A global healthtech firm accelerates clinical trial protocol reviews

A global healthtech firm accelerates clinical trial protocol reviews

A global clinical research and healthcare analytics firm faced critical bottlenecks in clinical trial protocol reviews that delayed regulatory submissions and treatment approvals. Modus Create built an AI-powered application on Amazon Web Services (AWS) that streamlines protocol optimization, automates source data verification, and delivers real-time clinical insights. As a result, the healthtech firm doubled analyst productivity and reduced protocol review timelines from weeks to hours, accelerating time-to-market for life-saving treatments.

OUR WORK INVOLVED

• User research and clinical workflow analysis
• End-to-end experience design
• Intelligent app development
• Clinical NLP core with Amazon SageMaker
• AI training with AWS Trainium
• Low-latency processing with AWS Inferentia
• High-performance computing with EC2 UltraClusters
• GxP compliance implementation

Life sciences organizations manage staggering amounts of data to bring new drugs and treatments to market. A single Phase III clinical trial can generate over 3.5 million data points. The sheer complexity and length of these trials can significantly drive up the cost of treatments.

Since AI went mainstream, one of its most promising applications has been speeding up clinical trials. If AI can help develop, test, and launch drugs faster and with greater precision, it could fundamentally strengthen healthcare delivery.

That shift is already happening. Case in point: One global leader in health data analytics and clinical research services partnered with Modus Create to develop an AWS-poweredAI product to accelerate trial timelines and make life-saving treatments more affordable.

Challenge

Breaking bottlenecks in protocol reviews

Every clinical trial starts with a clinical trial protocol-a detailed document detailing eligibility, dosing, endpoints, and methodology.

For this healthtech firm, analysts had to cross-reference each attribute against historical trial data, regulatory requirements, and operational constraints. Their legacy review process relied on fragmented tools never designed for clinical development workflows. Teams juggled spreadsheets, static PDFs, and disconnected databases, forcing analysts to perform manual endpoint extraction, repetitive feasibility assessments, and protocol deviation analysis.

The lack of intelligent automation made identifying compliance gaps, protocol amendment risks, and optimization opportunities both time-consuming and error-prone.

Moreover, protocol reviews that should take days stretched into weeks, delaying regulatory submission timelines and ultimately postponing patient access to critical treatments.

With 80% of clinical trials failing to meet enrollment targets and average development costs reaching $2.6 billion per approved therapy, the organization needed a digital product that could accelerate clinical development cycles while maintaining regulatory compliance standards.

Solution

An intelligent app to expedite clinical development timelines

The healthtech firm wanted to expedite clinical trial protocol reviews by developing an AI-powered application. Modus Create’s experience with AWS healthcare services and regulatory compliance frameworks made us the right partner for this mission-critical implementation.

1. Researching existing workflows

We began by observing how analysts managed their routine operations. The research revealed that manual data lookups, repetitive text searches, and inconsistent protocol formatting slowed every step of the protocol review process.

These bottlenecks helped us understand where automation can have the greatest impact. Everyone agreed that the new AI-powered platform we were to engineer must be able to accept multiple document types, adapt to regional data formats, and deliver actionable insights while preserving established workflows.

2. Bringing AI to protocol reviews

Next, we worked on an NLP core built on Amazon SageMaker. The team analyzed millions of historical trial protocols to structure key details like endpoints, eligibility, and dosing. A pre-trained BioClinicalBERT model, developed using MIMIC-III clinical notes and fine-tuned with internal datasets, added domain-specific accuracy.

This progression turned a static review process into one capable of automatically detecting gaps, conflicts, or unusual deviations that could influence trial outcomes.

3. Scaling insights in real time

The intelligence layer required both speed and scale to be effective. We deployed inference endpoints on AWS Inferentia to deliver low-latency outputs, giving analysts actionable insights within seconds of uploading a protocol. In parallel, Amazon EC2 UltraClusters enabled large-scale, distributed processing of millions of protocol pages and historical datasets.

This high-performance environment reduced tasks that once took weeks to just hours, while advanced cross-document comparisons created a more comprehensive and consistent review process across trials.

4. Developing a compliant, future-proof healthtech product

The team embedded GxP compliance into the solution from the start, aligning workflows, audit trails, and data handling with stringent regulatory standards. We used AWS Trainium to provide cost-efficient training for fine-tuning NLP models, enabling faster experimentation without inflating infrastructure costs.

The team also enabled AI-driven coding suggestions with Amazon CodeWhisperer. This helped developers rapidly implement data pipelines, interface components, and integration scripts.

Together, these capabilities created a secure, compliant healthtech platform that was ready to integrate with real-world data sources.

5. Driving stickiness with GitHub Copilot

Modus Create’s Training and Enablement team partnered with key stakeholders of the healthtech firm to design hands-on sessions aligned with both business goals and budget. Drawing on our experience scaling enablement across large teams, we delivered targeted 101 (beginner) and 201 (intermediate) workshops. We then followed up with an ROI session to help teams translate their performance, usage, and priorities into actionable metrics.

Impact

Faster delivery of critical life-saving treatments

The AI-powered application we developed fundamentally transformed the way the healthtech organization conducts protocol reviews. Some of the measurable benefits include:

• Analyst productivity doubled: Analysts can now review twice as many protocols within the same timeframe.
• Review timelines reduced by 50%: Protocol evaluations that used to take weeks are now completed in hours.
• Error rate reduction: Automated data extraction and comparison reduced manual errors, cutting the error rate from 12% to just 0.8%.
• Faster protocol amendment detection: Early identification of potential compliance gaps and operational constraints reduced costly late-stage modifications by 43%.
• Improved patient recruitment feasibility:AI-powered analysis optimized inclusion/exclusion criteria and enrollment strategies, decreasing screen failure rates from 65% to 41%.
• Streamlined regulatory submission preparation: Structured protocol insights accelerated regulatory documentation workflows, cutting submission preparation time by 35%.

The platform’s scalability ensures it can support increasing client demand while maintaining quality and efficiency. Its architecture is future-ready, facilitating integration with real-world data sources and additional tools to position the firm for growth in a competitive clinical research market.