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Harnessing Real-World Data for Regulatory Use and Applying Innovative Applications
Kelly H Zou1, Jim Z Li2, Joseph Imperato3
1Research, Development and Medical, Upjohn Division, Pfizer Inc, New York, NY 10017, USA.
Journal of Multidisciplinary Healthcare
|August 18, 2020
Summary
Real-world data (RWD) from diverse sources can generate real-world evidence (RWE) for drug insights. Harnessing RWD faces challenges, but artificial intelligence offers potential solutions for improved healthcare.
Area of Science:
- Health Informatics
- Clinical Research
- Data Science
Background:
- Vast amounts of real-world data (RWD) are accessible from numerous healthcare sources.
- RWD analysis yields real-world evidence (RWE), crucial for understanding drug usage, benefits, and risks.
Purpose of the Study:
- To define and explain real-world data (RWD) and real-world evidence (RWE).
- To detail the utilization of RWD and RWE by regulatory authorities.
- To explore challenges and the role of artificial intelligence in RWD analysis.
Main Methods:
- Review of existing literature and regulatory practices concerning RWD and RWE.
- Identification and categorization of diverse RWD sources (EHRs, claims, registries, devices, apps).
- Analysis of structured and unstructured data types within RWD.
Main Results:
- RWD encompasses a wide array of information from various healthcare touchpoints.
- RWE provides critical insights into drug performance and safety in real-world settings.
- Regulatory bodies are increasingly incorporating RWE into their decision-making processes.
Conclusions:
- Harnessing RWD is complex due to data heterogeneity and volume.
- Artificial intelligence presents a promising avenue for overcoming RWD challenges.
- Effective RWD and RWE utilization, supported by AI, can significantly advance global health outcomes.
Keywords:
artificial intelligencereal-world datareal-world evidenceregulatoryrobotic process automationMore Related Videos
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