Medical device companies face a unique challenge when they want to study their product performance post-launch – a significant lack of detailed data on use of their device in clinical practice and the related outcomes. Understanding clinical outcomes or adverse events post-market can be particularly challenging to piece together based on inadequate real-world data (RWD) on device use.

While medical device companies may use RWD from medical claims or registries, these data sources lack detailed information about device types and brands and can have a lag of 6-12 months. More importantly, sources like medical claims lack information on clinical outcomes and the patient care journey.

Companies in the pharma and biotech space have used real-world data derived from electronic health records (EHRs) to access clinical care and outcomes data for their therapies at a growing rate over the last decade. However, the use of RWD derived from EHRs, while presenting great potential for medical device research, has been quite limited due to three key challenges:

1. Real-world data sources lack device specifics: Device data capture is not standardized and lacks brand-level and device-specific identifiers for researchers to study specific devices as opposed to a broad device class.

2. EHR data, while clinically rich, is messy and hard to analyze: While rich in clinical detail, data from EHRs is incredibly “messy” and fragmented, especially when aggregated across healthcare organizations.

3. Critical information is hidden in clinician notes: Valuable clinical information on devices is often hidden in clinician notes; a researcher is more likely to find vital details about outcomes and clinical measurement in the free text in clinical and surgical notes than structured fields.

We explore each of these challenges in our latest whitepaper, “Better Device Outcomes Through Better Data.

You’ll also learn how Truveta creates research-ready, clinically rich device data that is clean and complete by:

1. Obtaining information on devices from multiple locations in the EHR

2. Standardizing messy and fragmented device data across thousands of sites of care to a device hierarchy using clinical expert-led AI

3. Extracting key device information from free-text clinician notes along with rich clinical details that are not found in claims data.

This results in US health data that has unparalleled breadth and depth and includes full diagnoses, vital signs, lab tests, clinician notes, and images.  Truveta Data is updated daily from 28 health system members from more than 20,000 clinics and 800 hospitals across all 50 states. This data is linked across health systems and augmented with social drivers of health (SDOH), mortality, and claims data for a complete view of the patient journey, then normalized, de-identified, and made available for research.

Today, Truveta Data features 150,000 unique devices and close to 13,000 device brands across 1,600 device manufacturers. Availability of timely, complete, and clean health data of the highest quality for devices can transform how medical devices are approved, monitored post-market, and improved.