Development and testing of tools to detect ambulatory surgical adverse events

J Patient Saf. 2013 Jun;9(2):96-102. doi: 10.1097/PTS.0b013e31827d1a88.


Objectives: Numerous health-care systems in the United States, including the Veterans Health Administration (VA), use the National Surgical Quality Improvement Program (NSQIP) to detect surgical adverse events (AEs). VASQIP sampling methodology excludes many routine ambulatory surgeries from review. Triggers, algorithms derived from clinical logic to flag cases where AEs have most likely occurred, could complement VASQIP by detecting a higher yield of ambulatory surgeries with a true surgical AE.

Methods: We developed and tested a set of ambulatory surgical AE trigger algorithms using a sample of fiscal year 2008 ambulatory surgeries from the VA Boston Healthcare System. We used VA Boston VASQIP-assessed cases to refine triggers and VASQIP-excluded cases to test how many trigger-flagged surgeries had a nurse chart review-detected surgical AE. Chart review was performed using the VA electronic medical record. We calculated the ratio of cases with a true surgical AE over flagged cases (i.e., the positive predictive value [PPV]), and the 95% confidence interval for each trigger.

Results: Compared with the VASQIP rate (9 AEs, or 2.8%, of the 322 charts assessed), nurse chart review of the 198 trigger-flagged surgeries yielded more cases with at least 1 AE (47 surgeries with an AE, or 6.0%, of the 782 VASQIP-excluded ambulatory surgeries). Individual trigger PPVs ranged from 12.4% to 58.3%.

Conclusions: In comparison with VASQIP, our set of triggers identified a higher rate of surgeries with AEs in fewer chart-reviewed cases. Because our results are based on a relatively small sample, further research is necessary to confirm these findings.

Publication types

  • Comparative Study
  • Evaluation Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Ambulatory Surgical Procedures / adverse effects*
  • Ambulatory Surgical Procedures / standards
  • Data Mining* / methods
  • Electronic Health Records
  • Humans
  • Patient Safety*
  • Postoperative Complications / etiology*
  • Postoperative Complications / prevention & control
  • Quality Indicators, Health Care*
  • Reproducibility of Results
  • Risk Factors
  • Time Factors
  • United States
  • United States Department of Veterans Affairs