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Overcoming RWD challenges for medical device research with complete EHR data

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"Overcoming RWD challenges for medical device research with complete EHR data"

How timely, complete and clean data from EHRs can accelerate device research

Medical device companies have historically faced significant challenges when trying to study product performance post-launch. Existing data sources, such as claims, registries, or hospital chargemasters, typically do not contain timely, device-level information necessary for robust research studies. Claims and chargemaster data also lack information on clinical outcomes and the patient journey.

Electronic health record (EHR) data have the potential to unlock deep insights into device use and effectiveness, product safety, and patient outcomes. A large-language, multi-modal AI model can harness these insights. 

Download this whitepaper to learn how:

  • How device-specific identifiers are captured, normalized, and structured to enable researchers to study specific devices
  • The types of device insights hidden in free-text clinician notes and how expert-led AI can extract those insights at scale
  • Potential use cases that these device-level data points unlock 


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