Benchmarking the use of a rapid response team by surgical services at a tertiary care hospital.

Barocas DA, Kulahalli CS, Ehrenfeld JM, Kapu AN, Penson DF, You CC, Weavind L, Dmochowski R
J Am Coll Surg. 2014 218 (1): 66-72

PMID: 24275072 · PMCID: PMC4353563 · DOI:10.1016/j.jamcollsurg.2013.09.011

BACKGROUND - Rapid response teams (RRT) are used to prevent adverse events in patients with acute clinical deterioration, and to save costs of unnecessary transfer in patients with lower-acuity problems. However, determining the optimal use of RRT services is challenging. One method of benchmarking performance is to determine whether a department's event rate is commensurate with its volume and acuity.

STUDY DESIGN - Using admissions between 2009 and 2011 to 18 distinct surgical services at a tertiary care center, we developed logistic regression models to predict RRT activation, accounting for days at-risk for RRT and patient acuity, using claims modifiers for risk of mortality (ROM) and severity of illness (SOI). The model was used to compute observed-to-expected (O/E) RRT use by service.

RESULTS - Of 45,651 admissions, 728 (1.6%, or 3.2 per 1,000 inpatient days) resulted in 1 or more RRT activations. Use varied widely across services (0.4% to 6.2% of admissions; 1.39 to 8.73 per 1,000 inpatient days, unadjusted). In the multivariable model, the greatest contributors to the likelihood of RRT were days at risk, SOI, and ROM. The O/E RRT use ranged from 0.32 to 2.82 across services, with 8 services having an observed value that was significantly higher or lower than predicted by the model.

CONCLUSIONS - We developed a tool for identifying outlying use of an important institutional medical resource. The O/E computation provides a starting point for further investigation into the reasons for variability among services, and a benchmark for quality and process improvement efforts in patient safety.

Copyright © 2014 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

MeSH Terms (22)

Adolescent Adult Aged Aged, 80 and over Benchmarking Female Hospital Rapid Response Team Humans Logistic Models Male Middle Aged Multivariate Analysis Patient Acuity Proportional Hazards Models Prospective Studies Quality Improvement Risk Adjustment ROC Curve Severity of Illness Index Surgery Department, Hospital Tertiary Care Centers Young Adult

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