Enterprise-ready harmonization engine delivers AI-ready clinical data for faster cohort discovery, trial screening, feasibility analysis, and ongoing study execution.

SEQSTER PDM, Inc. (“SEQSTER”), the leading healthcare technology company and the data connection, collection, and orchestration layer for patient health data, today announced the launch of 1-Click Data Refinery™, an enterprise-grade data harmonization engine designed for pharmaceutical companies, contract research organizations, and healthcare enterprises. The solution transforms raw, patient-consented EHR data into clean, structured, AI-ready patient records that support rapid model training, real-time inference, and production-scale deployment.

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SEQSTER Launches 1-Click Data Refinery™ + AI

Life sciences companies are investing heavily in AI to speed up trials and improve decisions, but many efforts fail to scale because the real constraint is not the model, it is the data. Clinical data from EHR systems, even in FHIR or CCDA formats, is built for record exchange, not analysis. It is often bloated, repetitive, and inconsistent across vendors, with the meaningful clinical insight buried inside technical markup. Without converting that raw data into clean, consistent, high-signal patient datasets, AI programs become costly, slow to deploy, and difficult to scale across large populations.

SEQSTER’s 1-Click Data Refinery™ addresses this challenge by refining and orchestrating clinical data at the source. The platform normalizes and deduplicates raw EHR records across health systems and clinical notes, producing longitudinal, patient-centric data representations that AI systems can immediately consume. This enables organizations to deploy AI faster, reduce data engineering overhead, and improve confidence in AI-driven outputs used in regulated clinical environments.

Sean White, CEO of Inflection AI, stated, "In the AI era, everyone has access to powerful foundation models, but the organizations that win will be those with the cleanest, most reliable data pipelines. SEQSTER's 1-Click Data Refinery addresses the fundamental bottleneck we see across healthcare AI: raw clinical data that's technically interoperable but practically unusable for machine reasoning. By refining EHR data at the source, SEQSTER is solving the 'garbage in, garbage out' problem that has stalled so many promising AI initiatives in clinical research. This is exactly the kind of infrastructure the industry needs to move from AI pilots to production-scale deployment."

The unique product offering is SEQSTER’s data readiness at scale, built on more than 10 years of production experience refining real-world CCDA and FHIR across diverse EHR environments. 1-Click Data Refinery converts patient-consented records into harmonized longitudinal patient representations, with the structure and provenance needed for reliable retrieval and inference.

“Teams run into scalability issues when they make AI ingest directly from raw CCDA and FHIR exports,” said Xiang Li, PhD, Chief Technology Officer of SEQSTER. “The clinically meaningful signal is often a small fraction of the total record. Our data refinery distills that complexity into structured longitudinal data so AI can reason over the full record, rather than spend tokens parsing noise and hit context window limits.”

ABOUT SEQSTER

SEQSTER is the leading healthcare technology company that connects, collects, and refines patient-consented health data across care settings into a unified, longitudinal patient view.

With 150 million patients in its regulatory-grade platform, life sciences companies can accelerate research, AI developers can train higher-quality models, and patients can gain meaningful insights to better manage their health, all from consented, standardized health data.

Founded in 2016, SEQSTER is shaping a new era in healthcare by connecting patients, data, and AI to enable faster, smarter, and more trustworthy clinical and research decisions.

Learn more: info@seqster.com | www.seqster.com

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By refining EHR data at the source, SEQSTER is solving the 'garbage in, garbage out' problem that has stalled so many promising AI initiatives in clinical research.