Hasil untuk "Medical legislation"

Elliott Steen Windfeld, M. Brooks

Carlos Enrique Castro Osorio

Objetivo: validar el uso de instrumentos comerciales manuales para la detección de metales, en el proceso de búsqueda y recuperación de proyectiles y elementos metálicos durante la necropsia, a fin de ofrecer alternativas de detección inicial, fáciles y económicas, y fortalecer el proceso de búsqueda y recuperación de proyectiles y elementos metálicos en tejidos biológicos, en un modelo de cadáver humano. Introducción: recuperar evidencias es una actividad crítica durante las necropsias medicolegales; especialmente en las muertes violentas por proyectiles de armas de fuego, en las que los proyectiles y /o elementos metálicos, pueden migrar por cavidades corporales o quedar en vísceras huecas, grandes vasos, prendas, embalaje, entre otros, lo que dificulta su recuperación. Metodología: se utilizó un dispositivo manual de detección de metales para ubicar proyectiles y elementos metálicos en un cadáver; su embalaje, así como en las prendas de ropa. Se cálculo de la sensibilidad y especificidad, valor predictivo positivo y negativo general y especifico por ubicación anatómica. Resultados: se obtuvo una sensibilidad general del 83.33 % y especificidad del 100 %, Valor predictivo positivo =1, tasa de falsos positivos = 0,00; Valor Predictivo Negativo = 0,60; tasa de falsos negativos = 0,17. Para los objetos más pequeños, iguales o menores a 5 mm de diámetro especificidad = 100% y sensibilidad = 62,5%. Conclusión: Se encontró que estos dispositivos pueden ser herramientas útiles para la detección de proyectiles metálicos en necropsias, especialmente para objetos metálicos de 7 mm o más. Su aplicación en las áreas rurales o en espacios abiertos donde disponer de un equipo de rayos X no siempre es posible es factible. Para los objetos más pequeños, iguales o menores a 5 mm de diámetro, aunque la especificidad es del 100%, la sensibilidad del 62,5% en algunas áreas anatómicas, sugiere que se requiere más investigación para trabajar en el mejoramiento de la detección de estos objetos presentes en el cadáver.

S. Ye. Titor

The issue of staff shortage in socially important sectors of Russian economy, including health care, is not new. The state adopts various mechanisms and implements projects aimed at its solution, but the efforts are insufficient. The statistics of the need for personnel in the health care sector has been analyzed as a justification for the relevance of the study. The experience of the Soviet distribution of graduates, foreign experience, and new draft laws have been studied, and proposals for updating the “old” experience considering the new realities have been formulated. The updated mechanism of the medical graduates state distribution has been proposed. Theoretical conclusions about the necessity of introducing the socially important specialties concept into the law enforcement turnover have been formulated, and the author’s definition has been given. The comparative analysis of the existing mechanisms of providing the labor market with qualified specialists (target training) with the proposed order of distribution has been carried out, and the advantages of the latter and the disadvantages of the existing one have been shown. Proposals for updating the current legislation necessary for state distribution implementation in socially important areas of training, such as health care, have been formulated. The proposed system will allow solving another important problem such as youth employment.

Xu Wang, Xiaoxi Zhang, Jiangjiang He

On December 31, 2019, the Wuhan Municipal Health Commission announced an outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), China is now at a critical period in the control of the epidemic. The Chinese Government has been taking a series of rapid, comprehensive, and effective prevention and control measures. As the pandemic has developed, a fact has become apparent: there is a serious dearth of emergency medical supplies, and especially an extreme shortage of personal protective equipment such as masks and medical protective clothing. This is one of the major factors affecting the progress of epidemic prevention and control. Although China has made great efforts to strengthen the ability to quickly respond to public health emergencies since the SARS outbreak in 2003 and it has clarified requirements for emergency supplies through legislation, the emergency reserve supplies program has not been effectively implemented, and there are also deficiencies in the types, quantity, and availability of emergency medical supplies. A sound system of emergency reserve supplies is crucial to the management of public health emergencies. Based on international experiences with pandemic control, the world should emphasize improving the system of emergency reserve medical supplies in the process of establishing and improving public health emergency response systems, and it should promote the establishment of international cooperative programs to jointly deal with public health emergencies of international concern in the future.

Yi Zhang, Yidong Zhao, Hui Xue et al.

