Various Areas Of Healthcare Research Articles

6.Y.2. AI and health care: what are the capabilities and limitations?

Abstract With the rapid advancement of artificial intelligence (AI) technologies and their implementation in health care, there is a critical need to explore the different applications and implications of AI. This workshop seeks to look at different aspects of AI, both potential applications for AI technology in health, public opinions of use of AI in health care, and the reality of implementing AI in different contexts. Understanding health care approaches to AI from different countries is a unique opportunity for shared learning of innovations across various sectors of health care and patient engagement in order to most effectively engage this emerging field. AI, in whichever form it may be incorporated into health care, has the opportunity to impart meaningful, innovative change, but understanding this new technology comes with important questions. This session will dive into research on AI in various areas of health care by 1) looking at different ways AI is being harnessed in health care; 2) discussing lessons which can be learned and adapted from other countries; 3) discussing promising solutions and approaches to implementing AI in health care; 4) discussing the limitations or concerns of integrating AI in health care. The panel will be composed of 1 moderator and 2 panelists. Panelists are experts in their fields, established researchers and practitioners, and alumni of the Harkness Fellowship from Germany and the United Kingdom. A moderator will briefly frame the discussion for the audience and introduce the speakers and their presentations before leading an interactive, engaging question and answer portion. The coherence of this panel lays in its theme of leveraging AI to enhance healthcare delivery and understanding ways we can work across countries to foster ethical, equitable AI technologies that revolutionize healthcare practices with the ultimate goal of improving patient outcomes and health system efficiencies. Dr. Saira Ghafur from the U.K. will begin by presenting her research on evidence-based reasons for implementing AI in health care and Dr. Benedikt Simon from Germany will close out by discussing his work in AI in Germany and the challenges that have prevented robust integration. Each panelist will speak for roughly 8-10 minutes, with about 5 minutes reserved for moderator and audience questions. Key messages • Sharing cross-country experiences and research enables us to better understand how AI can impact health care. • In weighing both the benefits and challenges or limitations of implementing AI, we can best understand effective approaches to utilizing AI in health care.

ЛЕЧЕБНО-ВОССТАНОВИТЕЛЬНЫЕ И БИОТЕХНОЛОГИЧЕСКИЕ РЕСУРСЫ НАУЧНОЙ МУЗЫКОТЕРАПИИ В РЕШЕНИИ ПРОБЛЕМ БЕСПЛОДИЯ

Scientific music therapy is an advanced therapeutic and preventive direction, widely in demand in various areas of health care, psychological assistance, and social rehabilitation, but still underestimated in reproductive medicine. Complex scientific research conducted over thirty years has helped to better understand the nature of the main mechanisms of music's influence on the neuroendocrine system,vital organs, and cellular structures. On this basis, unique and effective methods of scientific music therapy and promising biotechnologies have been created, the possibilities of which in solving infertility problems are discussed in this article.

Traumatic Brain Injury Patients Admitted on High-Census Days Receive Less Critical Care and Have an Increased Risk for Delirium.

The utilization of healthcare services in a growing population has raised concerns about its impact on clinical outcomes. Studies have shown that increased hospital census is associated with higher admission rates and unnecessary consults, tests, and procedures in various areas of healthcare. Traumatic brain injuries (TBIs), a significant concern due to their potential for long-term disabilities, are commonly encountered in intensive care units (ICUs) and are a leading cause of patient mortality. Despite extensive research on various aspects of TBI, the effect of the patient census on TBI outcomes remains unexplored. This study aims to investigate the relationship between healthcare provider patient census and clinical outcomes in TBI patients at a level I trauma center. A retrospective review was conducted from 2017 to 2022. The mean number of patients per day in the trauma service was determined, with patients below this average considered to be present on low-census days and those above it on high-census days. Patient demographics, mechanisms of injury, vital signs, TBI severity, and associated injuries were analyzed. Adjusted regression analyses were conducted. Over the study period, 1,527 TBI patients were identified. Demographics were similar between patients admitted on high- and low-census days. Patients with moderate TBI were 30% less likely to be admitted to the ICU on high-census days, whereas there was no difference in ICU admission for patients with mild or severe TBI. Delirium was significantly higher in patients admitted on high-census days compared to those on low-census days. This was further identified to be predominantly driven by patients with mild TBI admitted on high-census days. While most outcomes remained consistent, significant rates of delirium were found in our mild TBI patients admitted on high-census days suggesting the need for additional factors in the evaluation of these patients on admission. This study also reveals potential under-triage in moderate TBI patientson high-census days as they had significantly lower rates of ICU admission. These findings emphasize the need for further investigations to optimize patient care strategies within the context of fluctuating healthcare system demands.

