1,721,018 research outputs found
A panoramic flight through artificial intelligence in anesthesiology and critical care medicine: The present of an intelligent future
No full textInnovative Technologies for Smarter and Efficient Operating Room Scheduling
No full textAn optimized scheduling system for surgical procedures is considered fundamental for maximizing hospital resource utilization and improving patient outcomes. The integration of Artificial Intelligence (AI) tools and New Technologies is paramount in this project to enable personalized patient care and optimize perioperative clinical pathways. We read with interest the manuscript by Parks et al., which developed a predictive model of surgical case durations. The model appears to adopt a pragmatic approach by analyzing tangible variables and undergoing validation across various types of surgical procedures, which suggests potential avenues for enhancing efficiency and sustainability in healthcare practices. However, we have some observations, particularly regarding the feasibility and practical implementation of the proposed model. A key limitation of the model is the precise definition of surgical duration, which requires further specification. To effectively translate the model into a practical scheduling approach, it is essential to consider total Operating Room (OR) occupancy time as a critical determinant of surgical planning and resource allocation. This includes not only the actual procedural time but also preoperative preparation, anesthesia induction and recovery, cleaning, and material restocking, all of which significantly impact overall scheduling efficiency. Another critical aspect concerns the quality and reliability of the input data, which is fundamental for ensuring the accuracy and effectiveness of the model. Furthermore, the adoption of new technologies should be regarded not merely as an innovation but as a means to develop high-performance, efficient tools that enhance current clinical practice. In this context, machine learning models should not only serve as analytical instruments but also as actionable tools, enabling the transition from predictive insights to strategic planning and optimized scheduling, ultimately improving decision-making and resource allocation. While making accurate predictions is a good starting point, maintaining an active AI model requires investment in resources, such as an increase in the number of surgical cases compared to the current organizational system. It may be beneficial to consider the creation of a multidisciplinary group that could promote the integration of AI with other emerging technologies
The health technology assessment in the artificial intelligence era: the AI surgical department
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Human Judgment versus ChatGPT: Preserving the Essence of Medical Competence in the Age of Artificial Intelligence
No full textEnhancing cardiac arrest response: Evaluating GPT-4o's advanced voice interaction system
No full textExploring Artificial Intelligence in Anesthesia: A Primer on Ethics, and Clinical Applications
No full textThe field of anesthesia has always been at the forefront of innovation and technology, and the integration of Artificial Intelligence (AI) represents the next frontier in anesthesia care. The use of AI and its subtypes, such as machine learning, has the potential to improve efficiency, reduce costs, and ameliorate patient outcomes. AI can assist with decision making, but its primary advantage lies in empowering anesthesiologists to adopt a proactive approach to address clinical issues. The potential uses of AI in anesthesia can be schematically grouped into clinical decision support and pharmacologic and mechanical robotic applications. Tele-anesthesia includes strategies of telemedicine, as well as device networking, for improving logistics in the operating room, and augmented reality approaches for training and assistance. Despite the growing scientific interest, further research and validation are needed to fully understand the benefits and limitations of these applications in clinical practice. Moreover, the ethical implications of AI in anesthesia must also be considered to ensure that patient safety and privacy are not compromised. This paper aims to provide a comprehensive overview of AI in anesthesia, including its current and potential applications, and the ethical considerations that must be considered to ensure the safe and effective use of the technology
Postoperative vocal fold dysfunction in covid-19 era: are we still in time for a recovery?
No full textIntegrating data science and neural architecture techniques for automatic pain assessment in critically ill patients
No full textWearable devices as part of postoperative early warning score systems: a scoping review
No full textPostoperative deterioration is often preceded by abnormalities in vital parameters, but limited resources prevent their continuous monitoring in patients with no indication to ICU admission. The development of new technologies allowed the introduction of wearable devices (WDs), enabling the possibility of postoperative monitoring in surgical wards. We performed a Scoping Review to determine the current use of wearable devices as part of Continuous Remote Early Warning Score (CREWS) systems and their efficiency during postoperative period. This Scoping Review was conducted according to PRISMA-ScR guidelines. PICO framework was used before the search to define the review protocol. Systematic literature research has been performed on PubMed, MeSH, MEDLINE and Embase, considering a period between 2018 and February 2024. Prospective and retrospective studies involving patients undergoing cardiac and non-cardiac surgery are included. A total of 10 articles were included in the review. 11 different CE/FDA approved wearable devices were used in the studies analyzed. In all studies the WDs were applied the day of the surgery. The use of WDs as part of CREWS systems is feasible and safe. Furthermore, with the aid of other technologies (LoRa and Artificial Intelligence), they shorten Length of Stay (LOS) and reduce the number of ICU admissions with a reduction in healthcare costs. Continuous monitoring in surgical departments can facilitate the correct and timely identification of postoperative complications. This article is a starting point for the development of new protocols and for the application of these monitoring systems in clinical practice.
Keywords: Early warning system; IoT; Postoperative care; Postoperative monitoring; Telemedicine; Wearable devices
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