Alert to Action: Implementing Artificial Intelligence–Driven Clinical Decision Support Tools for Sepsis

Abstract

Background: Sepsis is the leading cause of mortality among hospitalized patients in our health care system and has been the target of major international initiatives such as the Surviving Sepsis Campaign championed by the Society of Critical Care Medicine and Get Ahead of Sepsis led by the Centers for Disease Control and Prevention. Methods: Our institution has strived to improve outcomes for patients by implementing a novel suite of integrated clinical decision support tools driven by a predictive learning algorithm in the electronic health record. The tools focus on sepsis multidisciplinary care using industry-standard heuristics of interface design to enhance usability and interaction. Results: Our novel clinical decision support tools demonstrated a higher level of interaction with a higher alert-to-action ratio compared to the average of all best practice alerts used at Ochsner Health (16.46% vs 8.4% to 12.1%). Conclusion: By using intuitive design strategies that encouraged users to complete best practice alerts and team-wide visualization of clinical decisions via a checklist, our clinical decision support tools for the detection and management of sepsis represent an improvement over legacy tools, and the results of this pilot may have implications beyond sepsis alerting.