Effects of mindfulness-based cognitive therapy for Chinese adults with PTSD symptoms: protocol for a randomised controlled trial

IntroductionEmerging evidence supports mindfulness as a potential psychotherapy for post-traumatic stress disorder (PTSD). Individuals with subthreshold PTSD experience significant impairment in their daily life and functioning due to PTSD symptoms, despite not meeting the full diagnostic criteria for PTSD in DSM-5. Mindfulness skills, including non-judgmental acceptance, attentional control and openness to experiences may help alleviate PTSD symptoms by targeting characteristics such as intensified memory processing, dysregulated hyperarousal, avoidance, and thought suppression. This trial aims to test the effects of mindfulness-based cognitive therapy (MBCT) when compared to an active control.Method and analysisThis 1:1 randomised controlled trial will enroll 160 participants with PTSD symptoms in 2 arms (MBCT vs. Seeking Safety), with both interventions consisting of 8 weekly sessions lasting 2 h each week and led by certified instructors. Assessments will be conducted at baseline (T0), post-intervention (T1), and 3 months post-intervention (T2), with the primary outcome being PTSD symptoms measured by the PTSD checklist for DSM-5 (PCL-5) at T1. Secondary outcomes include depression, anxiety, attention, experimental avoidance, rumination, mindfulness, and coping skills. Both intention-to-treat and per-protocol analyses will be performed. Mediation analysis will investigate whether attention, experimental avoidance, and rumination mediate the effect of mindfulness on PTSD symptoms.DiscussionThe proposed study will assess the effectiveness of MBCT in improving PTSD symptoms. The findings are anticipated to have implications for various areas of healthcare and contribute to the enhancement of existing intervention guidelines for PTSD.Trial registration numberChiCTR2200061863.

The impact of artificial intelligence on Ukrainian medicine: benefits and challenges for the future

The worldwide trend of digitisation across all aspects of society necessitates a fresh approach to the management system in the healthcare sector of Ukraine. The use of artificial intelligence (AI) tools in medicine opens up opportunities for transforming the healthcare sector by creating an effective and personalised approach to medical services. Aims: The purpose of the study was a comprehensive analysis of the current role of artificial intelligence in Ukrainian medicine and the projected future impact of its development. Methodology: The work was completed by utilising several common scientific methods of cognition: logical and structural analysis, abstraction, comparison, induction, and deduction, as well as concretisation and formalisation. Results The benefits resulting from the active implementation of artificial intelligence technologies in medicine were analysed and evaluated in the article. The risks that will accompany the further rooting of AI in medical information systems were identified and a number of appropriate preventive measures were proposed. The experience of developed countries in the transformation of medicine through the use of artificial intelligence capabilities was studied. The feasibility and prospects of using AI technologies in various areas of healthcare were analysed. A number of tools and technologies were proposed to ensure an adequate level of security and protection of personal information. Scientific Novelty: The principles for adapting the existing algorithms of the medical industry in Ukraine to meet the requirements of digitalisation have been formulated, the expediency of transformation in this area was substantiated. Conclusion: It was substantiated that the increasing utilisation of artificial intelligence enables the ability to produce precise and efficient decisions within intricate analytical procedures. The results of the study may be of practical value for the process of optimising the modern medical industry in Ukraine in the context of globalisation of the introduction of artificial intelligence technologies.

An integrated deep learning and natural language processing approach for continuous remote monitoring in digital health

The rapid adoption of digital health technologies generates a vast amount of health data, presenting an opportunity to leverage advanced data analytics and cognitive computing to extract insights and knowledge from this data and improve healthcare outcomes. This research presents an integrated deep learning (DL) and natural language processing (NLP) approach for continuous remote monitoring in digital health. The approach involves using DL algorithms to analyze data collected from wearable devices and remote monitoring tools, while NLP is used to analyze patient feedback and electronic medical records. Various DL algorithms, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are suggested to analyze medical images and time-series data, and unsupervised learning techniques, such as autoencoders, are recommended for feature extraction. We provide a comparison between our proposed approach and previous related works. We also identify and discuss the limitations of existing works and explain how our approach overcomes these limitations. The study also delivers a review of the prevailing work of data analytics and cognitive computing in digital health, examining the applications of these technologies in various areas of healthcare, identifying the challenges and opportunities of implementing them in healthcare, and proposing research directions that can help accelerate their adoption. The proposed approach can enable health professionals to remotely monitor patients’ health conditions in real-time and predict the likelihood of adverse health events, leading to improved patient outcomes, reduced healthcare costs, and enhanced quality of care. This paper provides insights into current trends, challenges, and research directions in digital health, informing future research and development.

