1,720,955 research outputs found
Innovative 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
ERAS and the challenge of the new technologies
No full textThe integration of artificial intelligence (AI) and all new technologies (NTs) into enhanced recovery after surgery (ERAS) protocols offers significant opportunities to address implementation challenges and improve patient care. Despite the proven benefits of ERAS, limitations such as resistance to change, resource constraints, and poor interdepartmental communication persist. AI can play a crucial role in overcoming ERAS implementation barriers by simplifying clinical plans, ensuring high compliance, and creating patient-centered approaches. Advanced techniques like machine learning and deep learning can optimize preoperative management, intraoperative phases, and postoperative recovery pathways. AI integration in ERAS protocols has the potential to revolutionize perioperative medicine by enabling personalized patient care, enhancing monitoring strategies, and improving clinical decision-making. The technology can address common postoperative challenges by developing individualized ERAS plans based on patient risk factors and optimizing perioperative processes. While challenges remain, including the need for external validation and data security, the authors suggest that the combination of AI, NTs, and ERAS protocols should become an integral part of routine clinical practice. This integration ultimately leads to improved patient outcomes and satisfaction in surgical care, transforming the perioperative medicine landscape by tailoring pathways to patients’ needs
Artificial Intelligence in Operating Room Management
No full textThis systematic review examines the recent use of artificial intelligence, particularly machine learning, in the management of operating rooms. A total of 22 selected studies from February 2019 to September 2023 are analyzed. The review emphasizes the significant impact of AI on predicting surgical case durations, optimizing post-anesthesia care unit resource allocation, and detecting surgical case cancellations. Machine learning algorithms such as XGBoost, random forest, and neural networks have demonstrated their effectiveness in improving prediction accuracy and resource utilization. However, challenges such as data access and privacy concerns are acknowledged. The review highlights the evolving nature of artificial intelligence in perioperative medicine research and the need for continued innovation to harness artificial intelligence’s transformative potential for healthcare administrators, practitioners, and patients. Ultimately, artificial intelligence integration in operative room management promises to enhance healthcare efficiency and patient outcomes
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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