[HTML][HTML] Role of artificial intelligence in patient safety outcomes: systematic literature review
Background: Artificial intelligence (AI) provides opportunities to identify the health risks of
patients and thus influence patient safety outcomes. Objective: The purpose of this …
patients and thus influence patient safety outcomes. Objective: The purpose of this …
Technological distractions (part 2): a summary of approaches to manage clinical alarms with intent to reduce alarm fatigue
Objective: Alarm fatigue is a widely recognized safety and quality problem where exposure
to high rates of clinical alarms results in desensitization leading to dismissal of or slowed …
to high rates of clinical alarms results in desensitization leading to dismissal of or slowed …
Machine-learning algorithm to predict hypotension based on high-fidelity arterial pressure waveform analysis.
Methods The algorithm was developed with two different data sources:(1) a retrospective
cohort, used for training, consisting of 1,334 patients' records with 545,959 min of arterial …
cohort, used for training, consisting of 1,334 patients' records with 545,959 min of arterial …
[HTML][HTML] Future medical artificial intelligence application requirements and expectations of physicians in German university hospitals: web-based survey
O Maassen, S Fritsch, J Palm, S Deffge, J Kunze… - Journal of medical …, 2021 - jmir.org
Background The increasing development of artificial intelligence (AI) systems in medicine
driven by researchers and entrepreneurs goes along with enormous expectations for …
driven by researchers and entrepreneurs goes along with enormous expectations for …
Effects of monitor alarm management training on nurses' alarm fatigue: A randomised controlled trial
J Bi, X Yin, H Li, R Gao, Q Zhang… - Journal of Clinical …, 2020 - Wiley Online Library
Background Chaotic monitor alarm management generates a large number of alarms, which
result in alarm fatigue. Intensive care unit (ICU) nurses are caretakers of critically ill patients …
result in alarm fatigue. Intensive care unit (ICU) nurses are caretakers of critically ill patients …
Applying machine learning to continuously monitored physiological data
The use of machine learning (ML) in healthcare has enormous potential for improving
disease detection, clinical decision support, and workflow efficiencies. In this commentary …
disease detection, clinical decision support, and workflow efficiencies. In this commentary …
Secondary brain injury: predicting and preventing insults
Mortality or severe disability affects the majority of patients after severe traumatic brain injury
(TBI). Adherence to the brain trauma foundation guidelines has overall improved outcomes; …
(TBI). Adherence to the brain trauma foundation guidelines has overall improved outcomes; …
Automated continuous noninvasive ward monitoring: future directions and challenges
AK Khanna, P Hoppe, B Saugel - Critical Care, 2019 - Springer
Automated continuous noninvasive ward monitoring may enable subtle changes in vital
signs to be recognized. There is already some evidence that automated ward monitoring …
signs to be recognized. There is already some evidence that automated ward monitoring …
Computational approaches to alleviate alarm fatigue in intensive care medicine: A systematic literature review
Patient monitoring technology has been used to guide therapy and alert staff when a vital
sign leaves a predefined range in the intensive care unit (ICU) for decades. However, large …
sign leaves a predefined range in the intensive care unit (ICU) for decades. However, large …
Using artificial intelligence to predict prolonged mechanical ventilation and tracheostomy placement
Background Early identification of critically ill patients who will require prolonged
mechanical ventilation (PMV) has proven to be difficult. The purpose of this study was to use …
mechanical ventilation (PMV) has proven to be difficult. The purpose of this study was to use …