Adoption of Artificial Intelligence in Health Care: A Nursing Perspective

Artificial intelligence (AI) is revolutionizing various areas of health care, particularly the medical and nursing field, called "Adoption of Artificial Intelligence in Health Care: A Nursing Perspective." This article examines the state of artificial intelligence (AI) in healthcare research, its use, its advantages for healthcare, and any challenges that can arise when adopting AI to healthcare organizations. The benefits of AI, such as increased efficiency, cost savings, future direction, improved decision-making, and enhanced patient care experiences. It is the need of the hour to address the challenges to ensure the successful adoption of AI in healthcare operations and treatment modalities including Nursing. The possible difficulties with applying AI, include issues with data protection, the requirement for additional knowledge and training, and moral issues. The Impact of Artificial Intelligence on Healthcare offers organizations looking to use this technology to achieve their cost-effective strategic objectives for improved patient care useful insights into how AI is transforming the medical and nursing fields and interventions

Application of Virtual Reality Technology and Its Impact On Digital Health In Healthcare Industry.

The application of virtual reality (VR) technology in the healthcare industry has gained momentum in recent years and has the potential to revolutionize the way healthcare is delivered. VR technology offers an immersive experience that allows medical professionals and patients to visualize and interact with medical data more intuitively and naturally, which can improve diagnosis, treatment, and overall patient outcomes. Digital health is an emerging field that combines technology and healthcare to improve patient outcomes, reduce costs, and enhance the overall delivery of care. The application of VR technology in digital health is rapidly expanding and can be used in various areas of healthcare, including patient education, medical training, and simulation. The impact of VR on digital health can be seen in a variety of ways, such as improved patient engagement, better patient outcomes, and more efficient and effective medical training. VR can be used to create interactive and engaging educational experiences for patients, which can help them better understand their medical conditions and treatments. Additionally, VR technology can provide medical professionals with hands-on training and the ability to practice complex procedures in a safe and controlled environment. Moreover, the use of VR in telemedicine can enhance the patient experience by providing a more immersive and engaging interaction with healthcare professionals. This can help to improve the overall delivery of care and can lead to better health outcomes. Overall, the application of virtual reality technology in digital health represents a promising opportunity for medical professionals to transform the way healthcare is delivered. By leveraging the power of VR, medical professionals can improve patient outcomes, enhance training and decision-making, and ultimately enhance the quality of care provided to patients.

Virtual reality technology in physician education: focus on emergency medical care

Virtual reality technology has been developing rapidly over the past two decades and is becoming available in various areas of healthcare — in surgery, neurology, rehabilitation, and education. The need for virtual reality technology is also manifested in the development of algorithms for emergency medical care by primary care physicians. Currently, 6 interactive case problems have been developed for the most common emergency conditions, requiring, among other things, resuscitation. The concept of teaching using virtual reality technology has become the basis for the development of an additional professional advanced training program for general practitioners "Development of an algorithm for providing emergency medical care for sudden acute diseases and conditions". A unique feature of the program is the ability to practice the emergency medical care algorithm using virtual reality technology, as well as practice skills using a simulation manikin and an automatic external defibrillator.

201 Racial and Ethnic Differences in the Initiation of Low Tidal Volume Ventilation in the Emergency Department

Low tidal volume ventilation (LTVV) is a key component of lung protective ventilation and is defined by tidal volumes of 6-8ml/kg of ideal body weight (IBW). Initiation of LTVV in the emergency department (ED) has been shown to improve outcomes including hospital length of stay and mortality. While many studies have identified racial and ethnic disparities in various areas of health care, none have done so with respect to LTVV in the ED. This study aims to describe the variation in the application of LTVV in the ED across race and ethnicity.

