Clario’s Generative AI Solution for Clinical Research on AWS

Introduction

Clinical research is a data‑intensive endeavor that relies heavily on the accurate interpretation of patient‑reported outcomes (PROs). One of the most time‑consuming tasks in this domain is the analysis of COA (clinical outcome assessment) interview transcripts. Traditionally, researchers spend weeks transcribing, coding, and extracting insights from these qualitative datasets, a process that is not only laborious but also prone to human error. In recent years, generative AI has emerged as a powerful tool for automating natural language processing tasks, yet many organizations still struggle to translate these advances into production‑ready solutions.

Clario, a leading provider of clinical research analytics, has addressed this challenge head‑on by building an end‑to‑end AI platform on Amazon Web Services (AWS). By combining Amazon Bedrock—a managed service that gives instant access to foundation models—with a suite of complementary AWS services, Clario has created a system that automatically processes COA interview transcripts, generates structured insights, and delivers them to researchers in real time. This post walks through the architecture, the key components, and the tangible benefits that Clario’s solution brings to the clinical research community.

The Challenge of COA Interview Analysis

COA interviews capture nuanced patient experiences that are essential for regulatory submissions and product development. However, the qualitative nature of these conversations means that each transcript can contain hundreds of sentences, each with subtle emotional cues, medical terminology, and contextual references. Manual coding requires domain experts to read through entire transcripts, annotate themes, and reconcile discrepancies across multiple reviewers. Even with a small team, this process can take several days per interview.

Beyond the sheer volume of data, the variability in interview styles—different interviewers, patient demographics, and recording quality—introduces additional complexity. A robust solution must therefore handle noisy audio, diverse linguistic patterns, and the need for consistent, reproducible coding across large datasets.

Architecting a Generative AI Workflow on AWS

Clario’s architecture is built around a modular pipeline that leverages AWS’s serverless and managed services to keep operational overhead low while maintaining high scalability. The core workflow can be broken down into five stages:

1. Ingestion – Audio files are uploaded to an Amazon S3 bucket. An S3 event triggers a Lambda function that initiates the transcription process.
2. Transcription – Amazon Transcribe Medical, tuned for clinical terminology, converts speech to text. The resulting transcript is stored back in S3.
3. Pre‑processing – A second Lambda function cleans the transcript, removes filler words, and segments the text into logical units for downstream analysis.
4. Generative Analysis – Amazon Bedrock hosts a fine‑tuned foundation model that performs entity extraction, sentiment analysis, and thematic coding. The model outputs structured JSON that maps each segment to predefined COA categories.
5. Post‑processing & Delivery – The final JSON is enriched with metadata, stored in DynamoDB, and exposed via an API Gateway endpoint that researchers can query from their dashboards.

Each stage is orchestrated by AWS Step Functions, which provides visual monitoring, error handling, and the ability to retry failed steps without manual intervention.

Amazon Bedrock: The Core of the Solution

At the heart of Clario’s automation is Amazon Bedrock, a managed service that gives developers instant access to multiple foundation models from leading AI vendors. Bedrock’s key advantages for this use case are:

• Rapid Model Access – Researchers can choose from models such as Anthropic’s Claude or Meta’s Llama, each offering different strengths in language understanding and domain adaptation.
• Fine‑Tuning Capabilities – Clario has fine‑tuned a Bedrock model on a proprietary dataset of annotated COA transcripts, enabling the model to recognize domain‑specific terminology and coding conventions.
• Scalable Inference – Bedrock handles request scaling automatically, ensuring that even a surge of simultaneous interview analyses does not degrade performance.
• Security & Compliance – All data processed through Bedrock remains within the AWS ecosystem, allowing Clario to meet HIPAA and GDPR requirements without exposing sensitive patient information to external services.

The Bedrock inference step receives a cleaned transcript segment and a prompt that instructs the model to extract key entities, assign sentiment scores, and map the content to COA categories. The model’s output is a JSON object that can be directly ingested by downstream services.

Integrating AWS Services for End‑to‑End Automation

While Bedrock handles the heavy lifting of natural language understanding, the surrounding AWS services ensure that the pipeline is robust, secure, and maintainable.

• Amazon S3 stores raw audio, transcripts, and final analysis results. S3’s versioning feature allows Clario to track changes and roll back if necessary.
• AWS Lambda functions provide lightweight compute for orchestration and data transformation without the need for dedicated servers.
• Amazon DynamoDB offers a low‑latency, NoSQL database for storing structured analysis results, enabling quick retrieval for dashboards and reporting tools.
• Amazon API Gateway exposes a RESTful interface that researchers can call to fetch analysis results, ensuring that the system can be integrated into existing clinical trial management platforms.
• AWS Step Functions orchestrate the entire workflow, providing visibility into each step, automatic retries on failure, and the ability to pause or resume the pipeline.
• AWS CloudWatch and AWS X-Ray give telemetry and tracing, allowing Clario’s DevOps team to monitor performance, identify bottlenecks, and maintain compliance with audit requirements.

By leveraging these services, Clario has eliminated the need for on‑premises servers, reduced operational costs, and achieved near real‑time turnaround times for COA interview analysis.

Real‑World Impact on Clinical Research

The adoption of Clario’s AI‑powered pipeline has yielded measurable benefits for clinical research teams. In a recent pilot study involving 120 COA interviews, the automated system reduced analysis time from an average of 48 hours per interview to under 4 hours, a 90% reduction in turnaround. Researchers reported higher confidence in the consistency of coding, as the model applied the same rules across all transcripts. Additionally, the structured JSON output enabled seamless integration with statistical analysis software, allowing teams to generate insights faster and focus on hypothesis testing rather than data wrangling.

Beyond speed, the solution also improves data quality. By automating the extraction of entities and sentiment, the system reduces human bias and ensures that rare but clinically significant themes are not overlooked. The ability to audit each step of the pipeline also satisfies regulatory scrutiny, a critical factor for studies that will inform drug approvals.

Future Directions and Scalability

Clario plans to extend the platform to support additional qualitative data types, such as focus group recordings and open‑ended survey responses. The modular architecture makes it straightforward to plug in new Bedrock models or fine‑tune existing ones for different clinical domains.

Scalability is baked into the design. Because the pipeline is serverless, it can automatically handle spikes in interview volume—such as those that occur during multi‑site trials—without manual provisioning. Moreover, Clario is exploring the use of Amazon SageMaker for model monitoring and drift detection, ensuring that the foundation model remains accurate as new data arrives.

Conclusion

Clario’s integration of Amazon Bedrock with AWS’s serverless ecosystem demonstrates how generative AI can be harnessed to transform clinical research workflows. By automating the laborious task of COA interview analysis, the platform delivers faster, more reliable insights that accelerate the drug development cycle. The result is a scalable, secure, and cost‑effective solution that empowers researchers to focus on the science rather than the data processing.

Call to Action

If you’re involved in clinical research and are looking to reduce the time and effort spent on qualitative data analysis, consider exploring how generative AI on AWS can help. Reach out to Clario today to schedule a demo and discover how our Bedrock‑powered pipeline can be tailored to your study’s unique needs. Embrace the future of clinical analytics and unlock deeper insights faster than ever before.