians must take time to prepare real world data for analysis, instead of evaluating it. They need time-saving, advanced solutions for continuous, fact-based overviews of trial activities and for automatically created, analysis-ready, optimized, real world evidence.
Oracle Health Sciences Data Management Workbench cleans, integrates, and delivers analysis-ready data. Oracle Healthcare Translational Research enables biostatisticians to accelerate biomarker analysis for drug discovery and for patient cohort identification.
With access to massive amounts of optimized real world data, clinical data scientists must use their understanding to convert real world, data-based inference into clear insight, while steering the clinical team away from unproductive data paths. They need an advanced platform offering a single, organized view of data from multiple sources. This advanced platform must also provide tools to help form new questions, drive new data patterns/directions, and translate any newly derived, drug-patient insights into support for real world evidence.
Organizations investing in these types of advanced platforms will be well positioned for continued evolution of capabilities that provide value to patients and society, in general. For example, looking into the future, routine, patient biosurveillance could aid data scientists in the early detection of deadly blood pathogens and the discovery of patient—zero, situational pathways. This kind of insightful, fast, data discovery—sorting through huge amounts of information (from biomarkers, electronic case report forms (eCRFs), electronic medical records (EMRs), electronic patient reported outcomes (ePROs), genomic and proteomic data, imaging data, labs, operational data, social media data, wearable device data, and more) to identify key data connections—could help avert a global pandemic.
Oracle Big Data Discovery — working with solutions like Oracle Healthcare Foundation and Oracle's Data Management Workbench, or frameworks like Hadoop — can identify new data patterns and relationships from very large amounts of raw, structured and unstructured data in no time. Oracle Healthcare Foundation can analyze multiple types of molecular and omics data (genes, proteins, pathways, variants, and expressions) that can result in biomarker identification for clinical trial patient cohorts and drug discovery research