journal
https://read.qxmd.com/read/38532235/a-graphical-interface-to-support-low-flow-volatile-anesthesia-implications-for-patient-safety-teaching-and-design-of-anesthesia-information-management-systems
#1
LETTER
James Xie, Megan Jablonski, Joan Smith, Andres Navedo
No abstract text is available yet for this article.
March 27, 2024: Journal of Medical Systems
https://read.qxmd.com/read/38530526/effectiveness-of-implementing-modified-early-warning-system-and-rapid-response-team-for-general-ward-inpatients
#2
JOURNAL ARTICLE
Wen-Jinn Liaw, Tzu-Jung Wu, Li-Hua Huang, Chiao-Shan Chen, Ming-Che Tsai, I-Chen Lin, Yi-Han Liao, Wei-Chih Shen
This retrospective study assessed the effectiveness and impact of implementing a Modified Early Warning System (MEWS) and Rapid Response Team (RRT) for inpatients admitted to the general ward (GW) of a medical center. This study included all inpatients who stayed in GWs from Jan. 2017 to Feb. 2022. We divided inpatients into GWnon-MEWS and GWMEWS groups according to MEWS and RRT implementation in Aug. 2019. The primary outcome, unexpected deterioration, was defined by unplanned admission to intensive care units...
March 26, 2024: Journal of Medical Systems
https://read.qxmd.com/read/38530457/a-dynamic-marketplace-for-distributing-anesthesia-call-a-quality-improvement-initiative
#3
JOURNAL ARTICLE
Mark A Deshur, Noah Ben-Isvy, Chi Wang, Andrew R Locke, Mohammed Minhaj, Steven B Greenberg
Anesthesiologists have a significant responsibility to provide care at all hours of the day, including nights, weekends, and holidays. This call burden carries a significant lifestyle constraint that can impact relationships, affect provider wellbeing, and has been associated with provider burnout. This quality improvement study analyzes the effects of a dynamic call marketplace, which allows anesthesiologists to specify how much call they would like to take across a spectrum of hypothetical compensation levels, from very low to very high...
March 26, 2024: Journal of Medical Systems
https://read.qxmd.com/read/38526807/secure-cloud-based-electronic-health-records-cross-patient-block-level-deduplication-with-blockchain-auditing
#4
JOURNAL ARTICLE
K Vivekrabinson, K Ragavan, P Jothi Thilaga, J Bharath Singh
In today's data-driven world, the exponential growth of digital information poses significant challenges in data management. In recent years, the adoption of cloud-based Electronic Health Records (EHR) sharing schemes has yielded numerous advantages like improved accessibility, availability, and enhanced interoperability. However, the centralized nature of cloud storage presents challenges in terms of information storage, privacy protection, and security. Despite several approaches that have been presented to ensure secure deduplication of similar EHRs, the validation of data integrity without a third-party auditor (TPA) remains a persistent task...
March 25, 2024: Journal of Medical Systems
https://read.qxmd.com/read/38509201/assessing-the-efficacy-of-a-novel-massive-open-online-soft-skills-course-for-south-asian-healthcare-professionals
#5
JOURNAL ARTICLE
Aditya Mahadevan, Ronald Rivera, Mahan Najhawan, Soheil Saadat, Matthew Strehlow, G V Ramana Rao, Julie Youm
In healthcare professions, soft skills contribute to critical thinking, decision-making, and patient-centered care. While important to the delivery of high-quality medical care, soft skills are often underemphasized during healthcare training in low-and-middle-income countries. Despite South Asia's large population, the efficacy and viability of a digital soft skills curriculum for South Asian healthcare practitioners has not been studied to date. We hypothesized that a web-based, multilingual, soft skills course could aid the understanding and application of soft skills to improve healthcare practitioner knowledge, confidence, attitudes, and intent-to-change clinical practice...
