The majority of health and patient data is stored today as unstructured medical text, such as medical notes, prescriptions, audio interview transcripts, and pathology and radiology reports. Identifying this information today is a manual and time consuming process, which either requires data abstraction by highly skilled medical experts, or teams of developers writing custom code and rules to try and extract the information automatically. In both cases this undifferentiated heavy lifting takes material resources away from efforts to improve patient outcomes through technology.
Working closely with Seattle’s own Fred Hutchinson Cancer Research Center, known as Fred Hutch to Seattleites, we implemented the new Amazon Comprehend Medical – a new HIPAA-eligible machine learning service allowing developers to process unstructured medical text and identify information such as patient diagnosis, treatments, dosages, symptoms and signs, and more, in addition to relations associated (e.g., which condition is associated with medication, or the condition is part of a family history, or a patient stopped taking a particular medication, etc.) – to support their goals to eradicate cancer in the future. Comprehend Medical is helping to identify patients for clinical trials who may benefit from specific cancer therapies. Fred Hutch was able to evaluate millions of clinical notes to extract and index medical conditions, medications and choice of cancer therapeutic options, reducing the time to process each document from hours, to seconds.
“Curing cancer is, inherently, an issue of time,” said Matthew Trunnell, chief information officer, Fred Hutchinson Cancer Research Center. “For cancer patients and the researchers dedicated to curing them, time is the limiting resource. The process of developing clinical trials and connecting them with the right patients requires research teams to sift through and label mountains of unstructured medical record data. Amazon Comprehend Medical will reduce this time burden from hours per record to seconds. This is a vital step toward getting researchers rapid access to the information they need when they need it so they can find actionable insights to advance lifesaving therapies for patients.”
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