The ROI of Smarter Trials: Q&A with Raj Indupuri

Phase III trial data volume has grown sixfold to over 5.9 million datapoints.

Without a unified, modern clinical data infrastructure to make sense of all this, teams are devoting significant time to data aggregation and review, report generation, and queries. As cycle times have become the new currency of drug development, AI has the potential to accelerate timelines and get treatments to patients faster, but sponsors need a better understanding of what the investment and return is for clinical trial technology.

Pharmaceutical Executive recently spoke with Raj Indupuri, co-founder and CEO of eClinical Solutions, to discuss the company’s new research on the ROI and measurable results generated by the company’s clinical data intelligence platform, elluminate.

According to Indupuri, price transparency for clinical trial technology investments have historically lacked. The goal of this data, along with the company’s new value calculator, is to help life science leaders better understand the immediate and demonstrable benefits of an AI-powered data intelligence platform and bring price transparency to the industry’s adoption of innovative technology.

A transcript of his conversation with Pharmaceutical Executive can be found below.

Pharmaceutical Executive: How do growing data volumes and increasing trial complexity impact clinical research teams?
Raj Indupuri: With data points collected in Phase III pivotal trials increasing by 283.2% between 2010 and 2020 and a growing volume of external data sources, clinical trials are becoming more complex, significantly increasing the effort required to consolidate, standardize, and review data. When study data is housed across multiple systems, listings, and spreadsheets, data management, medical review, and trial operations teams often spend excessive time navigating data across fragmented sources.

This leads to inefficient workflows, time-consuming manual processes, difficulty tracking data or finding a single source of truth, and the risk of working with outdated information. As shortening cycle times has become an organizational imperative, teams and organizations need technology investments that can demonstrate that result in a measurable way.

PE: Why do sponsors still rely on manual processes and how can AI-powered clinical data platforms help accelerate cycle timelines?
Indupuri: Study teams continue to be overwhelmed with fragmented data sources, trackers, Excel spreadsheets, and systems. As a result, they often spend significant time downloading reports and consolidating data, ultimately slowing down processes, limiting the ability to uncover issues and course correct, and leading to potentially costly study delays. Through implementation of an AI-powered clinical data intelligence platform, teams can help eliminate inefficiencies and streamline data processes, for example reducing time spent on data aggregation from disparate systems by 90%; time spent creating reports, listings, and data quality checks by 75%; and time from last patient last visit (LPLV) to database lock by 25%.

PE: Why has clinical trial technology historically lacked price transparency?
Indupuri: Pricing transparency for innovation investment in clinical trials is something that has remained limited to date within the industry. As tech adoption increased, sponsors have not always had a clear way to connect investments to measurable operational and financial outcomes. Some of the reasons are differences in starting points, a lack of quantified and verified “before” metrics for comparison, and variability in insourcing and outsourcing models.

At times, tech solutions in life sciences have solved an interesting use case or paint point, but the organization may not have connected that use case to anticipated return. In addition, the broader industry’s rapid wave of tech modernization made it somewhat difficult for biopharma organizations to know which update or investment was proving valuable, especially when innovation occurred simultaneously across a fragmented landscape of point solutions. Which recent change is driving ROI at the organizational level when different functions are responsible for a given tool or process? In the category of clinical data intelligence, the time was right to measure and confirm the ROI of connecting the data that underpins this clinical technology ecosystem.

Based on benchmark data from eClinical Solutions’ clients, our research found that an investment of $5M over three years in elluminate, eClinical Solutions’ AI-powered clinical data intelligence platform, has a payback period of 4.6 months and the 3-year total value created is $17.2M. By quantifying the value, sponsors gain a clear understanding of how a unified data foundation can deliver meaningful returns across speed, quality, and cost in clinical trials.

This level of transparency will be critical to help distinguish between meaningful innovation and investments that aren’t translating into measurable impact, especially as AI becomes more embedded across clinical development.

PE: As the industry distinguishes meaningful AI innovation from expensive hype, what role does price transparency and ROI play?
Indupuri: Our data, which is rooted in tactical applications rather than theoretical outcomes, found that sponsors who utilize elluminate can significantly reduce manual burdens for data teams and reduce cycle times by modernizing data infrastructure and analytics, streamlining clinical and data operations, and improving the speed and quality of clinical trials.

The analysis found it’s possible for a sponsor to experience up to 241% ROI over three years after an initial investment. We are still in the early innings of AI innovation in clinical trials. These findings showcase how technology investments can translate into measurable operational and business value, in this case by eliminating data siloes and fragmentation, surfacing signals across datasets that may not be apparent through traditional methods of data aggregation and analysis, and eliminating static data snapshots to replace them with continuous data intelligence and oversight that helps drive tomorrow’s breakthroughs.

PE: What does a potential 241% ROI signal to sponsors and how should they think about next steps for AI adoption?
Indupuri: Sponsors are looking for proof that technology can address the longstanding barriers in clinical trials and drive value. Our research sought to examine if and how sponsors and CROs are seeing ROI following their initial investment in an AI-powered clinical data intelligence platform, bringing transparency to the market. The data found that sponsors are seeing a direct ROI, demonstrating that technology innovation can deliver real value across the speed, quality, and cost of clinical trials.

AI readiness and continued success in AI adoption require frictionless data flows and a data foundation that is harmonized, governed, transparent and compliant. Implementing the latest technology, and this is especially true of AI, doesn’t automatically solve inefficiencies if an organization’s existing business processes are not aligned to support it. Full ROI will not be realized unless workflows evolve alongside data and AI innovation. But as we continue to witness the rapid escalation in data chaos, there is only more urgency to make efficient and intelligent use of the clinical data patients have entrusted to research.