March 21, 2024: Journal of Medical Systems
https://read.qxmd.com/read/38488884/effects-of-intra-operative-cardiopulmonary-variability-on-post-operative-pulmonary-complications-in-major-non-cardiac-surgery-a-retrospective-cohort-study
#6
JOURNAL ARTICLE
Sylvia Ranjeva, Alexander Nagebretsky, Gabriel Odozynski, Ana Fernandez-Bustamante, Gyorgy Frendl, R Alok Gupta, Juraj Sprung, Bala Subramaniam, Ricardo Martinez Ruiz, Karsten Bartels, Jadelis Giquel, Jae-Woo Lee, Timothy Houle, Marcos Francisco Vidal Melo
Intraoperative cardiopulmonary variables are well-known predictors of postoperative pulmonary complications (PPC), traditionally quantified by median values over the duration of surgery. However, it is unknown whether cardiopulmonary instability, or wider intra-operative variability of the same metrics, is distinctly associated with PPC risk and severity. We leveraged a retrospective cohort of adults (n = 1202) undergoing major non-cardiothoracic surgery. We used multivariable logistic regression to evaluate the association of two outcomes (1)moderate-or-severe PPC and (2)any PPC with two sets of exposure variables- (a)variability of cardiopulmonary metrics (inter-quartile range, IQR) and (b)median intraoperative cardiopulmonary metrics...
March 15, 2024: Journal of Medical Systems
https://read.qxmd.com/read/38456950/3d-cnn-based-deep-learning-model-based-explanatory-prognostication-in-patients%C3%A2-with-multiple-myeloma-using-whole-body-mri
#7
JOURNAL ARTICLE
Kento Morita, Shigehiro Karashima, Toshiki Terao, Kotaro Yoshida, Takeshi Yamashita, Takeshi Yoroidaka, Mikoto Tanabe, Tatsuya Imi, Yoshitaka Zaimoku, Akiyo Yoshida, Hiroyuki Maruyama, Noriko Iwaki, Go Aoki, Takeharu Kotani, Ryoichi Murata, Toshihiro Miyamoto, Youichi Machida, Kosei Matsue, Hidetaka Nambo, Hiroyuki Takamatsu
Although magnetic resonance imaging (MRI) data of patients with multiple myeloma (MM) are used to predict prognosis, few reports have applied artificial intelligence (AI) techniques for this purpose. We aimed to analyze whole-body diffusion-weighted MRI data using three-dimensional (3D) convolutional neural networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM), an explainable AI, to predict prognosis and explore the factors involved in prediction. We retrospectively analyzed the MRI data of a total of 142 patients with MM obtained from two medical centers...
March 8, 2024: Journal of Medical Systems
https://read.qxmd.com/read/38441786/virtual-reality-for-the-management-of-pain-and-anxiety-in-patients-undergoing-implantation-of-pacemaker-or-implantable-cardioverter-defibrillator-a-randomized-study
#8
RANDOMIZED CONTROLLED TRIAL
Fabien Squara, Jules Bateau, Didier Scarlatti, Sok-Sithikun Bun, Pamela Moceri, Emile Ferrari
BACKGROUND: The Virtual Reality Headset (VRH) is a device aiming at improving patient's comfort by reducing pain and anxiety during medical interventions. Its interest during cardiac implantable electronic devices (CIED) implant procedures has not been studied. METHODS: We randomized consecutive patients admitted for pacemaker or Implantable Cardioverter Defibrillator (ICD) at our center to either standard analgesia care (STD-Group), or to VRH (VRH-Group). Patients in the STD-Group received intra-venous paracetamol (1 g) 60 min before the procedure, and local anesthesia was performed with lidocaine...
March 5, 2024: Journal of Medical Systems
https://read.qxmd.com/read/38441727/quantum-machine-based-decision-support-system-for-the-detection-of-schizophrenia-from-eeg-records
#9
JOURNAL ARTICLE
Gamzepelin Aksoy, Grégoire Cattan, Subrata Chakraborty, Murat Karabatak
Schizophrenia is a serious chronic mental disorder that significantly affects daily life. Electroencephalography (EEG), a method used to measure mental activities in the brain, is among the techniques employed in the diagnosis of schizophrenia. The symptoms of the disease typically begin in childhood and become more pronounced as one grows older. However, it can be managed with specific treatments. Computer-aided methods can be used to achieve an early diagnosis of this illness. In this study, various machine learning algorithms and the emerging technology of quantum-based machine learning algorithm were used to detect schizophrenia using EEG signals...
