Knowledge Center

The way clinical trials are monitored is once again about to change. 

This isn’t the first time clinical trial monitoring has evolved. Over the past three decades, the industry has undergone two major transformations in how monitoring is performed.

For much of the 1990s and early 2000s, monitoring relied on frequent on-site visits and extensive source data verification (SDV), with many studies aiming to verify nearly every data point. While rigorous in intent, this approach became increasingly difficult to sustain as trials grew larger and more complex, delivering diminishing returns relative to its cost and operational burden.

The industry responded by adopting risk-based monitoring (RBM) and centralized monitoring, shifting from exhaustive verification to a targeted, data-driven approach focused on the risks that mattered most. This evolution was reinforced by FDA and EMA guidance and ultimately codified in ICH E6(R2) in 2016.

While RBM improved efficiency and data quality, it did not fundamentally change how monitoring work was performed. CRAs still spent much of their time manually reviewing data, reconciling information across systems, documenting findings, and coordinating follow-up activities. 

We are now entering a third shift. Unlike the first two, which primarily redistributed how monitoring effort was allocated, this one fundamentally changes the old rules on who and what is monitoring trial performance. 

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For nearly six decades, the way data moves through a clinical trial has barely changed. A site collects it, a sponsor analyzes it, and the FDA eventually receives it, months or years after the fact. That lag has delayed regulatory decisions, slowed drug development timelines, and in some cases, kept promising therapies from reaching patients who needed them sooner.

Thankfully, that is starting to change. In late April 2026, the FDA announced something that has been talked about for years but rarely demonstrated, real-time clinical trials. 

The agency unveiled two proof-of-concept studies already underway, one with AstraZeneca and one with Amgen, where safety signals and endpoint data are being shared with the FDA as the trial progresses, not months or years later. Additionally, a broader pilot program is set to launch this summer.

This is a fundamental rethinking of how clinical evidence gets generated and reviewed, with implications for how quickly the industry can move from trial initiation to regulatory decision. The old model was sequential in how it collected data, packaged it, and delivered the data at the end. The new model treats it as something that flows continuously, in real time, to the people who need to act on it.

However, real-time data sharing only works if the data being shared is coherent in the first place. 

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What makes a good doctor? Being an expert in modern medicine is table stakes, as no one trusts a doctor who doesn't know the ins and outs of healthcare. 

However, as anyone who has encountered a medical professional whose bedside manner left something to be desired, it’s not enough. Research agrees, as studies from all the way back to the 1980s shows that knowledge without communication skills often fails to get patients to adhere to treatments.

The best doctors combine both clinical expertise and genuine bedside manner in order to give patients the best experience. 

This framing and duality is exactly what sponsors and contract research organizations (CROs) should keep in mind when searching for clinical trial AI. 

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Discover how AI-enabled eCOA and agentic workflows reduce clinical trial startup time, translation cycles, and meeting overhead—cutting eCOA build timelines from 16–20 weeks to under 8 weeks.

Learn how Medable enabled a top-5 pharma to scale vaccine mega-trials with near-100% enrollment, real-time safety data, and >90% diary compliance.

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How do you drive adoption in a complex women’s health study? See how ICON and Medable reached 85% eConsent uptake across 1,200+ participants with a smarter, site-first approach.

Deployment of remote and virtual clinical trial methods and technologies, referred to collectively as decentralized clinical trials (DCTs), represents a profound shift in clinical trial practice. To our knowledge, a comprehensive assessment of the financial net benefits of DCTs has not been conducted

Research looking at mobile apps and how they may provide a meaningful access point for all stakeholders for symptom management.

The clinical trials industry is at an operational inflection point. Despite decades of process refinement, drug development timelines have continued to lengthen, and manual workflows still account for a significant portion of skilled operator time across key trial functions. The emergence of large language model-based AI platforms has introduced a new variable into this equation, with multiple vendors now positioning their technology as a solution to these structural inefficiencies.

Two categories of vendors appear frequently in sponsor and CRO evaluations. The first are  general-purpose AI platforms, whose large language model capabilities have become some of the most widely benchmarked in the industry. The second are purpose-built clinical technology platforms with an established operational presence across decentralized and hybrid trial environments. 

Both have made substantive investments in life sciences AI in 2026. Their approaches, however, differ significantly in architecture, regulatory readiness, and deployment scope. This analysis examines those differences to help sponsors and CROs assess which type of platform is better suited to their operational requirements.

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Combining artificial intelligence (AI) and agentic AI with electronic Clinical Outcome Assessment (eCOA) systems fundamentally enhances how clinical trial data is collected, interpreted, and acted upon. At its core, eCOA captures structured data directly from patients, clinicians, or observers, such as symptom severity, quality of life, or functional outcomes. Modern platforms expand this further by supporting a full range of assessment types, including electronic patient-reported outcomes (ePRO), clinician-reported outcomes (eClinRO), observer-reported outcomes (eObsRO), and performance outcomes (ePerfO).

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Digital data capture has become essential to modern clinical research. Sponsors and research organizations increasingly rely on electronic outcome assessment tools to collect high quality patient data, reduce manual errors, and improve regulatory compliance.

Two terms appear frequently in this space: eCOA (electronic Clinical Outcome Assessment) and ePRO (electronic Patient Reported Outcome).

These terms are closely related. However, they are not interchangeable.

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