Impact of Predictive Text Clinical Decision Support on Imaging Order Entry in the Emergency Department

Abstract

Purpose: Imaging clinical decision support (CDS) is designed to assist providers in selecting appropriate imaging studies and is now federally required. The aim of this study was to understand the effect of CDS on decisions and workflows in the emergency department (ED).

Methods: The authors' institution's order entry platform serves up structured indications for imaging orders. Imaging orders are scored by CDS on the basis of appropriate use criteria (AUC). CDS triggers alerts for imaging orders with low AUC scores. Because free text alone cannot be scored by CDS, an artificial intelligence predictive text (AIPT) module was implemented to guide the selection of structured indications when free-text indications are entered. A total of 17,355 imaging orders in the ED over 6 months were retrospectively analyzed.

Results: CDS alerts for low AUC scores were triggered for 3% of all imaging study orders (522 of 17,355). Providers spent an average of 24 seconds interacting with alerts. In 18 of 522 imaging orders with alerts, alternative studies were ordered. After AIPT implementation, the percentage of unscored studies significantly decreased from 81% to 45% (P < .001).

Conclusions: In a quaternary academic ED, CDS alerts triggered by low AUC scores caused minimal increase in time spent on imaging order entry but had a relatively marginal impact on imaging study selection. AIPT implementation increased the number of scored studies and could potentially enhance CDS effects. CDS implementation enables the collection of novel data regarding which imaging studies receive low AUC scores. Future work could include exploring alternative models of CDS implementation to maximize its impact.

Keywords: Clinical decision support; artificial intelligence; diagnostic imaging; electronic health records; health services overutilization.