Daniel A Barocas1, Chirag S Kulahalli2, Jesse M Ehrenfeld3, April N Kapu4, David F Penson5, Chaochen Chad You6, Lisa Weavind4, Roger Dmochowski7. 1. Department of Urologic Surgery, Vanderbilt University, Nashville, TN; Center for Surgical Quality and Outcomes Research, Vanderbilt University, Nashville, TN. Electronic address: dan.barocas@vanderbilt.edu. 2. Vanderbilt University Medical School, Nashville, TN. 3. Department of Anesthesiology, Vanderbilt University, Nashville, TN. 4. Division of Anesthesiology Critical Care Medicine, Vanderbilt University, Nashville, TN. 5. Department of Urologic Surgery, Vanderbilt University, Nashville, TN; Center for Surgical Quality and Outcomes Research, Vanderbilt University, Nashville, TN; Geriatric Research, Education, and Clinical Center, Tennessee Valley Veterans Administration Health System, Nashville, TN. 6. Department of Urologic Surgery, Vanderbilt University, Nashville, TN; Center for Surgical Quality and Outcomes Research, Vanderbilt University, Nashville, TN. 7. Department of Urologic Surgery, Vanderbilt University, Nashville, TN.
Abstract
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.
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.
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