Development of a national strategy with recommendations for the care of seriously ill and dying people and their relatives in pandemics: A modified Delphi study

Background: The SARS-CoV-2 pandemic is a constant challenge for health care systems, also in Germany. Care of seriously ill and dying people and their relatives is often neglected and suffering increased due to sub-optimal symptom management, visiting restrictions and lonely dying. The project “Palliative Care in Pandemics (PallPan)” intended to develop a national strategy including evidence- and consensus-based recommendations for the care of seriously ill and dying people and their relatives during pandemic times in Germany. Aim: To reach consensus on evidence-based recommendations for the care of seriously ill and dying people and their relatives in pandemics. Methods: Three-step consensus process comprising two online Delphi rounds and an expert workshop conducted from April to June 2021. One hundred twenty experts from various areas of healthcare, administration, and politics in Germany were included. Results: During the consensus-process, pre-formulated evidence-based recommendations were refined step-by-step. This resulted in consensus on 33 recommendations on the topics of “supporting patients and their relatives,” “supporting staff,” and “supporting and maintaining structures and provision of palliative care.” The recommendations address professional carers and various responsibilities on a governmental, federal state and municipal level, and in healthcare facilities. Conclusion: We provide evidence and consensus-based recommendations for the care of seriously ill and dying people and their relatives in pandemics in Germany. This is an important step towards a pandemic preparedness and hopefully improves the future palliative care response to pandemics.

Evidence implementation across Europe: the challenge of collaboration during the pandemic.

Evidence implementation across Europe: the challenge of collaboration during the pandemic.

Research progress of artificial intelligence-based imaging diagnosis of infectious diseases

With the rapid development and progress of theory and technology, artificial intelligence (AI) has overcome many early technical limitations. Remarkable advances have recently been made in the application of AI to various areas of health care, and improvements in the performance of computer-aided diagnostics, such as accuracy, specificity, and processing speed, have been achieved, especially in the classification and identification of lesions. We review the various applications and worldwide progress of AI-based imaging diagnosis of infectious diseases.

Mechanical behaviour of flexible 3D printed gyroid structures as a tuneable replacement for soft padding foam

Various areas of healthcare utilise custom foam cushioning to treat or mitigate conditions like pressure ulcers, or to provide personalised support structures for patients with specific clinical needs. Polyurethane foams are often used; however, such materials require significant time and expertise to combine different foam types into a device that provides sufficient structural support in some areas, with soft pressure distribution in others. In this paper, flexible 3D printed gyroid based metamaterials are investigated as a tuneable replacement for polyurethane foams. The impact of changing key gyroid structural characteristics on the material’s mechanical response is examined. Samples with six different unit cell geometries for each of two flexible TPU 3D printing filaments (NinjaFlex and Flexion X60) were produced using fused filament fabrication, tested, and compared to three types of conventional polyurethane rehabilitation foam. Compression tests were conducted focussing on compressive stress-strain response, strain rate effect, print layer effect, and cyclic fatigue behaviour. In all tests it was observed that gyroid samples of both filament types were able to produce compressive responses comparable to the foams. Solid volume fraction was determined as the critical gyroid geometric parameter that influenced compressive response, and solid volume fractions capable of reproducing the specific response of each of the three rehab foams were determined. It is shown that 3D printed gyroid materials are a viable replacement for soft polyurethane foams, and the direct control of material response possible with simple geometric changes means such metamaterials may lead to improved optimisation of rehabilitation cushions.

Interprofessional Collaboration Competencies in the Health System: A Systematic Review.

Today's health systems require the collaboration of diverse staff such as physicians, nurses, social workers, and other healthcare professionals. In addition to professional competencies, they also need to acquire interprofessional competencies. Effective interprofessional collaboration among healthcare professionals is one of the solutions that can promote the effectiveness of the health system using existing resources. A systematic review was conducted in 2021 according to the PRISMA and through searching Web of Science, Scopus, PubMed, ProQuest, Science Direct, Emerald, Springer Link, Google Scholar, SID, and Magiran databases. The official websites of WHO, United Nations, and World Bank were also searched. The time frame for the research was from 2010 to 2020, and included both the English and Persian languages. Out of 7267 initially retrieved articles, 17 articles finally met the quality evaluation criteria and were analyzed through qualitative content analysis. Then their full texts were retrieved and analyzed in MAXQDA software, and final results were categorized. Competencies have been explored in various areas of health care, especially in the clinical field. The competencies introduced were extracted and categorized into six domains of "patient-centered care," "interprofessional communication," "participatory leadership," "conflict resolution," "transparency of duties and responsibilities," and "teamwork." The competence of "transparency of duties and responsibilities" was mentioned in all studies and is required for any collaboration. Interprofessional competencies provide quality, safety, and patient-centeredness through effective collaboration. Integrating interprofessional competencies into the educational curriculum, in-service training, and continue education is essential to form effective interprofessional collaboration.

