Richard T Griffey1, Ryan M Schneider2, Alexandre A Todorov3. 1. Division of Emergency Medicine, Washington University School of Medicine, Saint Louis, MO. Electronic address: griffeyr@wustl.edu. 2. Division of Emergency Medicine, Washington University School of Medicine, Saint Louis, MO. 3. Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO.
Abstract
STUDY OBJECTIVE: Trigger tools improve surveillance for harm by focusing reviews on records with "triggers" whose presence increases the likelihood of an adverse event. We refine and automate a previously developed emergency department (ED) trigger tool and present record selection strategies to further optimize yield. METHODS: We specified 97 triggers for extraction from our electronic medical record, identifying 76,894 ED visits with greater than or equal to 1 trigger. We reviewed 1,726 records with greater than or equal to 1 trigger, following a standard trigger tool review process. We validated query performance against manual review and evaluated individual triggers, retaining only those associated with adverse events in the ED. We explored 2 approaches to enhance record selection: on number of triggers present and using trigger weights derived with least absolute shrinkage and selection operator logistic regression. RESULTS: The automated query performed well compared with manual review (sensitivity >70% for 80 triggers; specificity >92% for all). Review yielded 374 adverse events (21.6 adverse events per 100 records). Thirty triggers were associated with risk of harm in the ED. An estimated 10.3% of records with greater than 1 of these triggers would include an adverse event in the ED. Selecting only records with greater than or equal to 4 or greater than or equal to 9 triggers improves yield to 17% and 34.8%, respectively, whereas use of least absolute shrinkage and selection operator trigger weighting enhances the yield to as high as 52%. CONCLUSION: The ED trigger tool is a promising approach to improve yield, scope, and efficiency of review for all-cause harm in emergency medicine. Beginning with a broad set of candidate triggers, we validated a computerized query that eliminates the need for manual screening for triggers and identified a refined set of triggers associated with adverse events in the ED. Review efficiency can be further enhanced with enhanced record selection.
STUDY OBJECTIVE: Trigger tools improve surveillance for harm by focusing reviews on records with "triggers" whose presence increases the likelihood of an adverse event. We refine and automate a previously developed emergency department (ED) trigger tool and present record selection strategies to further optimize yield. METHODS: We specified 97 triggers for extraction from our electronic medical record, identifying 76,894 ED visits with greater than or equal to 1 trigger. We reviewed 1,726 records with greater than or equal to 1 trigger, following a standard trigger tool review process. We validated query performance against manual review and evaluated individual triggers, retaining only those associated with adverse events in the ED. We explored 2 approaches to enhance record selection: on number of triggers present and using trigger weights derived with least absolute shrinkage and selection operator logistic regression. RESULTS: The automated query performed well compared with manual review (sensitivity >70% for 80 triggers; specificity >92% for all). Review yielded 374 adverse events (21.6 adverse events per 100 records). Thirty triggers were associated with risk of harm in the ED. An estimated 10.3% of records with greater than 1 of these triggers would include an adverse event in the ED. Selecting only records with greater than or equal to 4 or greater than or equal to 9 triggers improves yield to 17% and 34.8%, respectively, whereas use of least absolute shrinkage and selection operator trigger weighting enhances the yield to as high as 52%. CONCLUSION: The ED trigger tool is a promising approach to improve yield, scope, and efficiency of review for all-cause harm in emergency medicine. Beginning with a broad set of candidate triggers, we validated a computerized query that eliminates the need for manual screening for triggers and identified a refined set of triggers associated with adverse events in the ED. Review efficiency can be further enhanced with enhanced record selection.
Authors: Richard T Griffey; Ryan M Schneider; Alexandre A Todorov; Lauren Yaeger; Brian R Sharp; Marie C Vrablik; Emily L Aaronson; Christine Sammer; Antoinette Nelson; Holly Manley; Patricia Dalton; Lee Adler Journal: Acad Emerg Med Date: 2019-04-24 Impact factor: 3.451
Authors: Shan W Liu; Yuchiao Chang; Joel S Weissman; Richard T Griffey; James Thomas; Suvd Nergui; Azita G Hamedani; Carlos A Camargo; Sara Singer Journal: Acad Emerg Med Date: 2011-06-21 Impact factor: 3.451
Authors: David C Classen; Roger Resar; Frances Griffin; Frank Federico; Terri Frankel; Nancy Kimmel; John C Whittington; Allan Frankel; Andrew Seger; Brent C James Journal: Health Aff (Millwood) Date: 2011-04 Impact factor: 6.301
Authors: Richard Thomas Griffey; Ryan M Schneider; Lee M Adler; Roberta Capp; Christopher R Carpenter; Brenna M Farmer; Kathyrn Y Groner; Sheridan Hodkins; Craig A McCammon; Jonathan T Powell; Jonathan E Sather; Jeremiah D Schuur; Marc J Shapiro; Brian R Sharp; Arjun K Venkatesh; Marie C Vrablik; Jennifer L Wiler Journal: J Patient Saf Date: 2020-03 Impact factor: 2.844