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EMEA Thought Leadership
Developing IQVIA’s positions on key trends in the pharma and life sciences industries, with a focus on EMEA.
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EMEA Thought Leadership
Developing IQVIA’s positions on key trends in the pharma and life sciences industries, with a focus on EMEA.
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Europe
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EMEA Thought Leadership
Developing IQVIA’s positions on key trends in the pharma and life sciences industries, with a focus on EMEA.
Learn more -
Middle East & Africa
EMEA Thought Leadership
Developing IQVIA’s positions on key trends in the pharma and life sciences industries, with a focus on EMEA.
Learn more
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Impacting People's Lives
"We strive to help improve outcomes and create a healthier, more sustainable world for people everywhere.
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Harness the power to transform clinical development
Reimagine 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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Generate and disseminate evidence that answers crucial clinical, regulatory and commercial questions, enabling you to drive smarter decisions and meet your stakeholder needs with confidence.
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Elevate commercial models with precision and speed using AI-driven analytics and technology that illuminate hidden insights in data.
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Orchestrate your success across the complete compliance lifecycle with best-in-class services and solutions for safety, regulatory, quality and medical information.
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When your destination is a healthier world, making intelligent connections between data, technology, and services is your roadmap.
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Unlock your potential to drive healthcare forward
By 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.
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IQVIA AI is Healthcare-grade AI
Building 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.
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Meet the IQVIA AI Assistant
Your 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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Your healthcare data deserves more than just a cloud.
The IQVIA Human Data Science Cloud is our unique capability designed to enable healthcare-grade analytics, tools, and data management solutions to deliver fit-for-purpose global data at scale.
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Innovations make an impact when bold ideas meet powerful partnerships
The IQVIA Innovation Hub connects start-ups with the extensive IQVIA network of assets, resources, clients, and partners. Together, we can help lead the future of healthcare with the extensive IQVIA network of assets, resources, clients, and partners.
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Proven, faster DCT solutions
IQVIA Decentralized Trials deliver purpose-built clinical services and technologies that engage the right patients wherever they are. Our hybrid and fully virtual solutions have been used more than any others.
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IQVIA Patient Experience Solutions with Apple devices
Empowering patients to personalize their healthcare and connecting them to caregivers has the potential to change the care delivery paradigm.
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"At IQVIA your potential has no limits. We thrive on bold ideas and fearless innovation. Join us in reimagining what’s possible.
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Developers and AI: the future of healthcare
Apr 27, 2022
I remember Steve Balmer had a video, back in the day, going “developers, developers, developers, developers” at a Microsoft conference. And that really spoke to me because developers are uniquely positioned to change the world. Everything that we have around us is connected, it´s an app that we are using. And, that´s why I love what I do: the technology, the data, the algorithms that we're bringing to the market are just outstanding.
Machine Learning diagnostics, it's not meant to replace the diagnostician, the healthcare professional, the medical professional. We're trying to give them more tools, more insights as to what kinds of decisions can be made.
Our use cases are typically much more challenging and much more impactful than most other domains. And when we make a recommendation, somebody's life could be on the line, somebody's healthcare diagnosis could be on the line. So, we have to be a lot more thorough, a lot more careful, and more complete with the kind of information we report back to the users over Machine Learning models and models in production. We have to have some kind of explanation around it, discussing where this model, this recommendation, even this algorithm, how was it trained? And the problem with a lot of these things is that the Machine Learning operations piece of it is missing. How can you quickly pivot and learn from incoming data that is coming in so quickly? How can you quicky swap out the algorithms that are in the middle that are helping you make the decisions and how can you scale your decisions in production? So, you can monitor what's happening in production.
My approach has recently been to give people the tools, the architecture, the recommendations to build those platforms with those best practices embedded into them. So, you're going to be using the best tools, the best architecture, the best approaches, so that the bar is going to be lowered for you. And you're able to understand the set of decisions that are being made in the approach that we're bringing, that makes the diagnostician, the medical professional, the healthcare professional, more comfortable with bringing to bear their expertise, to the decision that we're helping them make. So, their confidence is boosted that the decision they're making is the right decision because we help them understand what exactly is being done by our algorithms, by our models. They're explainable AI.
This is a new thing that IQVIA is doing with healthcare to offer healthcare Machine Learning, end points to our clients. IQVIA is specifically poised to have that subject matter expertise in order to clearly explain what we know about this specific use case, the specific question that we're trying to answer, and we have the data to show them were learned from this in order to make these recommendations.
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