Much like the calls for more data visualization in health IT at June’s Health Datapalooza, data visualizations can play a powerful role in improving the speed and quality of clinical trials. For example, the VP of ClinOps at a 20-year-old biotech start-up recently leveraged visualizations to help proactively manage a major last-minute patient enrollment challenge. The stakes were high: patient enrollment dropped to 50% just before the final milestone and the company’s recent stock price anticipated the lead product approval. The VP needed reliable information to find the most efficient way to reduce patient withdrawals while also recruiting new patients. By leveraging visualizations with real-time alerts and intelligent forecasting, the VP was able to proactively conduct trend analysis to get the trial back on track.
Without visualizations, clinical trial leaders can easily miss adverse events, an oversight that can set a trial back by months and worse, prevent life-saving drugs from getting to patients in a timely fashion.
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The delays also come with a heavy price tag. Recent studies report that the cost for developing a new drug ranges from $2-5 million. Additionally, Gartner estimates the daily operational cost for trial interruptions is $30,000.
The move from manual processes and disconnected on premise IT systems to cloud will mitigate the operational inefficiencies that cause these million dollar delays. When it comes to clinical studies, working with siloed data from disparate systems and contract research organizations (CROs) creates uncertainty in data quality. This is only exacerbated with manual data aggregation and collaboration processes. Transitioning to fully digital will increase data accessibility and offer real-time insights to help drug makers troubleshoot proactively versus reactively.
A Picture (or Graph!) is Worth a Thousand Words
The transition to fully digital empowers the use of visualizations, which can turn numbers on stacks of paper into graphs that help a user instantaneously spot trends. For example, a Phase 3 cardiovascular disease study sponsored by a mid-size medical device company underwent a major transformation once they adopted visualizations into their study management practices.
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The study, which was conducted with 300 subjects at over seven sites, noticed that quality and compliance issues were as high as 30 percent. The Clinical Operations team reacted to this by adding more monitoring visits, in turn delaying the study and forcing an increase in trial budgets by more than 20%. Upon further investigation, the team identified several data inconsistencies. For example, the medical device sponsor was missing adverse effects and had incomplete data on the case report form, a tool that’s used by clinical trial sponsors to collect data from each participating site.
After meeting with the Clinical Trial Manager and Clinical Research Associate to review options, the trial leaders developed better visualizations that would help identify red flags before they became real problems. In addition to intelligent forecasting and real-time alerts, the medical device company also provided the sites with access to the same dashboards and high-level comparison reports. This fostered a healthy competition between the sites which improved the trials’ overall progress. They were able to decrease the monitoring visits by approximately 25%, while improving data quality and the speed to database lock by 2 months.
The study achieved this through five types of new visualizations including:
- Query rates and resolution times – With the ability to visualize the fields with the highest numbers of queries, the medical device company could identify whether the sites needed training or better CRF guidelines.
- Adverse events by severity – By identifying the most common adverse events from specific treatments, trial leaders could quickly spot these adverse events from a specific treatment.
- Protocol deviations – With a visualization of the protocol deviations and their severity, teams could identify needs for protocol amendments.
- Non-compliance and enrollment – Through a graded scale, non-compliance issues could help identify patients missing follow-up, incomplete tests and other important aspects of the trial that raise compliance questions.
How to Stay the Course: Top Three Data Visualization Best Practices
Creating a strong digital strategy requires discipline and alignment. There are three organizational tips to keep in mind to ensure a successful transition away from manual methodologies:
- Step 1: Establish a culture committed to continuous quality. Leaders should create an environment that values continuous data quality above all else to help ensure that it is a part of all operations, every step of the way.
- Step 2: Know your metrics — and automate them. At the beginning of a project, the team needs to align on critical study objectives to agree on what visualizations will arm them with the information they need to make smarter decisions. Automating key insights with alerts and forecasting will further create process efficiencies.
- Step 3: Rally the team and ensure alignment across the board. Clinical studies are often comprised of many cross-functional team members that must be collaborating across the same visualizations and in sync on priorities, milestone goals, etc.
With this foundation in place, drug developers can modernize life sciences. By getting a true “bird’s eye view” through clinical trial data visualizations, life science leaders will not only help reduce costs, but get life-saving drugs into the hands of the patients that need them most.
