The analysis of Real-World Data (RWD) must go beyond traditional claims data and clinical codes. Details of a patient’s characteristics and clinical facts also reside within unstructured clinical notes and text in the Electronic Health Record (EHR), molecular reports, and other sources. Automated processes to abstract and normalize clinically relevant information is essential for patient care, research, and new therapeutic discoveries.
Today, valuable health data is found only by manually pouring through volumes of unstructured data, driving higher costs of analysis.
MetiStream automates the process of building analytics and research-ready data assets at scale from unstructured RWD with the right context, relationships, and format using AI and NLP. Accelerate opportunities to increase the utility of clinical documents to improve care, commercialize new data solutions, and drive new therapeutic discoveries and innovations.
Enrich existing data to build a holistic
view of the patient.
Leverage the platform's features to build advanced analytics and models.
Verify and link source documents to key
clinical entities and concepts.
Organizations continue to face challenges in integrating, extracting and normalizing molecular data across various clinical and genomics reports.
Analysis of biomarkers has become increasingly important to assess risk and deliver personalized treatments to patients. The lack of a standard format, variability in data sources and the volume of unstructured text in the reports adds significant time and cost in analyzing and utilizing valuable data. The ability to automate, extract and correlate biomarker data with clinical data is critical in advancing therapeutic discoveries.
Deliver enhanced personalized
treatments and therapies.
Identify patients based on molecular characteristics for clinical trials and studies.
Advance clinical and therapeutic discovery with better evidence-based analysis.