ZS Launches ZAIDYN Clinical Development, Expanding Digital Life Sciences Platform

ZAIDYN Clinical Development empowers life sciences companies to break silos in clinical trial design and planning, remove barriers to enrollment and engagement, and provide patients with better products faster.

Global management consulting and technology company ZS has announced the launch of ZAIDYN Clinical Development. The offering is the latest extension of the ZAIDYN platform by ZS, designed to enable life sciences companies to digitally transform and scale customer engagement, field performance, analytics and – now – clinical development design and planning.

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“ZAIDYN Clinical Development will help our clients navigate the large-scale, disruptive changes we see happening in our industry – and those on the horizon as AI and machine learning transform clinical research and development (R&D)”

“Today we announce the launch of ZAIDYN Clinical Development, an extension of more than 35 years of software expertise,” said Sanjay Joshi, managing principal and platform lead for ZS. “With ZAIDYN, life sciences organizations can connect their global teams, products, processes, applications, algorithms and data assets for powerful insights to shape clinical research and accelerate clinical development timelines. Our platform works with existing IT systems to drive efficiency and connectivity, reduce costs and enable life sciences companies of all sizes to innovate, scale and grow as the global healthcare ecosystem evolves.”

ZAIDYN Clinical Development harnesses the power of a machine learning network, AI-driven insights and algorithms to create a fluid, fast, frictionless trial experience for business users, site researchers and patients. With our products, companies have the power to:

  • Drive innovation and decision making throughout the clinical development process with insightful, dynamic reporting and analysis for clinical teams and a high level of clinical trial activity for leaders
  • Optimize clinical trial planning and protocol design using AI and predictive insights that reflect participants’ and patients’ experiences and perspectives
  • Enable teams to test and model location and enrollment scenarios to find ways to make trials more inclusive and engaging, ease the burden on participants, mitigate changes, and accelerate trial efficiency
  • Create a growing universe of industrial and historical clinical research data, real-world data, location criteria and insights into patient studies
  • Raise the bar for clinical research with products built on Amazon Web Services (AWS) that apply the latest clinical data standards and drive connectivity in upstream and downstream systems

More than 130 companies already use ZS products to make business decisions. Now these companies can combine the clinical expertise of ZS consulting with the latest features of ZAIDYN Clinical Development to break business and clinical silos and enable teams to collaborate more efficiently.

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“ZAIDYN Clinical Development will help our clients navigate the large-scale, disruptive changes we see happening in our industry – and those on the horizon as AI and machine learning transform clinical research and development (R&D),” said Aaron Mitchell, ZS’s global R&D excellence practice leader. “Smarter life science platforms can help us improve the diversity of clinical trials, bring life-saving therapies to patients faster, and unlock patterns and breakthroughs that will change the future of clinical research. It’s all about using insights from data more effectively to drive more inclusive trials and better patient outcomes.”

“With the support of ZS and the ZAIDYN platform, we are accelerating the transformation we initiated three years ago,” said Cynthia Deparis, program manager at Sanofi. “The platform helps us take a data-driven approach to identify the best countries and locations for clinical trials. It has helped us integrate and expand the universe of clinical operational research data so that we can target sites and countries that help us realize our development portfolio and think about various strategic parameters such as the competitive landscape, the activity of our colleagues, historical knowledge about sites and feedback from well-equipped sites. With these insights and the ability to model different scenarios, we can continue to optimize recruitment and improve the accuracy of our projections, putting the development of our clinical trials on a continuous improvement cycle.”

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