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Application of a trigger tool in near real time to inform quality improvement activities: a prospective study in a general medicine ward
  1. Brian M Wong1,
  2. Sonia Dyal2,
  3. Edward E Etchells1,
  4. Sandra Knowles3,
  5. Lauren Gerard4,
  6. Artemis Diamantouros5,
  7. Rajin Mehta1,
  8. Barbara Liu6,
  9. G Ross Baker7,
  10. Kaveh G Shojania1
  1. 1Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
  2. 2Veterans and Community Program, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
  3. 3Applied Health Research Centre, St. Michael's Hospital, Toronto, Canada
  4. 4Department of Medicine, University of Toronto, Toronto, Canada
  5. 5Pharmacy, Sunnybrook Health Sciences Centre, Toronto, Canada
  6. 6Regional Geriatric Program, Sunnybrook Health Sciences Centre, Toronto, Canada
  7. 7Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
  1. Correspondence to Brian M Wong, Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada M4N 3M5; BrianM.Wong{at}, b.wong{at}


Background Retrospective record review using trigger tools remains the most widely used method for measuring adverse events (AEs) to identify targets for improvement and measure temporal trends. However, medical records often contain limited information about factors contributing to AEs. We implemented an augmented trigger tool that supplemented record review with debriefing front-line staff to obtain details not included in the medical record. We hypothesised that this would foster the identification of factors contributing to AEs that could inform improvement initiatives.

Method A trained observer prospectively identified events in consecutive patients admitted to a general medical ward in a tertiary care academic medical centre (November 2010 to February 2011 inclusive), gathering information from record review and debriefing front-line staff in near real time. An interprofessional team reviewed events to identify preventable and potential AEs and characterised contributing factors using a previously published taxonomy.

Results Among 141 patients, 14 (10%; 95% CI 5% to 15%) experienced at least one preventable AE; 32 patients (23%; 95% CI 16% to 30%) experienced at least one potential AE. The most common contributing factors included policy and procedural problems (eg, routine protocol violations, conflicting policies; 37%), communication and teamwork problems (34%), and medication process problems (23%). However, these broad categories each included distinct subcategories that seemed to require different interventions. For instance, the 32 identified communication and teamwork problems comprised 7 distinct subcategories (eg, ineffective intraprofessional handovers, poor interprofessional communication, lacking a shared patient care, paging problems). Thus, even the major categories of contributing factors consisted of subcategories that individually related to a much smaller subset of AEs.

Conclusions Prospective application of an augmented trigger tool identified a wide range of factors contributing to AEs. However, the majority of contributing factors accounted for a small number of AEs, and more general categories were too heterogeneous to inform specific interventions. Successfully using trigger tools to stimulate quality improvement activities may require development of a framework that better classifies events that share contributing factors amenable to the same intervention.

  • Trigger tools
  • Adverse events, epidemiology and detection
  • Hospital medicine

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