Measuring adherence to a Choosing Wisely recommendation in a regional oncology clinic.

196 Background: Natural language processing (NLP) has the potential to significantly ease the burden of manual abstraction of unstructured electronic text when measuring adherence to national guidelines. We incorporated NLP into standard data processing techniques such as manual abstraction and database queries in order to more efficiently evaluate a regional oncology clinic’s adherence to ASCO’s Choosing Wisely colony stimulating factor (CSF) recommendation using clinical, billing, and cancer registry data. Methods: Database queries on the clinic’s cancer registry yielded the study population of patients with stage II-IV breast, non-small cell lung (NSCL), and colorectal cancer. We manually abstracted chemotherapy regimens from paper prescription records. CSF orders were collected through queries on the clinic’s facility billing data, when available; otherwise through a custom NLP program and manual abstraction of the electronic medical record. The NLP program was designed to identify clinical note text containing CSF information, which was then manually abstracted. Results: Out of 31,725 clinical notes for the eligible population, the NLP program identified 1,487 clinical notes with CSF-related language, effectively reducing the number of notes requiring abstraction by up to 95%. Between 1/1/2012-12/31/2014, adherence to the ASCO CW CSF recommendation at the regional oncology clinic was 89% for a population of 322 patients. Conclusions: NLP significantly reduced the burden of manual abstraction by singling out relevant clinical text for abstractors. Abstraction is often necessary due to the complexity of data collection tasks or the use of paper records. However, NLP is a valuable addition to the suite of data processing techniques traditionally used to measure adherence to national guidelines.

Duke Scholars

Author Gary Herbert Lyman Medicine, Medical Oncology