Solution Brief
Real-World Evidence
Generate lifesaving insights and value from real-world data with the most productive data and AI platform
The issue
Randomized clinical trials aren’t enough. Pharmaceutical companies need to understand how their products perform in the real world. That requires better measurements of the value that a device, medicine or vaccine brings to patients in a real-world clinical setting, which may differ from randomized clinical trial data. Life sciences organizations have a wealth of real-world data (RWD) from a variety of sources, including disease registries, complaints, hospitals and labs, wearable devices, physician notes, social media and more. They may also have data from commercial providers that repackage electronic health records and insurance claims.
The challenge for life sciences companies is how to establish evidence platforms that manage and analyze RWD promptly to generate insights spanning the product life cycle – from discovery and R&D to regulatory inquiries and commercialization. The process requires finding the right data source, getting it into a format that can be queried and preparing the analytical environment to analyze divergent patient-related data. The scientific and reproducible evidence generated to answer key questions and drive value is real-world evidence (RWE). In addition, life sciences organizations are now looking at GenAI to improve and expedite the analysis of data, and even bolster real-world data with the addition and use of synthetic data.
The challenge
Managing and storing complex RWD
It’s difficult to manage the life cycle of big data, which includes data capture, storage, maintenance, use and archiving.
Collaboration among different stakeholders
The number of people who can access and analyze RWD in an organization is too often limited because of the programming skills required.
Analyzing real-world data is time-consuming
RWD produces a massive amount of data that’s not in a structure ready for creating and analyzing patient cohorts. So much data makes updating analytical and reporting techniques and cohorts difficult.
Governance difficulty
Lack of transparency, reproducibility and governance can hinder the value of RWD and jeopardize the organization’s ability to maintain compliance standards across the data.
Maximize ROI from RWD and drive speed to value through:
(Improved efficiency in clinical treatment design, development and delivery.; Expanded access to RWE to achieve better insights, increase safety and deliver value faster.; Effective data management throughout the life cycle of clinical investigation.)
Our approach
SAS provides a scalable RWE platform that gives statisticians, data scientists, methodologists and quantitatively trained scientists an environment they can trust and easily use.
We approach the problem by providing a framework that helps life sciences companies:
Manage data
Cleanse, standardize, load and integrate RWD prior to using it.
Meet industry data standards
Apply SAS® capabilities for FHIR and OMOP data model integration.
Provide access to any user
Users can interact directly with data from patient populations, quickly determine the feasibility of studies based on the number of patients meeting criteria and reduce time and resources extracting patient populations interactively. SAS provides a programming interface for technical users and an intuitive point-and-click user interface for non-tech users.
Visualize and analyze cohort data
Easily explore and gain insights into cohort characteristics and data evidence and make it accessible for in-memory analysis and visualization in SAS or other technologies of choice (R, Python, third-party visualization tools).
SAS delivers best-in-class data integration and high-performance analytical and visualization capabilities to help life sciences companies solve interoperability challenges and achieve faster time to insights, which can be lifesaving for patients.
For us it made a lot of sense to move to the cloud, and to really have a modern engagement with SAS as our partner in giving us the capabilities we need in a scalable way.Rob Schwalje Associate Vice President, Organon Business Technology
Organon chose SAS® Viya® to help the organization advance health economics and outcomes research to address unmet patient needs.
SAS difference
The SAS’ solution for real-world evidence allows organizations to tap the potential of real-world data, aligning the most productive data and AI platform to collect and derive intelligence that adds value and saves lives.
SAS helps life sciences companies improve efficiency and drive speed to value through RWD so they can:
- Improve automation and collaboration for all teams and users.
- Access up-to-date code sets and data ontologies that provide consistency and reliability.
- Build cohorts faster from complex data sets with full lineage documentation from data to analysis.
- Enable quick discovery for trial feasibility analysis, synthetic control arms, safety and efficacy and more.
SAS enables companies to increase RWE collaboration by:
- Removing barriers between stakeholders to improve collaboration when generating and disseminating RWE.
- Democratizing access to RWD and RWE for everyone, including citizen data scientists and non-programmers.
- Accessing an open environment with full transparency and integration through open source.
SAS helps life sciences organizations effectively manage RWD via:
- Integration of data from internal and third-party sources.
- A single analytics platform that increases transparency, consistency and reproducibility of analysis and support-data compliance standards, and provides an audit trail for regulatory submission.
- Trustworthy AI with embedded model interpretability, fairness and bias monitoring. SAS empowers life sciences organizations to use AI and GenAI with confidence.
- The ability to navigate, visualize and explore data quickly.
SAS in life sciences
1
45
countries with customers in life sciences
0
2350
life sciences customers worldwide
-30
100
life sciences companies in the Fortune 500 use SAS