Clinical Trials Enter the Automation Era, But Oversight Must Keep Pace

The debut of real-time data integration between Epic and clinical trial systems at Mount Sinai Tisch Cancer Center signals a deeper shift in how leading institutions are reimagining research infrastructure, where speed, accuracy, and regulatory readiness are becoming as critical as scientific discovery itself.

With the integration of IgniteData’s Archer platform, Mount Sinai is automating structured clinical data transfer from its electronic health record (EHR) environment into external trial platforms. By using HL7® FHIR® standards, the system ensures that information flows consistently and securely without human reentry. In early implementations, the process has reduced transcription time by up to 70 percent. But the implications go far beyond efficiency. At a time when research enrollment lags, trial costs climb, and workforce fatigue intensifies, automation is becoming a structural imperative, not just a digital upgrade.

Research Operations Become a Strategic Priority

Academic medical centers have long treated data quality and trial operations as secondary to therapeutic innovation. That model is no longer sustainable. The National Cancer Institute now assesses Comprehensive Cancer Center designations based not only on scientific output but also on operational maturity, including data integrity, study oversight, and multisite coordination. Institutions that want to lead in translational research must now prove they can scale responsibly, securely, and transparently.

Mount Sinai’s new infrastructure aligns with that reality. By minimizing redundant manual entry into electronic data capture (EDC) systems, researchers can accelerate trial initiation, reduce protocol deviations, and improve compliance with regulatory timelines. This matters not just for sponsors and investigators, but for patients. Delays in data aggregation can postpone safety reviews or obscure early signals, affecting both risk management and therapeutic momentum.

The clinical trial ecosystem continues to struggle with low participation rates, particularly among underrepresented populations. According to a 2022 GAO report, only 5 to 10 percent of eligible U.S. patients enroll in trials. Operational friction remains a primary barrier. By making it easier to identify, enroll, and track participants across trial networks, real-time integration could become a foundational enabler of more inclusive research design.

Interoperability in Practice, Not Just Policy

Mount Sinai’s approach reflects the growing operationalization of FHIR, which has moved from policy framework to live infrastructure in some of the nation’s most complex research environments. Yet most institutions remain far behind. A recent study in JAMA Network Open found that fewer than 15 percent of academic health centers have real-time interoperability between EHR systems and trial platforms.

That disconnect undermines data quality and increases regulatory risk. Manual reentry introduces errors that can delay approval, trigger protocol amendments, or jeopardize funding. And with sponsors increasingly demanding near real-time visibility into patient safety data, institutions that rely on legacy workflows may struggle to remain competitive.

What makes Mount Sinai’s deployment notable is not only the technology itself, but how it repositions research operations as a frontline capability. Instead of viewing administrative efficiency as a backend concern, leaders are now treating it as a strategic function that enables scale, reduces burnout, and accelerates access to life-saving therapies.

Automation Raises, Not Lowers, the Burden of Oversight

As automated data flows become more prevalent, institutions must recalibrate how they define accuracy, auditability, and responsibility. Automation is not a synonym for infallibility. Systems that extract data directly from source records may reduce human error, but they also shift the margin of error into algorithmic pipelines that few researchers fully understand.

To ensure compliance with standards set by the Food and Drug Administration and international regulatory bodies, sites must verify not only that data are transferred, but that they are correctly mapped, de-identified where appropriate, and traceable through audit trails. Without robust data governance policies, automation can introduce silent failures that are difficult to detect until after submission.

This challenge is compounded when institutions deploy vendor platforms across multiple specialties or trial sponsors. The need for consistent metadata tagging, source attribution, and access control grows exponentially with scale. In that context, early adopters will need to invest not just in software integration, but in workforce training and process modernization to avoid introducing new points of failure.

The Workforce Implication: From Burden to Specialization

Mount Sinai leaders have been clear about the intent behind this deployment: reclaiming time for their highly specialized research workforce. As competition for research talent intensifies, administrative workload remains a primary driver of turnover and burnout. Automating routine transcription is a retention strategy.

But automation also changes the role of clinical research professionals. With less time spent on data entry, their responsibilities will shift toward data validation, protocol optimization, and participant support. This requires new training programs and job descriptions that reflect a digitally enabled research model. Without this adaptation, automation may reduce one form of burden only to replace it with another.

It also creates a strategic inflection point for health systems. Institutions that successfully integrate automation into research operations will likely be better positioned to attract complex trials, secure multisite leadership roles, and align with sponsors seeking operational excellence. Those that lag may find themselves edged out not because of their scientific expertise, but because of their infrastructural limitations.

Institutional Readiness Must Outpace Technological Capability

The integration of Epic with trial platforms is not a technical breakthrough in isolation. It is a bellwether for how clinical research is evolving toward real-time infrastructure, embedded systems, and precision oversight. Mount Sinai’s deployment is a credible signal that top-tier institutions are treating research operations with the same seriousness as clinical transformation.

But early success stories must not be mistaken for industry readiness. Most hospitals lack the data architecture, governance standards, and staffing models required to replicate these results at scale. National policy has yet to catch up. While ONC continues to support interoperability standards through certification requirements, it has not yet established formal expectations for EHR-to-EDC integration.

Until those frameworks mature, the burden of ethical deployment will fall to institutions themselves. That includes building internal review processes for automated data transfer, engaging compliance and privacy officers early in implementation, and partnering with sponsors to validate outputs across heterogeneous systems.

Mount Sinai may have set a new bar. But it is up to the rest of the ecosystem to ensure that bar does not become an outlier.