Exploring the frontier of electronic health record surveillance: the case of postoperative complications.

Background: The aim of this study was to build electronic algorithms using a combination of structured data and natural language processing (NLP) of text notes for potential safety surveillance of 9 postoperative complications.

Methods: Postoperative complications from 6 medical centers in the Southeastern United States were obtained from the Veterans Affairs Surgical Quality Improvement Program (VASQIP) registry. Development and test datasets were constructed using stratification by facility and date of procedure for patients with and without complications.

Automated identification of postoperative complications within an electronic medical record using natural language processing.

Context: Currently most automated methods to identify patient safety occurrences rely on administrative data codes; however, free-text searches of electronic medical records could represent an additional surveillance approach.

Objective: To evaluate a natural language processing search-approach to identify postoperative surgical complications within a comprehensive electronic medical record.

Design, Setting, And Patients: Cross-sectional study involving 2974 patients undergoing inpatient surgical procedures at 6 Veterans Health Administration (VHA) medical centers from 1999 to 2006.