

We define sustainability broadly as our mission to improve patient outcomes and human health, ensure employee well-being and minimize our impact on the environment. We’ve identified the sustainability issues most relevant to our business and our stakeholders, which are encompassed in our three sustainability pillars: People, Public and Planet.
Last year, we continued our support for the annual Light the Night Walk for The Leukemia & Lymphoma Society. Each year, around 140 walks take place across the U.S. and Canada to raise funds for research into blood cancer cures. Our annual fundraising campaign ran through September and October, promoting employee engagement and raising awareness of blood cancer.
Last year:
Funds raised by individual IQVIA participants and through corporate sponsorship will help support critical cancer research.
AI generates new insights and recommendations, helping to increase clinical trial efficiency at all stages, from trial design to data submission.
Up to 170% faster enrollment for the AI-recommended sites.
Up to 50% fewer non-enrollers for the AI-recommended sites.
11% reduction in time to start enrollment (first patient in), saving an estimated 30 days over historic averages.
Re-engineering our clinical trial test kit services is one of our biggest opportunities to reduce waste. We are reducing single-use plastic packaging and optional items from our test kits, such as single-use needles and pipettes.
Our commitment to our sustainability efforts is unwavering because we believe creating a healthier world takes more than just healthcare.
As part of our continued commitment to transparency, IQVIA has made its most recent EEO-1 Component 1 report publicly available in the link below. The EEO-1 Component 1 report is a mandatory annual data collection by the U.S. Equal Employment Opportunity Commission that requires all private sector employers with 100 or more employees, and federal contractors with 50 or more employees meeting certain criteria, to submit demographic workforce data, including data by race/ethnicity, sex and job categories.