Critical care clinics
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Critical care clinics · Oct 2023
ReviewDesigning and Implementing "Living and Breathing" Clinical Trials: An Overview and Lessons Learned from the COVID-19 Pandemic.
The practice of medicine is characterized by uncertainty, and the findings of randomized clinical trials (RCTs) are meant to help curb that uncertainty. Traditional RCTs, however, have many limitations. ⋯ These new designs recognize uncertainty permeates medical decision making and aim to capitalize on modern health system infrastructure to integrate investigation as a component of care delivery. This article provides an overview of "living, breathing" trials, including current state, anticipated developments, and areas of controversy.
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Critical care clinics · Oct 2023
ReviewImplementing Artificial Intelligence: Assessing the Cost and Benefits of Algorithmic Decision-Making in Critical Care.
This article provides an overview of the most useful artificial intelligence algorithms developed in critical care, followed by a comprehensive outline of the benefits and limitations. We begin by describing how nurses and physicians might be aided by these new technologies. We then move to the possible changes in clinical guidelines with personalized medicine that will allow tailored therapies and probably will increase the quality of the care provided to patients. Finally, we describe how artificial intelligence models can unleash researchers' minds by proposing new strategies, by increasing the quality of clinical practice, and by questioning current knowledge and understanding.
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Data science has the potential to greatly enhance efforts to translate evidence into practice in critical care. The intensive care unit is a data-rich environment enabling insight into both patient-level care patterns and clinician-level treatment patterns. By applying artificial intelligence to these novel data sources, implementation strategies can be tailored to individual patients, individual clinicians, and individual situations, revealing when evidence-based practices are missed and facilitating context-sensitive clinical decision support. To achieve these goals, technology developers should work closely with clinicians to create unbiased applications that are integrated into the clinical workflow.
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Critical care clinics · Oct 2023
ReviewHow Electronic Medical Record Integration Can Support More Efficient Critical Care Clinical Trials.
Large volumes of data are collected on critically ill patients, and using data science to extract information from the electronic medical record (EMR) and to inform the design of clinical trials represents a new opportunity in critical care research. Using improved methods of phenotyping critical illnesses, subject identification and enrollment, and targeted treatment group assignment alongside newer trial designs such as adaptive platform trials can increase efficiency while lowering costs. Some tools such as the EMR to automate data collection are already in use. Refinement of data science approaches in critical illness research will allow for better clinical trials and, ultimately, improved patient outcomes.
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Critical care clinics · Oct 2023
ReviewMachine Learning of Physiologic Waveforms and Electronic Health Record Data: A Large Perioperative Data Set of High-Fidelity Physiologic Waveforms.
Perioperative morbidity and mortality are significantly associated with both static and dynamic perioperative factors. The studies investigating static perioperative factors have been reported; however, there are a limited number of previous studies and data sets analyzing dynamic perioperative factors, including physiologic waveforms, despite its clinical importance. To fill the gap, the authors introduce a novel large size perioperative data set: Machine Learning Of physiologic waveforms and electronic health Record Data (MLORD) data set. They also provide a concise tutorial on machine learning to illustrate predictive models trained on complex and diverse structures in the MLORD data set.