March 5, 2024: Journal of Medical Systems
https://read.qxmd.com/read/38411833/knowledge-perceptions-and-attitude-of-researchers-towards-using-chatgpt-in-research
#10
JOURNAL ARTICLE
Ahmed Samir Abdelhafiz, Asmaa Ali, Ayman Mohamed Maaly, Hany Hassan Ziady, Eman Anwar Sultan, Mohamed Anwar Mahgoub
INTRODUCTION: ChatGPT, a recently released chatbot from OpenAI, has found applications in various aspects of life, including academic research. This study investigated the knowledge, perceptions, and attitudes of researchers towards using ChatGPT and other chatbots in academic research. METHODS: A pre-designed, self-administered survey using Google Forms was employed to conduct the study. The questionnaire assessed participants' knowledge of ChatGPT and other chatbots, their awareness of current chatbot and artificial intelligence (AI) applications, and their attitudes towards ChatGPT and its potential research uses...
February 27, 2024: Journal of Medical Systems
https://read.qxmd.com/read/38411689/leveraging-large-language-models-for-clinical-abbreviation-disambiguation
#11
JOURNAL ARTICLE
Manda Hosseini, Mandana Hosseini, Reza Javidan
Clinical abbreviation disambiguation is a crucial task in the biomedical domain, as the accurate identification of the intended meanings or expansions of abbreviations in clinical texts is vital for medical information retrieval and analysis. Existing approaches have shown promising results, but challenges such as limited instances and ambiguous interpretations persist. In this paper, we propose an approach to address these challenges and enhance the performance of clinical abbreviation disambiguation. Our objective is to leverage the power of Large Language Models (LLMs) and employ a Generative Model (GM) to augment the dataset with contextually relevant instances, enabling more accurate disambiguation across diverse clinical contexts...
February 27, 2024: Journal of Medical Systems
https://read.qxmd.com/read/38393660/neuroign-explainable-multimodal-image-guided-system-for-precise-brain-tumor-surgery
#12
JOURNAL ARTICLE
Ramy A Zeineldin, Mohamed E Karar, Oliver Burgert, Franziska Mathis-Ullrich
Precise neurosurgical guidance is critical for successful brain surgeries and plays a vital role in all phases of image-guided neurosurgery (IGN). Neuronavigation software enables real-time tracking of surgical tools, ensuring their presentation with high precision in relation to a virtual patient model. Therefore, this work focuses on the development of a novel multimodal IGN system, leveraging deep learning and explainable AI to enhance brain tumor surgery outcomes. The study establishes the clinical and technical requirements of the system for brain tumor surgeries...
February 23, 2024: Journal of Medical Systems
https://read.qxmd.com/read/38386137/in-house-intraoperative-monitoring-in-neurosurgery-in-england-benefits-and-challenges
#13
JOURNAL ARTICLE
Menaka Pasangy Paranathala, Stephan Jaiser, Mohammed Akbar Hussain, Ana Mirallave-Pescador, Christopher J A Cowie, Mark R Baker, Damian Holliman, Charles Alexander Fry
BACKGROUND: Intraoperative neurophysiological monitoring (IOM) is a valuable adjunct for neurosurgical operative techniques, and has been shown to improve clinical outcomes in cranial and spinal surgery. It is not necessarily provided by NHS hospitals so may be outsourced to private companies, which are expensive and at cost to the NHS trusts. We discuss the benefits and challenges of developing an in-house service. METHODS: We surveyed NHS neurosurgical departments across England regarding their expenditure on IOM over the period January 2018 - December 2022 on cranial neurosurgery and spinal surgery...
February 22, 2024: Journal of Medical Systems
https://read.qxmd.com/read/38367119/transformer-models-in-healthcare-a-survey-and-thematic-analysis-of-potentials-shortcomings-and-risks
#14
REVIEW
Kerstin Denecke, Richard May, Octavio Rivera-Romero
Large Language Models (LLMs) such as General Pretrained Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT), which use transformer model architectures, have significantly advanced artificial intelligence and natural language processing. Recognized for their ability to capture associative relationships between words based on shared context, these models are poised to transform healthcare by improving diagnostic accuracy, tailoring treatment plans, and predicting patient outcomes...
