Why AI matters at this scale

Pregistry, as part of the life sciences giant Thermo Fisher Scientific, manages large-scale patient registries that collect real-world data (RWD) on disease progression, treatment patterns, and patient outcomes. This data is critical for pharmaceutical companies to demonstrate real-world effectiveness, support regulatory submissions, and secure market access. At an enterprise scale of 10,000+ employees and as a subsidiary of a $40B+ parent company, Pregistry operates with vast data inflows, stringent compliance requirements, and a need for high-velocity evidence generation. Manual data curation and analysis cannot scale to meet the volume or speed demands of modern drug development and health technology assessment. Artificial Intelligence presents a transformative lever to automate, enhance, and accelerate the entire real-world evidence (RWE) value chain, turning data complexity into a competitive advantage.

Concrete AI Opportunities with ROI Framing

1. Automated Real-World Data Abstraction & Structuring: A significant portion of registry data comes from unstructured electronic health records (EHRs) and clinical notes. Deploying natural language processing (NLP) models can automate the extraction of key variables—such as diagnoses, medications, and lab results—into structured fields. This reduces manual abstraction labor by an estimated 60-80%, cutting costs and shortening the time from data collection to analysis. The ROI is direct: faster, cheaper, and more scalable data processing for client studies.

2. Predictive Analytics for Patient Stratification and Trial Recruitment: Machine learning models trained on historical registry data can identify patients at high risk of disease progression or adverse events. This enables proactive patient management insights for clients. Furthermore, these models can match eligible registry patients to ongoing clinical trials with high precision. Improving trial recruitment efficiency directly reduces one of the most costly and time-consuming phases of drug development, offering immense value to pharmaceutical partners and creating a new service line for Pregistry.

3. AI-Powered Quality Assurance and Anomaly Detection: Ensuring data quality and integrity is paramount for regulatory-grade evidence. Unsupervised learning algorithms can continuously monitor incoming registry data to flag inconsistencies, outliers, or potential fraudulent entries in real-time. This shifts quality control from periodic manual audits to a continuous, automated process, significantly reducing rework costs and enhancing the trustworthiness of the evidence produced.

Deployment Risks Specific to Large Enterprises

Implementing AI at this scale within a large corporate structure like Thermo Fisher's ecosystem introduces specific challenges. Data Silos and Integration Complexity: Legacy systems across different business units and acquired companies can create fragmented data landscapes, making it difficult to build unified AI models. Regulatory and Compliance Hurdles: Any AI tool handling protected health information (PHI) must meet HIPAA, GDPR, and evolving FDA guidelines on algorithm transparency. "Black box" models may be unacceptable for regulatory submissions. Change Management at Scale: Rolling out new AI-driven workflows requires training thousands of employees, overcoming resistance to change, and aligning incentives across a vast organization, which can slow adoption and dilute ROI. Vendor Lock-in and Strategic Alignment: Choosing an AI platform (e.g., a specific cloud provider's tools) must be weighed against corporate IT strategy, potentially limiting flexibility.