1071: QUANTIFYING PASSIVE CLINICAL DECISION SUPPORT AND REDUCING LABORATORY RESULT FATIGUE

Abstract

Introduction: Clinical decision support (CDS) systems are intended to improve adherence to standard practices, enhance awareness, and increase care quality. Excessive passive decision support, such as laboratory result highlighting, can reduce CDS effectiveness. We quantify result highlighting burden across two institutions, and measure change in highlighting burden when applying published data-driven cutoffs to identify abnormal laboratory values. Methods: This multi-site retrospective study includes patients admitted to the pediatric ICU between 9/2016 and 9/2019. We describe the frequency of abnormal laboratory result highlighting by laboratory group, compared across sites. We use a logistic regression model to analyze the relationship between abnormal result highlighting to selected covariates and report odds ratios. We apply modified cutoff values based on mortality odds ratios (MOR) [Pollack et al, Peds Crit Care Med, 2021], indicating “abnormal” results when MOR >2.0. We re-calculate the frequency of abnormal result highlighting and compare to existing cutoffs. Results: We report a total of 19,087 ICU encounters and 12,507 unique patients. Across all laboratory groups, abnormal result highlighting differed significantly by institution (A: 54% vs B: 50%, p < 0.001). Percent of results with passive alerts was greatest in coagulation studies (A: 77%, B: 36%) and least in basic chemistry panels (A: 47%, B: 43%). In a logistic regression model, the only covariate consistently associated with an abnormal result was “collected < 24 hours from ICU admission,” for which odds of an abnormal result decreased [A: 12% decrease; B: 15% decrease]. When MOR-based cutoffs were applied, abnormal result highlight significantly decreased across all laboratory groups by >14%, except for complete blood counts (A: 14% increase, B: 16% increase). Conclusions: Abnormal result highlighting is a pervasive form of passive decision support that is differentially present across PICUs. Modifying laboratory result thresholds using data-driven MOR-based cutoffs reduces this passive CDS burden, which may better allow providers to identify physiologic signals from data noise. This work represents a first step in improving upon passive CDS to improve patient care quality and safety.