What Are Sponsors To Do With An Abundance of Biomarker Data?
Yesterday we discussed how researchers are trying to incorporate new biomarker data into studies, which can generate a new set of hurdles for clinical development programs. It’s clear that overcoming these challenges starts with integration.
Medidata was born on cloud to help ease the integration burdens that often hold up other studies. We built Medidata Rave EDC with web services to ease the burden of integrating disparate systems. Medidata Rave Web Services are a set of transactional data exchange interfaces in Medidata products that support data integration using open web APIs.
Today a number of sponsors, CROs and academic research organizations utilize Medidata Rave Web Services. Memorial Sloan Kettering, for instance, created a library to interface their clinical systems directly with Rave Web Services, now publicly available on GitHub.
While API driven web services can help organizations to stitch together different applications to support precision medicine data capture, it requires a higher degree of configuration, testing, and quality assurance before launching into production.
Introducing the Medidata Rave Data Capture and Management Family
As a result of the need to offer a unified data capture platform that can handle the large variety of biomarkers that today’s targeted therapy studies demand, Medidata has expanded Rave EDC to encompass a holistic data management capture family.
It seamlessly captures and integrates the abundance of data streams and biomarker measurements that today’s targeted therapies demand, not only from clinics and labs, but also from sensors, apps, images, genomics and real world evidence (RWE).
By capturing and integrating such a wide array of study data, the Medidata Clinical Cloud also automates many of the most challenging data management workflows across randomization, supply, coding, and safety.
It is now possible for patients to electronically consent, get randomized, and be provided their first supply all on their first visit. Medical terms can be coded automatically with natural language processing and machine learning. And unsupervised genomic clustering of patients against other biomarkers can pinpoint safety and efficacy quickly and accurately.
If you’d like to learn more about how clinical trial technologies like the Medidata Rave Data Capture and Management Family help power today’s targeted therapy clinical studies, check out this recent article from the Journal of Precision Medicine.