Metric Development for the Multicenter Improving Pediatric Sepsis Outcomes (IPSO) Collaborative.

Background: A 56 US hospital collaborative, Improving Pediatric Sepsis Outcomes, has developed variables, metrics and a data analysis plan to track quality improvement (QI)-based patient outcomes over time. Improving Pediatric Sepsis Outcomes expands on previous pediatric sepsis QI efforts by improving electronic data capture and uniformity across sites.

Methods: An expert panel developed metrics and corresponding variables to assess improvements across the care delivery spectrum, including the emergency department, acute care units, hematology and oncology, and the ICU. Outcome, process, and balancing measures were represented. Variables and statistical process control charts were mapped to each metric, elucidating progress over time and informing plan-do-study-act cycles. Electronic health record (EHR) abstraction feasibility was prioritized. Time 0 was defined as time of earliest sepsis recognition (determined electronically), or as a clinically derived time 0 (manually abstracted), identifying earliest physiologic onset of sepsis.

Results: Twenty-four evidence-based metrics reflected timely and appropriate interventions for a uniformly defined sepsis cohort. Metrics mapped to statistical process control charts with 44 final variables; 40 could be abstracted automatically from multiple EHRs. Variables, including high-risk conditions and bedside huddle time, were challenging to abstract (reported in <80% of encounters). Size or type of hospital, method of data abstraction, and previous QI collaboration participation did not influence hospitals' abilities to contribute data. To date, 90% of data have been submitted, representing 200 007 sepsis episodes.

Conclusions: A comprehensive data dictionary was developed for the largest pediatric sepsis QI collaborative, optimizing automation and ensuring sustainable reporting. These approaches can be used in other large-scale sepsis QI projects in which researchers seek to leverage EHR data abstraction.