IQVIA OMOP Health Data provides streaming conversion of ingested data in near-time into the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM).
Developing IQVIA’s positions on key trends in the pharma and life sciences industries, with a focus on EMEA.
Learn moreDeveloping IQVIA’s positions on key trends in the pharma and life sciences industries, with a focus on EMEA.
Learn moreDeveloping IQVIA’s positions on key trends in the pharma and life sciences industries, with a focus on EMEA.
Learn moreDeveloping IQVIA’s positions on key trends in the pharma and life sciences industries, with a focus on EMEA.
Learn more"We strive to help improve outcomes and create a healthier, more sustainable world for people everywhere.
LEARN MOREReimagine clinical development by intelligently connecting data, technology, and analytics to optimize your trials. The result? Faster decision making and reduced risk so you can deliver life-changing therapies faster.
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SEE LATEST REPORTSBy making intelligent connections between your needs, our capabilities, and the healthcare ecosystem, we can help you be more agile, accelerate results, and improve patient outcomes.
LEARN MOREBuilding on a rich history of developing AI for healthcare, IQVIA AI connects the right data, technology, and expertise to address the unique needs of healthcare. It's what we call Healthcare-grade AI.
LEARN MOREYour new expert analyst is here. Be at the forefront of data-driven decision-making with a new generative AI tool that enables you to interact with our products and solutions like never before. Get results you can trust, faster.
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LEARN MOREEmpowering patients to personalize their healthcare and connecting them to caregivers has the potential to change the care delivery paradigm.
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VIEW ROLESIn today's interconnected world, the ability to track and respond to public health trends is more critical than ever. Real World Data (RWD) is playing an increasingly vital role in this endeavor, providing invaluable insights that help monitor public health trends, predict outbreaks, and guide policy-making on a global scale.
RWD encompasses a wide range of data collected from various sources, including electronic health records, insurance claims, patient registries, and even social media. This data offers a comprehensive view of health outcomes and patient experiences in real-world settings, beyond the controlled environment of clinical trials.
One of the most significant advantages of RWD is its ability to provide timely and relevant information. For instance, during the COVID-19 pandemic, RWD was instrumental in tracking the spread of the virus, identifying hotspots, and informing public health responses. By analyzing data from multiple sources, health authorities were able to make informed decisions quickly, ultimately saving lives.
Moreover, RWD is not just about tracking diseases; it also plays a crucial role in understanding the broader determinants of health. By examining data on social, economic, and environmental factors, researchers can identify patterns and trends that influence public health. This holistic approach enables the development of targeted interventions that address the root causes of health disparities.
In addition to its role in monitoring and responding to health crises, RWD is also transforming the way we approach preventive care. By identifying at-risk populations and predicting potential health issues, RWD allows for proactive measures that can prevent diseases before they occur. This shift towards preventive care not only improves health outcomes but also reduces healthcare costs in the long run.
The integration of RWD into public health strategies is not without its challenges. Ensuring data privacy and security is paramount, as is addressing the ethical considerations of using personal health information. However, with robust governance frameworks and transparent practices, these challenges can be managed effectively.
As we look to the future, the potential of RWD in public health is immense. Emerging technologies such as artificial intelligence and machine learning are set to enhance our ability to analyze and interpret RWD, unlocking new insights and driving innovation in public health. By harnessing the power of RWD, we can build a healthier, more resilient world.
IQVIA OMOP Health Data provides streaming conversion of ingested data in near-time into the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM).