title: Applying trigger tools to detect adverse events associated with outpatient surgery.
abstract: OBJECTIVE
  The objective of this study is to evaluate the performance of 5 triggers to detect adverse events (AEs) associated with outpatient surgery. Triggers use surveillance algorithms derived from clinical logic to flag cases where AEs have most likely occurred. Current efforts to detect AEs have focused primarily on the inpatient setting, despite the increase in outpatient surgery in all health care settings.
  
  
  METHODS
  Using trigger logic, we retrospectively evaluated data from 3 large health care systems' electronic medical records. Patients were eligible for inclusion if they had an outpatient (same-day) surgery in 2007 and at least 1 clinical note in the 6 months after the surgery. Two nurse abstractors reviewed a sample of trigger-flagged cases from each health care system. After reaching interrater reliability targets (κ > 0.60), we calculated the positive predictive value (PPV) of each trigger and the confidence interval of the estimate.
  
  
  RESULTS
  The surgical triggers flagged between 1% and 22% of the outpatient surgery cases, with a wide range in PPVs (6.0%-62.0%). The pulmonary embolism and deep vein thrombosis and emergency department triggers had the lowest proportion of flagged cases along with the highest PPVs, showing the most promise for screening cases with a high probability of AE occurrence.
  
  
  CONCLUSIONS
  Triggers may be useful in identifying a narrow set of surgeries for further review to determine if a surgical AE occurred, complementing existing tools and initiatives used to detect AEs. Improved detection of AEs in outpatient surgery should help target potential areas for quality improvement.
authors.affiliation: Center for Organization, Leadership, Management Research, a VA Center of Excellence, VA Boston Healthcare System, Boston, Massachusetts 02130, USA. akrosen@bu.edu
authors.forename: Amy K
authors.initials: AK
authors.lastname: Rosen
authors.forename: Hillary J
authors.initials: HJ
authors.lastname: Mull
authors.forename: Haytham
authors.initials: H
authors.lastname: Kaafarani
authors.forename: Jonathan
authors.initials: J
authors.lastname: Nebeker
authors.forename: Stephanie
authors.initials: S
authors.lastname: Shimada
authors.forename: Amy
authors.initials: A
authors.lastname: Helwig
authors.forename: Brian
authors.initials: B
authors.lastname: Nordberg
authors.forename: Brenna
authors.initials: B
authors.lastname: Long
authors.forename: Lucy A
authors.initials: LA
authors.lastname: Savitz
authors.forename: Christopher W
authors.initials: CW
authors.lastname: Shanahan
authors.forename: Kamal
authors.initials: K
authors.lastname: Itani
mesh_terms: D000465:Algorithms; D000556:Ambulatory Surgical Procedures / Q000009:adverse effects* / Q000706:statistics & numerical data; D003697:Delphi Technique; D006801:Humans; D007049:Iatrogenic Disease / Q000517:prevention & control*; D008991:Monitoring, Physiologic / Q000295:instrumentation* / Q000379:methods; D011183:Postoperative Complications / Q000517:prevention & control*; D011237:Predictive Value of Tests; D015397:Program Evaluation; D012189:Retrospective Studies; D014481:United States