Trigger Tool Applied to Electronic Health Records Helps Identify Hospital Adverse Events

Applying a modified version of the Institute for Healthcare Improvement’s global trigger tool to data from electronic health records (EHRs) can effectively identify preventable adverse events in hospitals, according to an AHRQ-funded study in BMJ Quality & Safety. The study, based on hospital stays at a large health system between 2010 and 2015, examined “escalated” care hospital stays that involved patient transfers to intensive care or activation of a rapid response team within 15 days of admission. Only lower risk patients were included. Of nearly 88,500 hospitalizations, 41 involved preventable adverse events, either due to flaws in care management or diagnostic errors. Researchers concluded that using EHR data with the modified trigger tool could reveal adverse event causes and lead to improvement strategies. Access the abstract.

2017 Sep 21. pii: bmjqs-2017-006975. doi: 10.1136/bmjqs-2017-006975. [Epub ahead of print]

An electronic trigger based on care escalation to identify preventable adverse events in hospitalised patients.

Bhise V1,2, Sittig DF3, Vaghani V1,2, Wei L1,2, Baldwin J1,2, Singh H1,2.

Author information

Abstract

BACKGROUND:

Methods to identify preventable adverse events typically have low yield and efficiency. We refined the methods of Institute of Healthcare Improvement's Global Trigger Tool (GTT) application and leveraged electronic health record (EHR) data to improve detection of preventable adverse events, including diagnostic errors.

METHODS:

We queried the EHR data repository of a large health system to identify an 'index hospitalization' associated with care escalation (defined as transfer to the intensive care unit (ICU) or initiation of rapid response team (RRT) within 15 days of admission) between March 2010 and August 2015. To enrich the record review sample with unexpected events, we used EHR clinical data to modify the GTT algorithm and limited eligible patients to those at lower risk for care escalation based on younger age and presence of minimal comorbid conditions. We modified the GTT review methodology; two physicians independently reviewed eligible 'e-trigger' positive records to identify preventable diagnostic and care management events.

RESULTS:

Of 88 428 hospitalisations, 887 were associated with care escalation (712 ICU transfers and 175 RRTs), of which 92 were flagged as trigger-positive and reviewed. Preventable adverse events were detected in 41 cases, yielding a trigger positive predictive value of 44.6% (reviewer agreement 79.35%; Cohen's kappa 0.573). We identified 7 (7.6%) diagnostic errors and 34 (37.0%) care management-related events: 24 (26.1%) adverse drug events, 4 (4.3%) patient falls, 4 (4.3%) procedure-related complications and 2 (2.2%) hospital-associated infections. In most events (73.1%), there was potential for temporary harm.

CONCLUSION:

We developed an approach using an EHR data-based trigger and modified review process to efficiently identify hospitalised patients with preventable adverse events, including diagnostic errors. Such e-triggers can help overcome limitations of currently available methods to detect preventable harm in hospitalised patients.