Summary
AI-powered, unified digital platforms are streamlining non-interventional studies by removing site dependencies, accelerating study start-up, and enabling continuous patient participation through direct engagement and EHR integration. These approaches improve data completeness, reduce participant burden, and support long-term retention. With growing FDA guidance on AI in real-world evidence generation, this model offers clinical operations teams a scalable, patient-centric alternative to traditional site-based research.
Non-interventional studies complement clinical trials, playing an essential role in understanding long-term treatment safety and efficacy and deepening our knowledge of diseases. Yet despite their fundamentally different purpose, most non-interventional studies rely on site-based methods originally designed for interventional trials, introducing the same stakeholder burdens and inefficiencies that trials have faced for decades.
Just a few years ago, decentralized clinical trials (DCTs) promised to solve these trial challenges, reducing both patient burden and dependencies on sites. But DCTs struggled to deliver, as trials proved too complex for the initial DCT approach.
While DCTs failed to transform clinical research, artificial intelligence (AI)-powered study approaches are now fulfilling their promise within the non-interventional research space. Unified, digital platforms are making non-interventional studies more accessible and efficient, and creating seamless, patient-centric research experiences.
The burden clinical trial methods bring to non-interventional research
Using site-based models for non-interventional studies introduces unnecessary obstacles for researchers, patients, and sites. Collecting data through site visits incurs extensive resourcing and costs. This method typically limits data collection to data generated at an individual site or network, creating data gaps that are particularly impactful for non-interventional research, which often seeks to understand real-world treatment patterns and efficacy.
Patients carry increased burdens as well, often traveling long distances to participate in studies that may last a few years or more than a decade. Cell and gene therapy studies, for example, face mandated follow-up requirements that can last for up to 15 years andrequire repeated site visits—a substantial burden that frequently leads to study dropout and reduced data collection. Successful non-interventional research needs methods that work for patients, sites, and researchers in the real world.
Why AI-powered approaches work for non-interventional research
Remote, AI-powered research methodologies offer a more efficient, effective option that results in improved participation rates and data quality. By removing the dependency on sites, study start-up is faster, patients can enroll and participate from anywhere, and research can be informed by comprehensive patient journeys.
Complete medical record access
Key to the success of the most effective AI-powered methods is their ability to collect patient medical histories from electronic health records across all sites of care, avoiding data gaps that can come from site-based research. This comprehensive collection provides researchers with 360-degree views of patient journeys, including:
- Retrospective medical histories from all care providers
- Ongoing treatment patterns and outcomes
- Real-world medication adherence and side effects
- Healthcare utilization across different settings
The ability to retrieve complete patient records enables the abstraction of all data relevant to research questions without traditional site visits. AI algorithms can identify key data in vast sets of records that could take clinician-years to abstract manually. This enhanced data collection and abstraction leads to faster, deeper research insights.
Direct patient engagement for active data collection
Patient-centric AI-powered approaches also enable researchers to go directly to patients to collect and integrate ongoing prospective data, like patient-reported outcomes and wearable device data. Patients participate through user-friendly platforms that require little to no disruption of their daily routines, resulting in long-term engagement and data collection.
Rather than losing patients to travel barriers or scheduling conflicts, researchers maintain continuous engagement throughout study durations—even across multi-year timeframes.
AI enables comprehensive non-interventional research
Unified technology platforms eliminate the integration challenges that studies often face. Rather than cobbling together multiple point solutions, these platforms provide end-to-end study management within a single unified system. These platforms coordinate patient consent, enrollment, medical record collection, and outcome measurement through integrated workflows that streamline rather than complicate research processes.
The result is improved data quality and efficient operations that benefit all stakeholders. Patients participate more easily in research that matters to them, and sponsors generate high-quality evidence while avoiding the costs, inefficiencies, and administrative burdens of traditional site-based research.
FDA guidance and future implications of AI
Recognizing that powerful AI tools can streamline real-world evidence research and workflows, the FDA issued guidance earlier this year for sponsors on using AI to produce evidence that supports regulatory decision-making regarding drug safety, effectiveness, and quality.
Former FDA Commissioner Robert M. Califf, MD, said: “With the appropriate safeguards in place, artificial intelligence has transformative potential to advance clinical research and accelerate medical product development to improve patient care.” The agency noted that regulatory submissions increasingly incorporate AI due to its ability to process and analyze large datasets while providing predictions and insights for clinical and manufacturing decisions.
Given AI’s rapid advancement and its growing role in non-interventional studies, data collection, and evidence generation, further FDA guidance on evaluating and validating these technologies will prove essential. This includes ensuring transparency throughout study design, conduct, and reporting, plus establishing quality metrics that address bias and model drift.
But one thing is clear: AI’s role in research will only expand. The FDA welcomes AI as a powerful catalyst for more efficient, effective, and innovative research. AI will finally help fulfill the promise of DCTs and deliver sophisticated digital infrastructure for capturing data. This includes advanced electronic patient-reported outcome platforms, telehealth capabilities, patient-initiated sample collection, and intelligent wearable technologies.
Fulfilling the original DCT promise
Unified non-interventional research platforms, inspired by the promise of DCTs, deliver the patient-centric, efficient, and comprehensive studies that DCTs could not achieve.
AI represents one of the most significant technological advancements in recent years, with profound implications for healthcare research. These innovations will not only democratize study participation and representativeness by reducing systemic barriers, but also dramatically enhance research efficiency and data granularity. Remote data collection and reduced burden on sites enable a new era of non-interventional research that is more efficient, effective, and patient-centric.
The future of research lies in embracing unified technologies that maximize the unique advantages of non-interventional study designs, finally delivering on the transformative potential that DCTs envisioned but never fully realized.
Troy Astorino, co-founder and CTO of PicnicHealth
