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Clinical endpoints are evolving. Patient-reported outcomes, clinician assessments, and data from connected devices are now central to how many trials measure safety and efficacy. Yet these data streams are often collected and analyzed separately, limiting their value and delaying insight. Integrating clinical outcomes assessments (COAs) with device data at the point of capture is changing how endpoints support both science and operations.
Jan 28, 2026
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Electronic clinical outcome assessments have been part of clinical trials for years, yet many teams still experience them as one of the most operationally challenging components of study setup and execution. Despite advances in technology, the same friction points continue to surface across studies. This has led to growing interest in whether AI can finally simplify eCOA workflows, and where its limits remain.
Jan 28, 2026
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Feasibility has long been one of the most consequential, and most fragile, stages of clinical trial planning. Decisions about where to run a study and which sites to involve shape everything that follows, from enrollment speed to data quality to overall timelines. Yet feasibility has traditionally relied on limited historical experience, manual surveys, and assumptions that don’t always hold once a trial is underway.
Jan 28, 2026
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AI is often introduced into clinical development through small, targeted pilots. A tool to assist with protocol drafting, a model to improve enrollment forecasting, or an automation to speed document generation. These efforts can deliver real value, but on their own, they rarely change how trials are run at scale. Industrializing clinical trials with AI requires a broader shift, one that connects these capabilities into a coherent, end-to-end approach.
Jan 28, 2026
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Clinical trial planning has always involved a fair amount of uncertainty. Protocol assumptions are made months, sometimes years, before a study begins, often with limited visibility into how real-world conditions will evolve. Once execution starts, teams are left adjusting plans on the fly, reacting to delays, competition for sites, and shifting priorities. Decision intelligence and digital twin approaches are changing that dynamic by allowing teams to test decisions before those decisions carry real-world consequences.
Jan 28, 2026
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Few things create more stress in clinical trials than uncertainty around enrollment and timelines. Even well-designed studies can drift off course when recruitment slows, sites underperform, or assumptions made early in planning no longer hold true. For years, forecasting has relied heavily on static models and periodic updates. Today, that approach is starting to show its limits.
Jan 28, 2026
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As artificial intelligence becomes more deeply embedded in clinical development, the question is no longer whether AI can generate insights, but whether those insights can be trusted. In regulated, high-stakes environments like clinical trials, trust is not optional. It is the difference between adoption and abandonment. This reality is driving a clear industry consensus: AI must support human decision making, not replace it.
Jan 28, 2026
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Artificial intelligence is often positioned as the breakthrough that will finally solve long-standing challenges in clinical development. From forecasting enrollment to automating oversight and accelerating decision making, expectations are high. Yet many AI initiatives stall or underperform once deployed. The most common reason is not the sophistication of the algorithms, but the quality and structure of the data beneath them.
Jan 27, 2026
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Clinical control towers are often described as a solution to fragmented trial oversight. The promise is compelling: a single environment that brings together data, analytics, and visibility across studies, regions, and functions. In practice, however, building a control tower is far more complex than integrating dashboards or standing up a new platform.
Jan 27, 2026
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Clinical trial oversight is at an inflection point. For years, organizations have relied on dashboards, reports, and periodic reviews to understand how trials are progressing. While these tools brought visibility, they were never designed to keep pace with the growing complexity of modern clinical development. As trials generate more data from more sources, the industry is recognizing that visibility alone is no longer enough. What’s needed now is decision-ready insight.
Jan 27, 2026