The Use of Rapid Response Teams to Reduce Failure to Rescue Events: A Systematic Review

Hall, Kendall K.; Lim, Andrea N.W.; Gale, Bryan

doi:10.1097/pts.0000000000000748

Cited by 36 publications

(47 citation statements)

References 21 publications

(48 reference statements)

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“…In a study that measured the outcomes after the implementation of a rapid response system on a large healthcare jurisdiction, it was found that cardiopulmonary arrests, and related deaths, mortality and FTR decreased but at the same rate as before the implementation; however, the mortality in the low-mortality diagnostic-related group subpopulation decreased significantly. Other articles also report that RRTs have reduced hospital mortality, unplanned ICU transfers and unexpected cardiac arrests [19,27,46,47].…”

Section: Resultsmentioning

confidence: 99%

“…Rapid response teams and medical emergency teams have become the standard approach to the efferent arm, becoming more prevalent in hospital systems overtime [19]. Their constitution may vary in different institutions, but they are typically multidisciplinary and comprise a critical care physician, a critical care nurse and a respiratory therapist at minimum [8,46]. Evidence also shows that intensivists used on rapid response teams are predictors of high performance and may improve FTR [8,10].…”

Section: Efferent Limbmentioning

confidence: 99%

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Failure To Rescue, What Can Be Done To Prevent It?

Pedro

Maria

Humberto

2021

EJBMR

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Introduction: Failure to Rescue (FTR) is the failure to prevent a patient’s death after a complication. It measures the ability of a hospital to prevent the death of patients who develop one or more complication that was not present at the time of admission. Therefore, the aim of this study is to review the factors that contribute to FTR, and the measures and strategies that can be applied to prevent the FTR events, in order to discuss the best way to improve patient outcomes in the hospital setting. Methods: A search was conducted on PUBMED retrieving a total of 464 articles. A review of the selected articles’ bibliography was conducted to find other relevant articles. Sixty studies were reviewed in this paper. Results: Patient factors as increasing age, comorbidities and frailty increase the risk of FTR, as well as an increasing number of complications. Several hospital factors, nursing care, and microsystem also influence FTR. Some track and Trigger Systems (TTS) and Early Warning Scores (EWS) have been shown to predict clinical deterioration. On the other hand, machine learning systems have outperformed EWS. Rapid response teams have become the standard approach to delivery and escalation of care, and cognitive aids and crisis checklists also have potential to help reduce FTR. Conclusion: Patient and hospital factors are often non-modifiable; thus, microsystem factors could be a target for improvement. Creating clinical pathways can improve surveillance, and communication tools like SBAR can help relay information. EWS, machine learning models and continuous monitoring are strategies that can help detect clinical deterioration. In the efferent limb rapid response teams have shown to reduce FTR.

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Section: Resultsmentioning

confidence: 99%

Section: Efferent Limbmentioning

confidence: 99%

Failure To Rescue, What Can Be Done To Prevent It?

Pedro

Maria

Humberto

2021

EJBMR

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“…Many hospitals operate rapid response teams (RRTs), which use uses medical alert systems to respond quickly to such adverse events. There is evidence of decreased mortality and non-ICU cardiac arrest rates with the use of RRTs; however, the effects of RRTs on ICU transfer rates are equivocal [ 7 ]. Several risk scoring systems are used to identify patients at high risk of serious adverse events including unexpected inpatient death.…”

Section: Introductionmentioning

confidence: 99%

Prediction of In-Hospital Cardiac Arrest Using Shallow and Deep Learning

et al. 2021

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Sudden cardiac arrest can leave serious brain damage or lead to death, so it is very important to predict before a cardiac arrest occurs. However, early warning score systems including the National Early Warning Score, are associated with low sensitivity and false positives. We applied shallow and deep learning to predict cardiac arrest to overcome these limitations. We evaluated the performance of the Synthetic Minority Oversampling Technique Ratio. We evaluated the performance using a Decision Tree, a Random Forest, Logistic Regression, Long Short-Term Memory model, Gated Recurrent Unit model, and LSTM–GRU hybrid models. Our proposed Logistic Regression demonstrated a higher positive predictive value and sensitivity than traditional early warning systems.

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“…These guidelines are a combination of 10 objective quantitative indicators ( ie , body temperature, systolic blood pressure, pulse rate, mean arterial pressure, shock index, respiration rate, peripheral oxygen saturation, peripheral blood glucose, state of consciousness, and pain score), and 48 and 25 common complaints or symptoms seen in priority general patients and trauma patients, respectively. In addition, computer-aided patient triage software [ 4 ] was developed to identify patients in need of emergency care. Emergency patients were triaged according to disease severity and were classified as level I (fatal, red), level II (critical, orange), level III (emergency, yellow), level IV (not urgent, green), and level V (non-emergency, blue).…”

mentioning

confidence: 99%

“…This truly reflects the “emergency” of the ED. The rescue team shares some responsibilities with the Rapid Response team [ 4 ] in critical care medicine and can effectively respond to all kinds of emergencies. In a previous study, [ 5 ] emergency treatment and medical safety were improved by formulating standardized clinical management guidelines based on the best available evidence.…”

mentioning

confidence: 99%