February 17, 2024: Journal of Medical Systems
https://read.qxmd.com/read/38366043/the-breakthrough-of-large-language-models-release-for-medical-applications-1-year-timeline-and-perspectives
#15
REVIEW
Marco Cascella, Federico Semeraro, Jonathan Montomoli, Valentina Bellini, Ornella Piazza, Elena Bignami
Within the domain of Natural Language Processing (NLP), Large Language Models (LLMs) represent sophisticated models engineered to comprehend, generate, and manipulate text resembling human language on an extensive scale. They are transformer-based deep learning architectures, obtained through the scaling of model size, pretraining of corpora, and computational resources. The potential healthcare applications of these models primarily involve chatbots and interaction systems for clinical documentation management, and medical literature summarization (Biomedical NLP)...
February 17, 2024: Journal of Medical Systems
https://read.qxmd.com/read/38358554/text-mining-and-video-analytics-of-covid-19-narratives-shared-by-patients-on-youtube
#16
JOURNAL ARTICLE
Ranganathan Chandrasekaran, Karthik Konaraddi, Sakshi S Sharma, Evangelos Moustakas
This study explores how individuals who have experienced COVID-19 share their stories on YouTube, focusing on the nature of information disclosure, public engagement, and emotional impact pertaining to consumer health. Using a dataset of 186 YouTube videos, we used text mining and video analytics techniques to analyze textual transcripts and visual frames to identify themes, emotions, and their relationship with viewer engagement metrics. Findings reveal eight key themes: infection origins, symptoms, treatment, mental well-being, isolation, prevention, government directives, and vaccination...
February 15, 2024: Journal of Medical Systems
https://read.qxmd.com/read/38353872/an-mhealth-application-in-german-health-care-system-importance-of-user-participation-in-the-development-process
#17
JOURNAL ARTICLE
Peter Bickmann, Ingo Froböse, Christopher Grieben
This paper addresses the challenges and solutions in developing a holistic prevention mobile health application (mHealth app) for Germany's healthcare sector. Despite Germany's lag in healthcare digitalization, the app aims to enhance primary prevention in physical activity, nutrition, and stress management. A significant focus is on user participation and usability to counter the prevalent issue of user attrition in mHealth applications, as described by Eysenbach's 'law of attrition'. The development process, conducted in a scientific and university context, faces constraints like limited budgets and external service providers...
February 14, 2024: Journal of Medical Systems
https://read.qxmd.com/read/38353755/artificial-intelligence-in-operating-room-management
#18
REVIEW
Valentina Bellini, Michele Russo, Tania Domenichetti, Matteo Panizzi, Simone Allai, Elena Giovanna Bignami
This 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...
February 14, 2024: Journal of Medical Systems
https://read.qxmd.com/read/38329594/measuring-the-coverage-of-the-hl7%C3%A2-fhir%C3%A2-standard-in-supporting-data-acquisition-for-3-public-health-registries
#19
JOURNAL ARTICLE
Manju Bikkanuri, Taiquitha T Robins, Lori Wong, Emel Seker, Melody L Greer, Tremaine B Williams, Maryam Y Garza
With the increasing need for timely submission of data to state and national public health registries, current manual approaches to data acquisition and submission are insufficient. In clinical practice, federal regulations are now mandating the use of data messaging standards, i.e., the Health Level Seven (HL7® ) Fast Healthcare Interoperability Resources (FHIR® ) standard, to facilitate the electronic exchange of clinical (patient) data. In both research and public health practice, we can also leverage FHIR® ‒ and the infrastructure already in place for supporting exchange of clinical practice data ‒ to enable seamless exchange between the electronic medical record and public health registries...
February 8, 2024: Journal of Medical Systems
https://read.qxmd.com/read/38305947/chatgpt-for-parents-of-children-seeking-emergency-care-so-much-hope-so-much-caution
#20
LETTER
Julie Yu, Clyde Matava
No abstract text is available yet for this article.
February 2, 2024: Journal of Medical Systems
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