Active surveillance using electronic triggers to detect adverse events in hospitalized patients
- 1 June 2006
- journal article
- Published by BMJ in Quality and Safety in Health Care
- Vol. 15 (3) , 184-190
- https://doi.org/10.1136/qshc.2005.014589
Abstract
Background: Adverse events (AEs) occur with alarming frequency in health care and can have a significant impact on both patients and caregivers. There is a pressing need to understand better the frequency, nature, and etiology of AEs, but currently available methodologies to identify AEs have significant limitations. We hypothesized that it would be possible to design a method to conduct real time active surveillance and conducted a pilot study to identify adverse events and medical errors. Methods: Records were selected based on 21 electronically obtained triggers, including abnormal laboratory values and high risk and antidote medications. Triggers were chosen based on their expected potential to signal AEs occurring during hospital admissions. Each AE was rated for preventability and severity and categorized by type of event. Reviews were performed by an interdisciplinary patient safety team. Results: Over a 3 month period 327 medical records were reviewed; at least one AE or medical error was identified in 243 (74%). There were 163 preventable AEs (events in which there was a medical error that resulted in patient harm) and 138 medical errors that did not lead to patient harm. Interventions to prevent or ameliorate harm were made following review of the medical records of 47 patients. Conclusions: This methodology of active surveillance allows for the identification and assessment of adverse events among hospitalized patients. It provides a unique opportunity to review events at or near the time of their occurrence and to intervene and prevent harm.Keywords
This publication has 27 references indexed in Scilit:
- Real time patient safety audits: improving safety every dayQuality and Safety in Health Care, 2005
- Automated Detection of Adverse Events Using Natural Language Processing of Discharge SummariesJournal of the American Medical Informatics Association, 2005
- MediClass: A System for Detecting and Classifying Encounter-based Clinical Events in Any Electronic Medical RecordJournal of the American Medical Informatics Association, 2005
- Validation of a Discharge Summary Term Search Method to Detect Adverse EventsJournal of the American Medical Informatics Association, 2004
- An Error by Any Other NameThe American Journal of Nursing, 2004
- The measurement of active errors: methodological issuesQuality and Safety in Health Care, 2003
- Electronically Screening Discharge Summaries for Adverse Medical EventsJournal of the American Medical Informatics Association, 2003
- Detecting Adverse Events Using Information TechnologyJournal of the American Medical Informatics Association, 2003
- Recommended practices for surveillanceAmerican Journal of Infection Control, 1998
- A Study of Medical Injury and Medical MalpracticeNew England Journal of Medicine, 1989