Mount Sinai Slashes Trial Data-Entry Time by 55% with Archer Tech

Why Manual EHR Entries Stalled Cancer Progress

Historically, clinical trials have been plagued by the 'data-entry bottleneck.' Research coordinators would spend hours manually transcribing patient information from EHR systems into separate EDC databases for sponsors. This process is prone to human error and creates a massive administrative burden that distracts from core clinical work. In a high-stakes environment like the Tisch Cancer Center, where patients are often battling aggressive malignancies, this inefficiency is more than a nuisance—it is a barrier to rapid progress.

Experts noted that manual transcription was the primary cause of 'queries,' where trial sponsors flag data discrepancies for correction. These queries can delay trial timelines by weeks or months, costing millions in development funds and preventing vital drugs from reaching the market.

The reliance on manual entry has been a persistent issue across global research hubs, including those in India, where clinical trial operations are expanding rapidly.

By automating this, Mount Sinai has addressed the core cause of administrative friction.

The integration of the Archer platform into Epic allows for a 'push-button' data transfer, ensuring that the clinical reality captured in the hospital system is identical to the trial data submitted to regulators.

This eliminates the need for redundant documentation, a major pain point for medical staff globally.

Archer and Epic: The Code Behind the Efficiency

The technical architecture behind this development is centered on the Archer platform's ability to 'speak' the language of the Epic EHR. In many hospital settings, these systems operate in siloes, requiring specialized middleware or manual intervention to bridge the gap. Archer acts as an intelligent layer that sits between the hospital's patient database and the trial's EDC system.

When a patient visits for a trial-related check-up, the software identifies the relevant clinical data points—such as blood pressure, weight, or lab results—and maps them directly to the corresponding fields in the trial's electronic case report forms.

Alex Lieberman-Cribbin, Clinical Trials Manager at the Icahn School of Medicine at Mount Sinai, confirmed that the platform is transforming the operational landscape for research teams.

'This technology is making a real difference in the day-to-day work of clinical research teams by allowing them to focus more on study coordination, patient care, and efficient trial operations,' Lieberman-Cribbin said.

The system uses sophisticated validation rules to ensure that the data being transferred meets the strict requirements of trial protocols. If a data point is missing or inconsistent, the system flags it in real-time, allowing staff to correct it immediately while the patient is still in the clinic.

This proactive approach is why the re-query rate has plummeted to zero, a metric that is virtually unheard of in traditional manual trial management.

The Global Ripple Effect on Clinical Research

The success at Mount Sinai is likely to trigger a wider adoption of automated data transfer technologies in major medical centers worldwide. For countries like India, which has become a significant destination for global clinical trials, the implications are profound. Indian contract research organizations (CROs) and major hospital chains, such as Apollo Hospitals or Max Healthcare, could significantly improve their competitiveness by adopting similar integrated EHR-to-EDC solutions.

As the Indian pharmaceutical sector looks to increase its participation in global drug trials, the ability to provide high-quality, real-time data is essential.

Regulators, including the Central Drugs Standard Control Organization (CDSCO), are increasingly emphasizing the need for robust digital infrastructure in clinical research.

The Mount Sinai model provides a blueprint for how this can be achieved without disrupting existing hospital workflows.

While the cost of implementing such systems is an initial consideration, the long-term savings in staff time and the reduction in trial delays offer a clear return on investment.

Industry analysts noted that the shift toward automated data workflows is inevitable, driven by the need for faster drug development cycles and the increasing complexity of modern oncology treatments.

As data becomes the most valuable asset in medical research, the institutions that can capture it most accurately and efficiently will lead the field.

What Comes Next for Zero-Query Clinical Trials

Looking ahead, the next phase for the Tisch Cancer Center and similar institutions will be to expand the scope of automation beyond routine data points. Currently, the Archer platform handles structured data effectively, but the goal is to integrate unstructured data, such as physician notes or imaging reports, into the automated pipeline. This would further reduce the burden on research coordinators and provide a more holistic view of patient outcomes during trials.

The success of this implementation also opens the door for 'decentralized' clinical trials, where patients can participate in studies from their own homes. With secure, automated data transfer, the need for frequent site visits could be reduced, making trials more accessible to a broader patient population.

However, this transition requires maintaining the highest standards of data security and patient privacy.

As these systems become more prevalent, the focus will shift from the technology itself to the standardization of data protocols across different hospital systems.

The ultimate vision is a global research network where data flows from the patient's bedside to the researcher's monitor in real-time, regardless of the hospital or the country.

With the proof-of-concept established at Mount Sinai, the path toward a more efficient, accurate, and patient-centric clinical research model is clearer than ever.

The industry will be watching closely as these results are replicated in other therapeutic areas beyond oncology, potentially setting a new gold standard for the entire medical research community.

Operational Realities and Future Scalability

The implementation at Mount Sinai was not without its challenges, primarily involving the mapping of data fields between the hospital's complex Epic EHR and the specific requirements of various clinical trial protocols. Each trial has unique data needs, and ensuring that the Archer platform correctly interprets these requirements across different studies was a major technical undertaking.

Officials said that the research team spent months refining the mapping and validation rules to ensure that the automation did not introduce new errors.

This level of meticulous preparation is what allowed for the 100 percent accuracy rate observed in the initial rollout.

As the team continues to scale this technology to more trials, they plan to build a library of standardized data maps that can be reused, further speeding up the setup time for future projects.

This modular approach is key to making the system sustainable as the volume of trials increases.

The feedback from the research staff has been overwhelmingly positive, with many reporting a significant reduction in the stress associated with data entry deadlines.

By removing the repetitive, manual aspects of the job, the hospital is not only improving efficiency but also helping to retain its highly skilled research personnel.

In an era where clinical research talent is in high demand, this is a significant competitive advantage.

The Mount Sinai experience serves as a definitive case study for how technology can be used to solve systemic problems in medical research, proving that even in complex environments, automation can deliver tangible, measurable benefits.