Creating Health Equity

Evaluating Real-World Data for Insights

Real-world data (RWD) can complement and enhance standard clinical trial data by enabling research into populations and treatment settings much broader than those normally included in such studies or the academic centers that conduct them. Epidemiology and health services research are often deployed to convert raw RWD to real-world evidence — but the quality of the RWD is paramount.

The Challenge

Ģ��tv's life sciences client wanted to better understand and overcome RWD challenges related to blood and tissue sample tests used to diagnose diseases ranging from cancer to COVID-19. Ģ��tv assembled a multidisciplinary team to design and develop a bespoke scientific framework for appraising the quality of RWD across a diverse market landscape. We also developed software to empower researchers to apply the framework to an array of data providers and generate "goodness of fit" scores for each based on researcher use cases.

Ģ��tv's MULTIDISCIPLINARY Solution

Which RWD sets should be used for specific research questions? How can we compare data sets and data providers? Ģ��tv put together a multidisciplinary team to devise ways of measuring the quality of data. We also developed software that would determine which data to use for various use cases.

Our data sciences experts developed a multi-dimensional assessment strategy to score data quality characteristics such as accessibility, accuracy, coverage, integrity, timeliness, and uniqueness, among others. We then prioritized assessment questions according to specific research inquiries and built an interactive vendor repository tailored to our client's needs, enabling users to browse, filter, prioritize, and filter assessment scores according to research needs.

Ģ��tv's Impact

By leveraging "fit-for-purpose" RWD sources and generating real-world evidence, our client built a more accurate picture of the value, benefit, and risk of in-vitro diagnostic tests on a more diverse population representing different geographies and healthcare settings.

This work is vitally important for health equity because it's helping shed light on populations at higher risk of disease due to clinical, genetic, and social factors.

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