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Like the foundation of a new house, data plays a fundamental role in the management, monitoring and analysis of results for a clinical trial. Data sheds light on what direction to take a trial, understanding risks and providing new insights.
A whole new world of information has opened through new technology and the options continue to expand. Wearables, mobile applications, virtual technology and more continue to emerge, which brings a wealth of data to clinical trials. Emerging data sources in clinical trials include real-world evidence, e-clinical outcome assessments, mobile device-driven data, social media networks, and electronic health and medical records, among others that are now leveraged for more in-depth safety and efficacy analysis. The question becomes how can drug developers and service providers best partner to collate, validate, and secure all clinical trial-related data in a uniform format that can be leveraged through a study-specific, accessible, and dynamic platform for further analysis and interpretation.
Finding Insights: Integrating a Wealth of Data
While these new sources bring a new flow of information to clinical trials, a new challenge has emerged: how to successfully manage it and identify meaningful, actionable insights that impact a trial. The challenge goes beyond creating a solution that houses data centrally. The next generation of clinical trial technology solutions will allow clinical teams to access critical data how and when they want it.
"A whole new world of information has opened through new technology and the options continue to expand"
With the growing application of dynamic data visualization technology, this challenge can now be addressed. By using intuitive tools, large amounts of sensitive trial data can be translated into meaningful information. Data visualization not only contributes to finding the quality and meaning, it saves a tremendous amount of time and adds efficiency to the incredibly complicated trial process. Researchers receive real-time results, eliminating weeks of analysis during which new data has arrived. It also provides summaries, highlighting key indicators so researchers can locate what they need faster to respond and adjust.
It’s also important to realize data visualization doesn’t replace basic data. It takes use of it to a new place, helping to identify relationships and patterns that provide deeper insight. It can help direct a clinical trial about where to focus for optimal results with the added value of integrating information from different systems. While protecting sensitive information, data visualization brings together diverse Sources of Truth (SoT) without manipulation and slowing down timelines. It’s a way for researchers to find innovative ways to create uniform data while maintaining integrity and program quality.
Benefits of Data Visualization for Clinical Trials
These examples show how clinical trials can gain value, speed and efficiency using data visualization. It directly contributes to strengthening the following areas:
• Providing holistic picture of overall clinical trial progress
• Offering a summary of key results in a visual format
• Creation of a dashboard, where foundational KPIs can be monitored with one click
• Supporting strong risk-based monitoring, making it easier to spot areas of concern
• Greater flexibility to create ad-hoc reporting as new questions or patterns surface
• Faster analysis of adverse events
• Helping researchers spot safety signals for immediate adjustments
• Broader insights by integrating information from multiple databases
• Creating the opportunity to review data early and often
Six Steps to Get Started
Data visualization offers so much possibility that identifying where to start can be overwhelming. It’s best to begin with a basic question—where will data visualization make the most difference in current clinical trials?
1. Assess active trials. What trials have the highest priority? Which could benefit most from data visualization?
2. Review the volume of data in different trials. Are there complex trials where multiple types of information are streaming in and it’s been particularly difficult to analyze results?
3. Ask what trials face the most risk. Do certain trials need faster access to real-time results, safety signals and adverse event information?
4. Consider integration needs. Would bringing together different sets of data provide important information and speed analysis for specific clinical trials?
5. Identify your organization’s top needs for a good visualization tool. What requirements would most help your researchers? Strong graphical capabilities? Easy analysis? Ability to drill down? Integration? Intuitive user interface?
6. Work with a consultant to find out what other companies are doing. What best practices exist? How are other clinical trials using data visualization? What should we avoid?
With the rising complexities of clinical trials, dynamic data visualization offers the opportunity to simplify decision-making by collating disparate sources of information and transforming it into actionable intelligence. That opens up a new world of fresh insights that will lead to stronger trials, improved patient safety and better results.