Identificando eventos adversos luego de cirugías ambulatorias – mejorando mediciones de #SegPac – BMJQuality

Identifying adverse events after outpatient surgery: improving measurement of patient safety

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.11 Triggers facilitate more selective EMR review and also capitalise on the richness of EMR data, resulting in efficient and cost-effective tools that capture events missed by other methods (eg, voluntary reporting).12–15

The US Agency for Healthcare Research and Quality (AHRQ) sponsored a conference in 2008 that helped highlight the importance of triggers in identifying patient safety risks and hazards, while also endorsing future development of prospective triggers to enable timely interventions to prevent or reduce specific AEs.11 ,16 Triggers are now widely used to detect many types of AEs, including diagnostic errors, adverse drug events, hospital-acquired infections, delays in diagnoses and outpatient surgical AEs.17–26 Trigger algorithms are frequently applied to EMRs for automated surveillance, and increasingly to prospectively identify patients at risk of AEs.27 ,28

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