Computer Vision with Machine Learning Enabled Skin Lesion Classification Model

Recently, computer vision (CV) based disease diagnosis models have been utilized in various areas of healthcare. At the same time, deep learning (DL) and machine learning (ML) models play a vital role in the healthcare sector for the effectual recognition of diseases using medical imaging tools. This study develops a novel computer vision with optimal machine learning enabled skin lesion detection and classification (CVOML-SLDC) model. The goal of the CVOML-SLDC model is to determine the appropriate class labels for the test dermoscopic images. Primarily, the CVOML-SLDC model derives a gaussian filtering (GF) approach to pre-process the input images and graph cut segmentation is applied. Besides, firefly algorithm (FFA) with EfficientNet based feature extraction module is applied for effectual derivation of feature vectors. Moreover, naïve bayes (NB) classifier is utilized for the skin lesion detection and classification model. The application of FFA helps to effectually adjust the hyperparameter values of the EfficientNet model. The experimental analysis of the CVOML-SLDC model is performed using benchmark skin lesion dataset. The detailed comparative study of the CVOML-SLDC model reported the improved outcomes over the recent approaches with maximum accuracy of 94.83%.

Racial Disparity Between Whites and African Americans in Incidence and Outcome of Pancreatic Cancer: Have We Made a Difference?

Racial Disparity Between Whites and African Americans in Incidence and Outcome of Pancreatic Cancer: Have We Made a Difference?

CORR® International - Asia-Pacific: Wise Move-Implementing a 30-minute "Wisdom Session" for Patients after TKA.

CORR® International - Asia-Pacific: Wise Move-Implementing a 30-minute "Wisdom Session" for Patients after TKA.

Інтернет технології у закладах охорони здоров’я

Abstract. Introduction. With the accelerated scientific and technological progress, the further introduction of Internet information technologies in various areas of health care is becoming increasingly important. Recently, we have gained positive experience in the use of information technology in the management of health care facilities, computer diagnostics (including telemedicine diagnostics), in medical education and science. The current stage of development of society is formed under the strong influence of Internet technologies, which have come to all spheres of human activity, see the spread of information flows for society, forming a global information space. It is difficult to find an area in which Internet technologies are not currently used. Today's Internet information technology is increasingly used in the field of health care, which is convenient, sometimes just necessary. In a large number of medical studies, it is simply not possible to work without the use of special software. This process is accompanied by significant changes in the theory and practice of the medical field. Purpose. The purpose of the article is to scientifically substantiate the use of Internet information technologies in health care facilities, which increases the effectiveness of management decisions. Results. The article considers the concept of information Internet technologies in health care facilities. Along with the ability to use digital equipment in healthcare facilities, Ukraine is developing an electronic system of interaction between doctors, patients and medical institutions eHealth. Problems related to the introduction of medical information Internet technologies in health care facilities as a basis for modern information support and management decisions in health care and identify possible ways to solve them. Conclusions. The availability of available information will allow to regulate issues related to the implementation of coordination policies of various departments in the field of health, significantly improve doctors' awareness of the latest effective Internet information technologies in medical institutions, significantly affect the speed and quality of health data. of the patient, methods of treatment, to provide medical measures of preventive and educational character, and also to influence administrative decisions of the management.

The diagnostic and prognostic value of artificial intelligence and artificial neural networks in spinal surgery : a narrative review.

In recent years, machine learning (ML) and artificial neural networks (ANNs), a particular subset of ML, have been adopted by various areas of healthcare. A number of diagnostic and prognostic algorithms have been designed and implemented across a range of orthopaedic sub-specialties to date, with many positive results. However, the methodology of many of these studies is flawed, and few compare the use of ML with the current approach in clinical practice. Spinal surgery has advanced rapidly over the past three decades, particularly in the areas of implant technology, advanced surgical techniques, biologics, and enhanced recovery protocols. It is therefore regarded an innovative field. Inevitably, spinal surgeons will wish to incorporate ML into their practice should models prove effective in diagnostic or prognostic terms. The purpose of this article is to review published studies that describe the application of neural networks to spinal surgery and which actively compare ANN models to contemporary clinical standards allowing evaluation of their efficacy, accuracy, and relatability. It also explores some of the limitations of the technology, which act to constrain the widespread adoption of neural networks for diagnostic and prognostic use in spinal care. Finally, it describes the necessary considerations should institutions wish to incorporate ANNs into their practices. In doing so, the aim of this review is to provide a practical approach for spinal surgeons to understand the relevant aspects of neural networks. Cite this article: Bone Joint J2021;103-B(9):1442-1448.