Using electronic health records to classify risk for adverse safety events with ICU patient Mobility: A Cross-Sectional study

Background: Integrating early mobility (EM) expert consensus recommendations into an algorithm that uses electronic health record (EHR) data provides an opportunity for ICU nurse decision support. Objective: This study aimed to compare clinical differences in ICU EM algorithm domains among patients with and without documented EM and examine discordance between algorithm classification and documented EM. Methods: Secondary analysis of EHR data from adults admitted to the ICU from one health system's electronic data warehouse. We extracted demographic, clinical, and EM data for up to the first three days after ICU admission and applied the algorithm to classify patients as low/high-risk by clinical domain (respiratory, cardiovascular, neurological, activity order, overall) each day. We used the Wilcoxon rank sum test or Fisher's exact test to compare clinical criteria and algorithm classification between patients with and without documented EM. Results: From a total of 4,088 patients, documented EM increased each ICU day. Patients with EM were more likely to be classified by the algorithm as low-risk than those who stayed in bed each day. While a large proportion of low-risk patients remained in bed each day (813 day 1; 920 day 2; 881 day 3), some patients classified as high-risk had documented EM. Conclusions: A significant portion of patients identified as overall low-risk by the algorithm remained in bed, while some high-risk patients achieved EM. Variability between risk definitions and documented patient activity exists and understanding additional factors that nurses use to support EM decision-making is needed. Implications for clinical practice: EHR data can be used with a mobility algorithm to classify patients at low and high-risk for ICU EM. In the future, with additional refinements, an algorithm may augment clinician decisionmaking.