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

2021 ◽  
pp. 30-38
Author(s):  
Duarte de Brito Tiago Marçal Pedro ◽  
Pacheco Pereira Maria ◽  
Machado Humberto
Keyword(s):  
Machine Learning ◽  
Clinical Pathways ◽  
Hospital Setting ◽  
Rapid Response ◽  
Clinical Deterioration ◽  
Failure To Rescue ◽  
Rapid Response Teams ◽  
Hospital Factors ◽  
Time Of Admission ◽  
Improve Patient

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.

Rapid Response Teams and Failure to Rescue

2012 ◽  
Vol 27 (4) ◽  
pp. 352-358 ◽  
Author(s):  
Jere A. Hammer ◽  
Terry L. Jones ◽  
Sharon A. Brown
Keyword(s):  
Rapid Response ◽  
Failure To Rescue ◽  
Rapid Response Teams

Overcoming Barriers Impeding Nurse Activation of Rapid Response Teams

2019 ◽  
Vol 24 (3) ◽  
Author(s):  
Wendy Clayton
Keyword(s):  
Patient Outcomes ◽  
Clinical Judgment ◽  
Rapid Response ◽  
Rapid Response Teams ◽  
Bedside Nurse ◽  
Patient Deterioration ◽  
Rapid Response Systems ◽  
Trained Personnel ◽  
Response Systems ◽  
Improve Patient

Management of rapid patient deterioration requires prompt recognition and swift response by bedside nurses and specially trained personnel, who successfully intervene to improve patient outcomes. Timely recognition and activation of rapid response mechanisms requires prudent nursing care. When patient needs and nurse competencies are unbalanced, patient outcomes decline and nurse confidence diminishes. This article offers a brief background of rapid response, including the supporting theoretical framework. Also discussed are barriers to nursing action that result in synergistic imbalance, including: bedside nurse competence to recognize patient deterioration and activate rapid response systems; bedside nurse clinical judgment, interdisciplinary teamwork; and organizational culture. The article includes implications for practice aims to address identified barriers and improve patient outcomes.

RAPID RESPONSE TEAMS IMPROVE PATIENT CARE AND DO NOT IMPEDE RESIDENT EDUCATION: A REPORT OF A RESIDENT SURVEY.

2006 ◽  
Vol 34 ◽  
pp. A120
Author(s):  
Ilan S Rubinfeld ◽  
Bruno DiGiovine ◽  
Peter Watson ◽  
John Mailey ◽  
Gwenn Gnam ◽  
...  
Keyword(s):  
Patient Care ◽  
Resident Education ◽  
Improve Patient Care ◽  
Rapid Response ◽  
Rapid Response Teams ◽  
Improve Patient

Rapid Response Teams as a Patient Safety Practice for Failure to Rescue

JAMA ◽  
2021 ◽  
Author(s):  
Chelsea P. Fischer ◽  
Karl Y. Bilimoria ◽  
Amir A. Ghaferi
Keyword(s):  
Patient Safety ◽  
Rapid Response ◽  
Failure To Rescue ◽  
Rapid Response Teams ◽  
Safety Practice

Data Fail to Back Rapid Response Teams

2011 ◽  
Vol 4 (6) ◽  
pp. 8-9
Author(s):  
MARY ANN MOON
Keyword(s):  
Rapid Response ◽  
Rapid Response Teams

Predicting the Risk of Clinical Deterioration in Patients with Severe COVID-19 Infection Using Machine Learning

2020 ◽  
Author(s):  
Zhiqiang Zhou ◽  
Wei Li ◽  
Jiajia Qian ◽  
Bin Lin ◽  
Yucen Nan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Deterioration

scholarly journals External validation of an opioid misuse machine learning classifier in hospitalized adult patients

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Majid Afshar ◽  
Brihat Sharma ◽  
Sameer Bhalla ◽  
Hale M. Thompson ◽  
Dmitriy Dligach ◽  
...  
Keyword(s):  
Machine Learning ◽  
Screening Program ◽  
External Validation ◽  
Hospital Setting ◽  
Opioid Misuse ◽  
Original Cohort ◽  
Learning Classifier ◽  
Observational Cohort ◽  
Drug Abuse Screening Test ◽  
Screening Barriers

Abstract Background Opioid misuse screening in hospitals is resource-intensive and rarely done. Many hospitalized patients are never offered opioid treatment. An automated approach leveraging routinely captured electronic health record (EHR) data may be easier for hospitals to institute. We previously derived and internally validated an opioid classifier in a separate hospital setting. The aim is to externally validate our previously published and open-source machine-learning classifier at a different hospital for identifying cases of opioid misuse. Methods An observational cohort of 56,227 adult hospitalizations was examined between October 2017 and December 2019 during a hospital-wide substance use screening program with manual screening. Manually completed Drug Abuse Screening Test served as the reference standard to validate a convolutional neural network (CNN) classifier with coded word embedding features from the clinical notes of the EHR. The opioid classifier utilized all notes in the EHR and sensitivity analysis was also performed on the first 24 h of notes. Calibration was performed to account for the lower prevalence than in the original cohort. Results Manual screening for substance misuse was completed in 67.8% (n = 56,227) with 1.1% (n = 628) identified with opioid misuse. The data for external validation included 2,482,900 notes with 67,969 unique clinical concept features. The opioid classifier had an AUC of 0.99 (95% CI 0.99–0.99) across the encounter and 0.98 (95% CI 0.98–0.99) using only the first 24 h of notes. In the calibrated classifier, the sensitivity and positive predictive value were 0.81 (95% CI 0.77–0.84) and 0.72 (95% CI 0.68–0.75). For the first 24 h, they were 0.75 (95% CI 0.71–0.78) and 0.61 (95% CI 0.57–0.64). Conclusions Our opioid misuse classifier had good discrimination during external validation. Our model may provide a comprehensive and automated approach to opioid misuse identification that augments current workflows and overcomes manual screening barriers.

scholarly journals Multi-Drug Featurization and Deep Learning Improve Patient-Specific Predictions of Adverse Events

2021 ◽  
Vol 18 (5) ◽  
pp. 2600
Author(s):  
Ioannis N. Anastopoulos ◽  
Chloe K. Herczeg ◽  
Kasey N. Davis ◽  
Atray C. Dixit
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Variable Number ◽  
Patient Specific ◽  
Real World Evidence ◽  
Genetic Features ◽  
The Uk ◽  
Therapeutic Compounds ◽  
Improve Patient ◽  
Reaction Profile

While the clinical approval process is able to filter out medications whose utility does not offset their adverse drug reaction profile in humans, it is not well suited to characterizing lower frequency issues and idiosyncratic multi-drug interactions that can happen in real world diverse patient populations. With a growing abundance of real-world evidence databases containing hundreds of thousands of patient records, it is now feasible to build machine learning models that incorporate individual patient information to provide personalized adverse event predictions. In this study, we build models that integrate patient specific demographic, clinical, and genetic features (when available) with drug structure to predict adverse drug reactions. We develop an extensible graph convolutional approach to be able to integrate molecular effects from the variable number of medications a typical patient may be taking. Our model outperforms standard machine learning methods at the tasks of predicting hospitalization and death in the UK Biobank dataset yielding an R2 of 0.37 and an AUC of 0.90, respectively. We believe our model has potential for evaluating new therapeutic compounds for individualized toxicities in real world diverse populations. It can also be used to prioritize medications when there are multiple options being considered for treatment.

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