Automatic Normalization of Anatomical Phrases in Radiology Reports Using Unsupervised Learning.

In today's radiology workflow, free-text reporting is established as the most common medium to capture, store, and communicate clinical information. Radiologists routinely refer to prior radiology reports of a patient to recall critical information for new diagnosis, which is quite tedious, time consuming, and prone to human error. Automatic structuring of report content is desired to facilitate such inquiry of information.

Automating data abstraction in a quality improvement platform for surgical and interventional procedures.

Objective: This paper describes a text processing system designed to automate the manual data abstraction process in a quality improvement (QI) program. The Surgical Care and Outcomes Assessment Program (SCOAP) is a clinician-led, statewide performance benchmarking QI platform for surgical and interventional procedures. The data elements abstracted as part of this program cover a wide range of clinical information from patient medical history to details of surgical interventions.