CDISC Teams Up with Microsoft to Develop Open-Source Software for the Clinical Research Community

CDISC is teaming up with Microsoft to develop the CDISC Open Rules Engine (CORE), open-source software that executes machine-readable CDISC Conformance Rules. The global clinical research community will be able to leverage the CORE software to test study data for conformance to CDISC standards as well as regulatory and sponsor-specific conformance rule sets.

CDISC Conformance Rules as well as regulatory agency rules provide a critical quality check in ensuring study data conform to CDISC standards. An emerging industry best practice is to use Conformance Rules on an ongoing basis, throughout the study, to keep the data as close to submission ready as possible and to ensure quality in all data exchange scenarios. The free and open, Microsoft Azure-based CORE will execute Conformance Rules retrieved from the CDISC Library against standardized clinical research data and produce a report detailing the findings, which will allow researchers to receive, process, and review study data more efficiently and effectively.

"We are excited to work with Microsoft on another important initiative that extends our current work in support of standards-based process automation," said Sam Hume, CDISC VP, Data Science. "We look forward to building a community around CORE that will collaborate to create new innovative features and solutions."

"Microsoft is pleased to expand our work with CDISC to build the next generation CDISC Open Source Rules Engine to support Pharma Industry's digital transformation goals and ultimately accelerate time to market for life saving therapies." - Patty Obermaier, VP of Health and Life Sciences, Microsoft US.

To support and grow a community of open-source software developers, CDISC has initiated the CDISC Open Source Alliance (COSA). Several CDISC member organizations as well as individual developers have already committed to participate in COSA. Microsoft will provide ongoing guidance. Once released, CORE will become a COSA project supported by a global team of open-source developers and CDISC. A key component of COSA is community development.

CDISC collaborated with Microsoft on the Azure-based CDISC Library and CDISC 360, two projects that support standards-based process automation throughout the clinical research data lifecycle.

About CDISC

CDISC creates clarity in clinical research by convening a global community to develop and advance data standards of the highest quality. Required by the United States Food and Drug Administration (FDA) and Japan’s Pharmaceuticals and Medical Devices Agency (PMDA), recommended by the China National Medical Products Administration (NMPA) and adopted by the world's leading research organizations, CDISC standards enable the accessibility, interoperability, and reusability of data. With the help of CDISC standards, the entire research community can maximize the value of data for more efficient and meaningful research that has invaluable impact on global health. CDISC is a 501(c)(3) global nonprofit charitable organization with administrative offices in Austin, Texas, with hundreds of employees, volunteers, and member organizations around the world.

Most Popular Now

Unlocking the 10 Year Health Plan

The government's plan for the NHS is a huge document. Jane Stephenson, chief executive of SPARK TSL, argues the key to unlocking its digital ambitions is to consider what it...

Alcidion Grows Top Talent in the UK, wit…

Alcidion has today announced the addition of three new appointments to their UK-based team, with one internal promotion and two external recruits. Dr Paul Deffley has been announced as the...

AI can Find Cancer Pathologists Miss

Men assessed as healthy after a pathologist analyses their tissue sample may still have an early form of prostate cancer. Using AI, researchers at Uppsala University have been able to...

New Training Year Starts at Siemens Heal…

In September, 197 school graduates will start their vocational training or dual studies in Germany at Siemens Healthineers. 117 apprentices and 80 dual students will begin their careers at Siemens...

AI, Full Automation could Expand Artific…

Automated insulin delivery (AID) systems such as the UVA Health-developed artificial pancreas could help more type 1 diabetes patients if the devices become fully automated, according to a new review...

How AI could Speed the Development of RN…

Using artificial intelligence (AI), MIT researchers have come up with a new way to design nanoparticles that can more efficiently deliver RNA vaccines and other types of RNA therapies. After training...

MIT Researchers Use Generative AI to Des…

With help from artificial intelligence, MIT researchers have designed novel antibiotics that can combat two hard-to-treat infections: drug-resistant Neisseria gonorrhoeae and multi-drug-resistant Staphylococcus aureus (MRSA). Using generative AI algorithms, the research...

AI Hybrid Strategy Improves Mammogram In…

A hybrid reading strategy for screening mammography, developed by Dutch researchers and deployed retrospectively to more than 40,000 exams, reduced radiologist workload by 38% without changing recall or cancer detection...

Penn Developed AI Tools and Datasets Hel…

Doctors treating kidney disease have long depended on trial-and-error to find the best therapies for individual patients. Now, new artificial intelligence (AI) tools developed by researchers in the Perelman School...

Are You Eligible for a Clinical Trial? C…

A new study in the academic journal Machine Learning: Health discovers that ChatGPT can accelerate patient screening for clinical trials, showing promise in reducing delays and improving trial success rates. Researchers...

Global Study Reveals How Patients View M…

How physicians feel about artificial intelligence (AI) in medicine has been studied many times. But what do patients think? A team led by researchers at the Technical University of Munich...

New AI Tool Addresses Accuracy and Fairn…

A team of researchers at the Icahn School of Medicine at Mount Sinai has developed a new method to identify and reduce biases in datasets used to train machine-learning algorithms...