The Role of RWD in Revolutionizing Clinical Trial Design
Using RWD to understand SDOH factors faced by patients with non-small cell lung cancer paves the way for improving health equity and more effective treatments for more people.
The application of AI technology is part of a carefully orchestrated effort dependent on human intelligence, and the collaboration of physicians, disease-specific specialists, nurses, data scientists, and technologists. Done right, these efforts can lead to profound benefits, and offer a promising future for clinical research and patient care.
Technology that offers tools that conform, de-identify, link and aggregate data and data science tools like AI, machine learning and advanced analytics, can help those researching rare diseases overcome the hurdles they face in discovery and development.
The ability to harness real-world insights at scale empowers life sciences companies and clinicians to develop more targeted therapies, improve patient outcomes, and drive evidence-based innovation in BPH treatment.
In an interview, Munich Re Specialty Senior Vice President Jim Craig talked about the risk that accompanies innovation and the important role that insurers play.
PurpleLab and Genentech executives will explore how to leverage real world data to eliminate blind spots in the patient journey — informing every phase of a drug’s lifecycle, from clinical development to market access — in a May 22 webinar.
Engaging in data sharing, observational cohort research, and deeper analysis of available data to yield further insights can drive improved results for patients, making label and cohort extension faster and more robust.
Considering the lack of high quality data, companies that excel in compiling and managing data effectively are attracting interest from industry partners and distinguishing themselves from their rivals. Here are some of the ways emerging AI applications are fueling and redefining the need for RWD across HCLS.
A new report by datma explores how companies can use federated data models to scale RWD.
Try a variety of LLM-based tools yourself. Understand the risks, limitations, and who is working on putting quality frameworks and guardrails in place. Engage your employees and partners in discussions about what might now be possible.
Enabling RWD-driven drug development accelerates the approval of more effective and affordable treatments. This approach successfully identifies patient patterns and density indicators and maps causal pathways.
By allowing study teams to design and conduct more inclusive and/or relevant studies, real-world data can enhance the standard of care and ultimately improve patient outcomes.