scholarly journals Intensive care unit procedures: cost savings and patient safety

Critical Care ◽  
10.1186/cc377 ◽  
1999 ◽  
Vol 3 (Suppl 1) ◽  
pp. P002
Author(s):  
NW Knudsen ◽  
MW Sebastian ◽  
RA Perez-Tamayo ◽  
WL Johanson ◽  
SN Vaslef
Keyword(s):  
Intensive Care Unit ◽  
Patient Safety ◽  
Intensive Care ◽  
Cost Savings

Atmung – Standardisierte Checkliste macht Intubation von Neugeborenen sicherer

2017 ◽  
Vol 22 (03) ◽  
pp. 124-125
Author(s):  
Maria Weiß
Keyword(s):  
Intensive Care Unit ◽  
Patient Safety ◽  
Intensive Care ◽  
Neonatal Intensive Care Unit ◽  
Neonatal Intensive Care ◽  
Improve Patient Safety ◽  
Improve Patient

Hatch LD. et al. Intervention To Improve Patient Safety During Intubation in the Neonatal Intensive Care Unit. Pediatrics 2016; 138: e20160069 Kinder auf der Neugeborenen-Intensivstation sind besonders durch Komplikationen während des Krankenhausaufenthaltes gefährdet. Dies gilt auch für die Intubation, die relativ häufig mit unerwünschten Ereignissen einhergeht. US-amerikanische Neonatologen haben jetzt untersucht, durch welche Maßnahmen sich die Komplikationsrate bei Intubationen in ihrem Perinatal- Zentrum senken lässt.

scholarly journals Patterns of Cost for Patients Dying in the Intensive Care Unit and Implications for Cost Savings of Palliative Care Interventions

2016 ◽  
Vol 19 (11) ◽  
pp. 1171-1178 ◽  
Author(s):  
Nita Khandelwal ◽  
David Benkeser ◽  
Norma B. Coe ◽  
Ruth A. Engelberg ◽  
Joan M. Teno ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Palliative Care ◽  
Intensive Care ◽  
Cost Savings

scholarly journals Development and Validation of Unplanned Extubation Prediction Models Using Intensive Care Unit Data: Comparative Machine Learning Study (Preprint)

2020 ◽  
Author(s):  
Sujeong Hur ◽  
Ji Young Min ◽  
Junsang Yoo ◽  
Kyunga Kim ◽  
Chi Ryang Chung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Patient Safety ◽  
Intensive Care ◽  
Predictive Value ◽  
Prediction Models ◽  
Support Vector ◽  
Unplanned Extubation ◽  
Electronic Health Record Data ◽  
Icu Patients

BACKGROUND Patient safety in the intensive care unit (ICU) is one of the most critical issues, and unplanned extubation (UE) is considered as the most adverse event for patient safety. Prevention and early detection of such an event is an essential but difficult component of quality care. OBJECTIVE This study aimed to develop and validate prediction models for UE in ICU patients using machine learning. METHODS This study was conducted an academic tertiary hospital in Seoul. The hospital had approximately 2,000 inpatient beds and 120 intensive care unit (ICU) beds. The number of patients, on daily basis, was approximately 9,000 for the out-patient. The number of annual ICU admission was approximately 10,000. We conducted a retrospective study between January 1, 2010 and December 31, 2018. A total of 6,914 extubation cases were included. We developed an unplanned extubation prediction model using machine learning algorithms, which included random forest (RF), logistic regression (LR), artificial neural network (ANN), and support vector machine (SVM). For evaluating the model’s performance, we used area under the receiver operator characteristic curve (AUROC). Sensitivity, specificity, positive predictive value negative predictive value, and F1-score were also determined for each model. For performance evaluation, we also used calibration curve, the Brier score, and the Hosmer-Lemeshow goodness-of-fit statistic. RESULTS Among the 6,914 extubation cases, 248 underwent UE. In the UE group, there were more males than females, higher use of physical restraints, and fewer surgeries. The incidence of UE was more likely to occur during the night shift compared to the planned extubation group. The rate of reintubation within 24 hours and hospital mortality was higher in the UE group. The UE prediction algorithm was developed, and the AUROC for RF was 0.787, for LR was 0.762, for ANN was 0.762, and for SVM was 0.740. CONCLUSIONS We successfully developed and validated machine learning-based prediction models to predict UE in ICU patients using electronic health record data. The best AUROC was 0.787, which was obtained using RF. CLINICALTRIAL N/A

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