CancerLinQ, a learning health system product of the American Society for Clinical Oncology (ASCO), provides subscribing practices with innovative analytic tools to enable them to improve quality of care to cancer patients using Real World Data (RWD).
It does this by extracting structured data from Electronic Health Records (EHRs) and creating a standards-based, EHR-agnostic view of patient records.
One challenge facing Real World Data is “missingness”, caused by important clinical attributes existing only in unstructured clinical notes and/or scanned documents.
This talk will focus on the challenges of extracting knowledge from unstructured data, CancerLinQ’s efforts to date, and the opportunities for using Artificial Intelligence/Machine Learning to move from Real World Data to physician usable knowledge.
