Late phase research, technology solutions and access to RWD are at the forefront of creating efficiencies in all stages of study design and implementation
Late phase research is undergoing rapid transformation due to the impact of healthcare digitalisation and the greater availability of and access to Real World Data (RWD).
How can the abundance of real world data (RWD) from Electronic Health Records (EHRs) enhance your late phase research studies while decreasing study costs?
With ICON’s Real-World Intelligence™ – real world data (RWD) plus advanced expertise – you have the insights and tools to answer these important questions.
With the right technology infrastructure and support, sponsors can more completely leverage RWE across the enterprise for maximum value.
Read our white paper, “On a Technology-Enabled Collision Course: Clinical Research Meets Clinical Practice through Real World Evidence” and learn how to:
Real World Data from Electronic Health Records can enhance your late phase research studies while decreasing study costs.
RWD-powered, post-marketing studies require fewer resources and EHRs are an efficient data source to support observational studies. Retrospective and prospective analyses, as well as case-control cross-sectional studies, can be more cost-effectively performed using EHR data.
An EHR system can also be used as the information backbone of Pragmatic clinical trials by supporting recruitment efforts and automatically capturing outcomes data.
Read our whitepaper: Meeting Evidentiary Needs with Electronic Health Records white paper
Real-World Evidence (RWE) is derived from Real World Data (RWD), and early use of Real World Evidence can cut post-marketing study costs and Medical Device time-to-market.
Devices are especially good candidates for early RWE use since evidence collected in the context of actual patient care from previously approved versions or similar devices often can be used to supplement findings from clinical trials of the latest version in development.
RWD such as sleep quality and quantity have clinical relevance in Alzheimer's disease, providing objective measures of sleep and activity patterns that are not subject to patient recall bias. Review the use of wearables in Alzheimer’s disease to provide objective measures of sleep and activity patterns that are not subject to patient recall bias.