Clinical natural language processing is ideally suited for the large amounts of unstructured text found in clinical notes and reports. NLP systems "categorize and structure it according to standard nomenclature – in this case focusing on terms used in a range of medical specialties – that will ultimately speed data searches for both diagnoses and medical research. These NLP platforms or “pipelines” aid indexing and searching electronic medical records within institutions to quickly find similar cases or conditions, so physicians are not reliant solely on their own clinical experience in analyzing a problem. Researchers may also use these tools to aid retrospective epidemiological studies or do groundwork for new clinical trials."
The two NLP solutions include clinical notes and pathology reports.
The teams developed methods for extracting information from over 20 million unstructured clinical notes. Physicians can "mine the text for references of specific conditions, drugs, diseases, signs and symptoms; anatomical areas or organs; or treatment procedures."
In addition, they focused on unstructured pathology reports to be able to mine cancer disease characteristics. "The system extracts tumor characteristics, lymph node status and metastatic disease information enabling the automatic computation of cancer stage, which is critical to determine optimal treatment."
Mayo reporting realizing the following benefits:
"Physicians can research past records to examine earlier cases of rare conditions, thereby “conferring” with their colleagues across time to aid diagnosis and treatment decisions.
Retrospective studies of tissue samples can propel new research findings, as happened with a major breast cancer finding at Mayo in 2008.
Enhanced ability to mine data and determine potential study factors or participants has already enabled individualized medicine treatments in psychiatric care."