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Identifying and correlating clinical data points to determine treatment effectiveness and assess a patient’s outcome is very complex and manually intensive.
Accelerate outcomes analysis using AI and NLP to identify, transform and correlate unstructured clinical data across a single patient or a cohort of patients. Conduct analysis of treatment response, progression, side effects, biomarkers, and overall survival using valuable RWD captured within clinical documents, such as doctor notes, pathology, radiology, genomics reports, and more.
Evaluate therapeutic outcomes based on specific patient cohorts.
Analyze specific side effects and events that impact performance.
Trace the lineage of the clinical fact to the source document.
Lung cancer is the leading cause of cancer deaths in the United States, resulting in more than 150,000 annual deaths. Unfortunately, only 5% of the adults eligible for screening receive a Low Dose CT (LDCT) scan.
MetStream’s lung cancer screening solution quickly processes and analyzes unstructured clinical data, along with structured data, to identify patient cohorts eligible for LDCT screening. Our solution uses AI and NLP technology to determine smoking status, applicable quit date, and pack years to pinpoint the right cohort of patients to take action based on USPSTF guidelines
Create a list of patients for LDCT screening
to conduct outreach.
Stratify patients based on smoking status, pack years, and demographics.
Analyze symptoms and risk factors to determine care pathways.
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