Literature DB >> 24275072

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

Daniel A Barocas1, Chirag S Kulahalli2, Jesse M Ehrenfeld3, April N Kapu4, David F Penson5, Chaochen Chad You6, Lisa Weavind4, Roger Dmochowski7.   

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

Entities:  

Keywords:  AUC; O/E; ROM; RRT; SOI; area under the curve; observed-to-expected; rapid response team; risk of mortality; severity of Illness

Mesh:

Year:  2013        PMID: 24275072      PMCID: PMC4353563          DOI: 10.1016/j.jamcollsurg.2013.09.011

Source DB:  PubMed          Journal:  J Am Coll Surg        ISSN: 1072-7515            Impact factor:   6.113


  12 in total

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Authors:  K Hillman; M Parr; A Flabouris; G Bishop; A Stewart
Journal:  Resuscitation       Date:  2001-02       Impact factor: 5.262

2.  Guidelines for the uniform reporting of data for Medical Emergency Teams.

Authors:  Michelle Cretikos; Michael Parr; Ken Hillman; Gillian Bishop; Daniel Brown; Kathy Daffurn; Hanh Dinh; Nevenka Francis; Tracy Heath; Grant Hill; Jeff Murphy; David Sanchez; Nancy Santiano; Lis Young
Journal:  Resuscitation       Date:  2005-09-08       Impact factor: 5.262

3.  Introduction of the medical emergency team (MET) system: a cluster-randomised controlled trial.

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4.  Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.

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6.  The Department of Veterans Affairs' NSQIP: the first national, validated, outcome-based, risk-adjusted, and peer-controlled program for the measurement and enhancement of the quality of surgical care. National VA Surgical Quality Improvement Program.

Authors:  S F Khuri; J Daley; W Henderson; K Hur; J Demakis; J B Aust; V Chong; P J Fabri; J O Gibbs; F Grover; K Hammermeister; G Irvin; G McDonald; E Passaro; L Phillips; F Scamman; J Spencer; J F Stremple
Journal:  Ann Surg       Date:  1998-10       Impact factor: 12.969

Review 7.  Rapid-response teams.

Authors:  Daryl A Jones; Michael A DeVita; Rinaldo Bellomo
Journal:  N Engl J Med       Date:  2011-07-14       Impact factor: 91.245

8.  A prospective before-and-after trial of a medical emergency team.

Authors:  Rinaldo Bellomo; Donna Goldsmith; Shigehiko Uchino; Jonathan Buckmaster; Graeme K Hart; Helen Opdam; William Silvester; Laurie Doolan; Geoffrey Gutteridge
Journal:  Med J Aust       Date:  2003-09-15       Impact factor: 7.738

9.  The ratio of observed-to-expected mortality as a quality of care indicator in non-surgical VA patients.

Authors:  W R Best; D C Cowper
Journal:  Med Care       Date:  1994-04       Impact factor: 2.983

10.  Effectiveness of the Medical Emergency Team: the importance of dose.

Authors:  Daryl Jones; Rinaldo Bellomo; Michael A DeVita
Journal:  Crit Care       Date:  2009-10-06       Impact factor: 9.097

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  3 in total

1.  Predictors of mortality and cost among surgical patients requiring rapid response team activation.

Authors:  Alexandre Tran; Shannon M Fernando; Daniel I McIsaac; Bram Rochwerg; Garrick Mok; Andrew J E Seely; Dalibor Kubelik; Kenji Inaba; Dennis Y Kim; Peter M Reardon; Jennifer Shen; Peter Tanuseputro; Kednapa Thavorn; Kwadwo Kyeremanteng
Journal:  Can J Surg       Date:  2020-12-09       Impact factor: 2.089

2.  Outcomes of hospitalized hematologic oncology patients receiving rapid response system activation for acute deterioration.

Authors:  Benjamin Gershkovich; Shannon M Fernando; Brent Herritt; Lana A Castellucci; Bram Rochwerg; Laveena Munshi; Sangeeta Mehta; Andrew J E Seely; Daniel I McIsaac; Alexandre Tran; Peter M Reardon; Peter Tanuseputro; Kwadwo Kyeremanteng
Journal:  Crit Care       Date:  2019-08-27       Impact factor: 9.097

3.  What are healthcare workers' preferences for hand hygiene interventions? A discrete choice experiment.

Authors:  Wenlin Chen; Chung-Li Tseng
Journal:  BMJ Open       Date:  2021-11-03       Impact factor: 2.692

  3 in total

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