Electronic health record-based triggers to detect adverse events after outpatient orthopaedic surgery

Adverse events (AEs) after outpatient orthopaedic surgery are common, but difficult to detect. Electronic health records facilitate abstraction of large quantities of data, and may allow automated identification of 'triggers' or clues indicating the possibility of an AE. We evaluated electronic health record-based triggers to detect AEs after outpatient orthopaedic surgery.

The medical records of 1464 patients undergoing outpatient surgical procedures in one of five orthopaedic services at our institution were manually reviewed for the occurrence of 90-day postoperative AEs. We used electronic health records to identify triggers suggestive of an AE. Each trigger was evaluated for positive predictive value (PPV). We constructed a logistic regression model to determine triggers associated with AEs and used the beta coefficients derived from the model to produce a formula for the likelihood of identifying an AE in the medical record.

The overall rate of 90-day AEs was 10%, with surgical site infection being the most common (3.3%). Electronic triggers with the highest PPVs for the occurrence of 90-day AEs were antibiotic prescription (75%), emergency department visit (41%), bone/joint or blood culture (41%), repeat surgery (39%) and consult with infectious disease specialist (33%). Using our formula to predict the likelihood of identifying an AE in the medical record, a predicted probability of >0.10 had a specificity of 80% and sensitivity of 53% for actual AE.

Electronic health record-based triggers may facilitate quality-improvement efforts to monitor morbidity after outpatient orthopaedic surgery. Further research is needed to understand the optimal use of electronic triggers as surgical quality indicators and as screening tools to flag cases for manual review.

Level III, prognostic study.