Telemedicine represents an area of rapid growth in anesthesiology. Remote preoperative evaluation is associated with high patient and physician satisfaction scores, reduced patient travel and wait times, and similar procedure cancellation rates compared with in-person clinic evaluation. Preoperative tele-evaluation has facilitated a return to normal function during the coronavirus disease 2019 (COVID-19) pandemic. Intraoperatively, remote vital sign monitoring and telecommunications technology combined with a care team model allows provision of expert care in areas experiencing a shortage of anesthesiologists. Virtual intensive care units provide overflow capability for postoperative patients, whereas patient smartphones can reduce the need for in-person evaluation.
The electroencephalogram (EEG) can be analyzed in its raw form for characteristic drug-induced patterns of change or summarized using mathematical parameters as a processed electroencephalogram (pEEG). In this article we aim to summarize the contemporary literature pertaining to the commonly available pEEG monitors including the effects of commonly used anesthetic drugs on the EEG and pEEG parameters, pEEG monitor pitfalls, and the clinical implications of pEEG monitoring for anesthesia, pediatrics, and intensive care.
Since the first public demonstration of general anesthesia in 1846, anesthesiology has seen major advancements as a specialty. These include both important technological improvements and the development and implementation of internationally accepted patient safety standards. Together, these ultimately resulted in the recognition of anesthesiology as the leading medical specialty advocating for patient safety. Modern-day anesthesiology faces a new challenge of automated anesthesia delivery. Despite evidence for a more refined and precise delivery of anesthesia through this platform, there is currently no substitute for the presence of an appropriately trained anesthesia clinician to manage the complex interplay of human factors and...
Monitoring, derived from the Latin monere (to warn), meaning continuous measurement of variables over time, is core to the delivery of anesthesia and perioperative care. Currently, the utility of basic standard monitoring is universally accepted as part of a complex man-machine interaction and feedback system aimed at keeping the patient within safe physiologic limits. Perioperative care has always been intimately associated with technological advancements to augment postoperative recovery. Although it may be coincidental, many experts cite improved monitoring as one of the reasons for improved patient safety and perioperative outcomes, as well as enhanced recovery after surgery.
With the tremendous volume of data captured during surgeries and procedures, critical care, and pain management, the field of anesthesiology is uniquely suited for the application of machine learning, neural networks, and closed loop technologies. In the past several years, this area has expanded immensely in both interest and clinical applications. This article provides an overview of the basic tenets of machine learning, neural networks, and closed loop devices, with emphasis on the clinical applications of these technologies.