Train, Analyze, Intervene: How a Three-Pronged Approach Improves Data Quality in Dermatology Trials

The rapid evolvement of precision oncology requires real-world data generated at a scale that unlocks insights that can not be provided from clinical trial evidence. Machine learning and large language models can be used to quickly identify information, supporting custom integrated real-world evidence solutions to make informed decisions.

Explore the pivotal role of Next-Generation Sequencing (NGS) panel customization in driving advancements in oncology research.

By employing effective use of the patient voice, companies can better boost patient engagement success.

This piece offers a practical assessment of leveraging various decentralized methodologies and technologies.

Delve into the impact of external stakeholder requirements in developing an integrated evidence generation strategy that maximizes product value.

Learn how strategic planning and patient-centric design of real-world studies drive value critical to stakeholder acceptance.