The Emergency Department Trigger Tool: A Novel Approach to Screening for Quality and Safety Events

Griffey, Richard T.; Schneider, Ryan; Todorov, Alexandre A.

doi:10.1016/j.annemergmed.2019.07.032

Cited by 12 publications

(19 citation statements)

References 32 publications

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“…In this study, we confirmed the results of the derivation phase of our ED trigger tool demonstration project in an independent sample. We validated the majority (24/30) of triggers previously identified as being associated with AEs 30 and confirmed the utility of the tool in identifying all-cause harm, while maintaining representativeness of event types captured. We also added three previously marginal and two new triggers.…”

Section: Discussionmentioning

confidence: 56%

“…First, we validate the EDTT trigger selection in an independent sample by confirming: 1) that the 30 triggers found to be associated with AEs in our prior derivation study are again found associated with AEs in the validation phase and 2) that the tool as a whole performs at least as well for detecting AEs as in the derivation phase. 30 Second, we combine derivation and validation data for a larger sample size to refine our EDTT model, with trigger weights that reflect the strength of their association with AEs, enhancing our ability to predict AE occurrence using query trigger data alone. Third, we test three progressively more restrictive strategies to select records for review, which aim to enhance yield and, thus, the efficiency of reviews: 1) random selection from records with one or more triggers (the usual trigger tool approach), 2) records with higher numbers of triggers, and 3) using weighted trigger counts.…”

Section: Methodsmentioning

confidence: 99%

“…Data on all 92 candidate triggers were acquired in one pass for all 92,859 visits using direct queries of our EMR as previously described. 30 Sampling from this data set for the derivation and validation phases allowed us to assess whether the 30 identified triggers should be retained and also (after analyzing both samples) whether other eliminated triggers should be reconsidered. We also added three new triggers we thought might be high yield, based on our experience with reviews during the derivation phase: M19 (propofol administration), L14 (HCO 3 < 18), and M20 (enema).…”

Section: Trigger Data Acquisitionmentioning

confidence: 99%

“…We were able to significantly increase the yield and efficiency of reviews by selecting records with higher number of triggers and using weighted trigger counts. 30 Anticipating a future multicenter validation study, here we: 1) validate the 30 previously identified triggers in an independent data set and confirm performance of the tool as a whole for detecting AEs, 2) combine derivation and validation samples to develop multivariable models to determine trigger weights for predicting AE occurrence, 3) compare AE yields using three record selection approaches, and 4) evaluate the representativeness of AE types detected to address concerns that stricter record selection could limit the types and severity of AEs identified.…”

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confidence: 99%

“…We identified 30 triggers associated with AEs in a derivation sample of 1,786 records. We were able to significantly increase the yield and efficiency of reviews by selecting records with higher number of triggers and using weighted trigger counts 30 …”

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confidence: 99%

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The Emergency Department Trigger Tool: Validation and Testing to Optimize Yield

Griffey

Schneider

Todorov

2020

Academic Emergency Medicine

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Objective: Recognized as a premier approach for adverse event (AE) detection, trigger tools have been developed for multiple clinical settings outside the emergency department (ED). We recently derived and tested an ED trigger tool (EDTT) with enhanced features for high-yield detection of harm, consisting of 30 triggers associated with AEs. In this study, we validate the EDTT in an independent sample and compare record selection approaches to optimize yield for quality improvement. Methods: This is a retrospective observational study using data from 13 months of visits to an urban, academic ED by patients aged ≥ 18 years (92,859 records). We conducted standard two-tiered trigger tool reviews on an independent validation sample of 3,724 records with at least one of the 30 triggers found associated with AEs in our previous derivation sample (N = 1,786). We also tested three new candidate triggers and reviewed 72 records with no triggers for comparison purposes. We compare derivation and validation samples on: 1) triggers showing persistent associations with AEs, 2) AE yield (AEs detected/records reviewed), and 3) representativeness of AE types detected. We use bivariate associations of triggers with AEs as the basis for trigger selection. We then use multivariable modeling in the combined derivation and validation samples to determine AE risk scores using trigger weights. This allows us to predict occurrence of AEs and derive population prevalence estimates. Finally, we compare yield for detection of AEs under three record selection strategies (random selection, trigger counts, weighted trigger counts). Results: Twenty-four of the 30 triggers were confirmed to be associated with AEs on bivariate testing. Three previously marginal triggers and two of three new candidate triggers were also found to be associated with AEs. The presence of any of these 29 triggers was associated with an AE rate of 10% in our selected sample (compared to 1.1% for none, p < 0.001). The risk of an AE increased with number of triggers. Combining data from both phases, we identified 461 AEs in 429 unique visits in 5,582 records reviewed. Our multivariable model (which emphasized parsimony) retained 12 triggers with a ROC AUC of 82% in both samples. Selecting records for review based on number of triggers improves yield to 14% for 4+ triggers (top 10% of visits) and to 28% for 8+ (top 1%). A weighted trigger count has corresponding yields of 18 and 38%. The method for selecting records for review did not appear to affect event-type representativeness, with similar distributions of event types and severities detected. Conclusions: In this single-site study of the EDTT we observed high levels of validity in trigger selection, yield, and representativeness of AEs, with yields that are superior to estimates for traditional approaches to AE detection. Record selection using weighted triggers outperforms a trigger count threshold approach and far outperforms random sampling from records with at least one trigger. The EDTT is a promising efficient ...

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Section: Discussionmentioning

confidence: 56%

Section: Methodsmentioning

confidence: 99%

Section: Trigger Data Acquisitionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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