Technologies to Optimize the Care of Severe COVID-19 Patients for Health Care Providers Challenged by Limited Resources
Bibliographic record
Abstract
Health care systems are belligerently responding to the new coronavirus disease 2019 (COVID-19). The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a specific condition, whose distinctive features are severe hypoxemia associated with (>50% of cases) normal respiratory system compliance. When a patient requires intubation and invasive ventilation, the outcome is poor, and the length of stay in the intensive care unit (ICU) is usually 2 or 3 weeks. In this article, the authors review several technological devices, which could support health care providers at the bedside to optimize the care for COVID-19 patients who are sedated, paralyzed, and ventilated. Particular attention is provided to the use of videolaryngoscopes (VL) because these can assist anesthetists to perform a successful intubation outside the ICU while protecting health care providers from this viral infection. Authors will also review processed electroencephalographic (EEG) monitors which are used to better titrate sedation and the train-of-four monitors which are utilized to better administer neuromuscular blocking agents in the view of sparing limited pharmacological resources. COVID-19 can rapidly exhaust human and technological resources too within the ICU. This review features a series of technological advancements that can significantly improve the care of patients requiring isolation. The working conditions in isolation could cause gaps or barriers in communication, fatigue, and poor documentation of provided care. The available technology has several advantages including (a) facilitating appropriate paperless documentation and communication between all health care givers working in isolation rooms or large isolation areas; (b) testing patients and staff at the bedside using smart point-of-care diagnostics (SPOCD) to confirm COVID-19 infection; (c) allowing diagnostics and treatment at the bedside through point-of-care ultrasound (POCUS) and thromboelastography (TEG); (d) adapting the use of anesthetic machines and the use of volatile anesthetics. Implementing technologies for safeguarding health care providers as well as monitoring the limited pharmacological resources are paramount. Only by leveraging new technologies, it will be possible to sustain and support health care systems during the expected long course of this pandemic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".