ZAIDYN® Patient helps biopharma extract more value from its analytics
Impact by the numbers
A leading U.S.-based biopharmaceutical company needed to optimize ROI from its investment in an Amazon Web Services (AWS) platform to develop a robust patient analytics capability. Legacy analytics processes and codes were slow, expensive to maintain and unable to keep pace with the business. The legacy system could no longer could deliver value to corporate marketing, U.S. and global commercial operations, IT and other stakeholders.
The challenge
The Fortune 200 company’s real-world-data-based analytics were performed ad hoc, with code written on an as-needed basis to support the given use case at hand. This approach constrained its ability to reuse, standardize and scale its capabilities, while making it difficult for teams to maintain consistency across business rules. The client was looking to elevate the existing analytics processes to accelerate decision-making and invested heavily in building a patient analytics data management and reporting capability on top of the AWS platform.
As part of its overall transformation vision, the company wanted to build a modern analytics platform that could:
- More accurately, quickly and less expensively respond to analytics requests from the business with optimized processes
- Free up time for decision-making and automated insights generation
- Expand analytics capabilities to new therapy areas and scale nonlinearly to keep pace with business growth and evolving portfolio requirements
- Integrate with AWS platforms to expedite patient-level analytics to optimize business outcomes
The solution
For this project, we implemented ZAIDYN Patient in a software-as-a-service environment to support the company’s patient analytics needs while adhering to data governance and ownership requirements. ZAIDYN is ZS’s AI-powered, cloud-native life sciences intelligence platform. It unites data, analytics and workflows across commercial, medical and patient teams. Our approach facilitated the migration from legacy processes and the adoption of new analytics data sources. Implementation was completed in less than a month.
ZS worked with the client’s IT teams to customize the user interface and transfer previous clinical studies data and findings to the new platform. ZS collaborated with stakeholders to develop performance requirements, align on business rules and triangulate the results.
A phased approach allowed the company to set up studies by indication, allowing the client to adopt ZAIDYN Patient according to plan. Detailed documentation and training sessions helped teams get to work and demonstrate ZAIDYN’s potential to the company’s global and forecasting teams.
“Everything from setup to usage has moved so fast. It’s been a dream so far, and I was able to demo it to several stakeholders myself,” said a senior director of global data strategy at the company. “ZS has been an exceptional partner in this initiative.”
The impact
The client saw early results from implementing ZAIDYN Patient, including a 35% reduction in cycle time and other notable benefits including:
- Greater agility and speed. We set up the platform, onboarded data and made the platform analytics-ready in less than a month.
- Reduced cycle time. We demonstrated immediate value with reusable knowledge libraries coupled with intuitive visual workflows and built-in operational components.
- Lower total cost of ownership (TCO). We projected how the client can lower TCO by 20% within the first two years.
- Optimized ROI. ZAIDYN seamlessly integrated with the AWS platform program, helping the client transition to a new data provider.
- Standardized and automated workflows. ZAIDYN Patient increases knowledge library reusability and reduces reliance on people-based expertise to foster innovation.
- Scalability. ZAIDYN Patient will help solve various use cases across multiple therapeutic areas in oncology, such as hormone receptor positive, triple-negative breast cancer, non-small cell lung cancer, acute myeloid leukemia and myelodysplastic syndromes.