Natural Language Processing and the Oncologic History: Is There a Match?

摘要

Purpose:The widespread adoption of electronic health records (EHRs) is creating rich databases documenting the cancer patient's care continuum. However, much of this data, especially narrative “oncologic histories,” are “locked” within free text (unstructured) portions of notes. Nationwide incentives, ranging from certification (Quality Oncology Practice Initiative) to monetary reimbursement (the Health Information Technology for Economic and Clinical Health Act), increasingly require the translation of these histories into treatment summaries for patient use and into tools to assist in transitions of care. Unfortunately, formulation of treatment summaries from these data is difficult and time-consuming. The rapidly developing field of automated natural language processing may offer a solution to this communication problem.