Sepsis Prediction in Hospitalized Children: Clinical Decision Support Design and DeploymentRebecca J. Stephen, Kate Lucey, Michael S. Carroll, Jeremy Hoge, Kimberly Maciorowski, Roderick C. Jones, Megan O’Connell, Carly Schwab, Jillian Rojas, L. Nelson Sanchez Pinto
- General Medicine
- Pediatrics, Perinatology and Child Health
Following development and validation of a sepsis prediction model described in a companion article, we aimed to use quality improvement and safety methodology to guide the design and deployment of clinical decision support (CDS) tools and clinician workflows to improve pediatric sepsis recognition in the inpatient setting.
CDS tools and sepsis huddle workflows were created to implement an electronic health record-based sepsis prediction model. These were proactively analyzed and refined using simulation and safety science principles before implementation and were introduced across inpatient units during 2020-2021. Huddle compliance, alerts per non-ICU patient days, and days between sepsis-attributable emergent transfers were monitored. Rapid Plan-Do-Study-Act (PDSA) cycles based on user feedback and weekly metric data informed improvement throughout implementation.
There were 264 sepsis alerts on 173 patients with an 89% bedside huddle completion rate and 10 alerts per 1000 non-ICU patient days per month. There was no special cause variation in the metric days between sepsis-attributable emergent transfers.
An automated electronic health record-based sepsis prediction model, CDS tools, and sepsis huddle workflows were implemented on inpatient units with a relatively low rate of interruptive alerts and high compliance with bedside huddles. Use of CDS best practices, simulation, safety tools, and quality improvement principles led to high utilization of the sepsis screening process.