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Identifying adverse events after outpatient surgery: improving measurement of patient safety
  1. Amy K Rosen1,
  2. Hillary J Mull1,2
  1. 1Center for Healthcare Organization and Implementation Research (CHOIR), VA Boston Healthcare System, Boston, Massachusetts, USA
  2. 2Department of Surgery, Boston University School of Medicine, Boston, Massachusetts, USA
  1. Correspondence to Dr Amy K Rosen, Center for Healthcare Organization and Implementation Research (CHOIR), VA Boston Healthcare System, Boston, MA 02130, USA; akrosen{at}bu.edu

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Identification of adverse events (AEs) is critical for improving patient safety. However, accurate measurement continues to be challenging, and efforts to detect and track surgical AEs in the outpatient setting lag behind those of the inpatient setting. Although numerous methods have been utilised over the years to detect AEs (eg, voluntary reporting systems, chart review and patient interviews), these detection systems suffer from a variety of limitations including resource constraints.1 ,2 More recent development of automated surveillance systems to detect AEs using electronic medical record (EMR) data has greatly facilitated the identification of AEs, particularly among ambulatory patients.3–6 Menendez et al illustrate how EMR data and electronic triggers can contribute to better measurement of patient safety in outpatient surgery.7

Trigger methodology has substantially improved since the seminal work of the Institute for Healthcare Improvement (IHI) in the early 2000s that helped promulgate the use of chart-based trigger tools for retrospective detection of AEs.8–10 Although triggers are still evolving as informatics tools, and are likely in their ‘early stages’ of development, the trigger methodology represents a good compromise between two modalities: automated surveillance systems and manual chart review (ie, the ‘gold standard’). Triggers rely on both electronic and manual review processes to search for patterns in the data consistent with a possible AE. Triggers use surveillance rules or algorithms derived from clinical logic to flag patient medical records for the presence of an AE. Once a trigger is flagged in the data, then the patient's medical record is reviewed to confirm the occurrence (yes/no) of the AE. …

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