Image registration is essential for medical image applications where alignment of voxels across multiple images is needed for qualitative or quantitative analysis. With recent advancements in deep neural networks and parallel computing, deep learning-based medical image registration methods become competitive with their flexible modelling and fast inference capabilities. However, compared to traditional optimization-based registration methods, the speed advantage may come at the cost of registration performance at inference time. Besides, deep neural networks ideally demand large training datasets while optimization-based methods are training-free. To improve registration accuracy and data efficiency, we propose a novel image registration method, termed Recurrent Inference Image Registration (RIIR) network. RIIR is formulated as a meta-learning solver to the registration problem in an iterative manner. RIIR addresses the accuracy and data efficiency issues, by learning the update rule of optimization, with implicit regularization combined with explicit gradient input. We evaluated RIIR extensively on brain MRI and quantitative cardiac MRI datasets, in terms of both registration accuracy and training data efficiency. Our experiments showed that RIIR outperformed a range of deep learning-based methods, even with only $5\%$ of the training data, demonstrating high data efficiency. Key findings from our ablation studies highlighted the important added value of the hidden states introduced in the recurrent inference framework for meta-learning. Our proposed RIIR offers a highly data-efficient framework for deep learning-based medical image registration.

Tianwei Zhang, Dong Wei, Mengmeng Zhu et al.

Self-supervised learning has emerged as a powerful tool for pretraining deep networks on unlabeled data, prior to transfer learning of target tasks with limited annotation. The relevance between the pretraining pretext and target tasks is crucial to the success of transfer learning. Various pretext tasks have been proposed to utilize properties of medical image data (e.g., three dimensionality), which are more relevant to medical image analysis than generic ones for natural images. However, previous work rarely paid attention to data with anatomy-oriented imaging planes, e.g., standard cardiac magnetic resonance imaging views. As these imaging planes are defined according to the anatomy of the imaged organ, pretext tasks effectively exploiting this information can pretrain the networks to gain knowledge on the organ of interest. In this work, we propose two complementary pretext tasks for this group of medical image data based on the spatial relationship of the imaging planes. The first is to learn the relative orientation between the imaging planes and implemented as regressing their intersecting lines. The second exploits parallel imaging planes to regress their relative slice locations within a stack. Both pretext tasks are conceptually straightforward and easy to implement, and can be combined in multitask learning for better representation learning. Thorough experiments on two anatomical structures (heart and knee) and representative target tasks (semantic segmentation and classification) demonstrate that the proposed pretext tasks are effective in pretraining deep networks for remarkably boosted performance on the target tasks, and superior to other recent approaches.

Yinchi Zhou, Tianqi Chen, Jun Hou et al.

Image-to-image translation is a vital component in medical imaging processing, with many uses in a wide range of imaging modalities and clinical scenarios. Previous methods include Generative Adversarial Networks (GANs) and Diffusion Models (DMs), which offer realism but suffer from instability and lack uncertainty estimation. Even though both GAN and DM methods have individually exhibited their capability in medical image translation tasks, the potential of combining a GAN and DM to further improve translation performance and to enable uncertainty estimation remains largely unexplored. In this work, we address these challenges by proposing a Cascade Multi-path Shortcut Diffusion Model (CMDM) for high-quality medical image translation and uncertainty estimation. To reduce the required number of iterations and ensure robust performance, our method first obtains a conditional GAN-generated prior image that will be used for the efficient reverse translation with a DM in the subsequent step. Additionally, a multi-path shortcut diffusion strategy is employed to refine translation results and estimate uncertainty. A cascaded pipeline further enhances translation quality, incorporating residual averaging between cascades. We collected three different medical image datasets with two sub-tasks for each dataset to test the generalizability of our approach. Our experimental results found that CMDM can produce high-quality translations comparable to state-of-the-art methods while providing reasonable uncertainty estimations that correlate well with the translation error.

Müjgan Solak, Nebahat Kayaer

Abstract Background It is noteworthy that there is an increase in medical lawsuits filed against nurses in Turkey and in the rest of the world. The purpose of this article is to examine nurses’ criminal liability and senior nursing students’ awareness of these responsibilities. Methods All senior students (n = 309) of the Faculty of Nursing who were studying in the 2020–2021 academic year of the university constituted the population of the research. The study was completed with 300 students who were studying between the dates of the research and who agreed to participate in the research. Data was collected online via Google Forms. The data obtained from the research was analyzed with the program SPSS 20. Numbers and percentages, averages, and chi-square tests were used to evaluate the data. Results This article provides an overview of nurses’ criminal liability and senior nursing students’ awareness of these responsibilities. The article offers striking implications regarding the awareness of trainee nurses who have not yet started their careers regarding the criminal liability of nurses. Conclusion The result of our research clearly revealed that the majority of senior nursing students have insufficient knowledge about professional criminal responsibilities, that they are not aware of their duties, powers, and criminal responsibilities as defined in legislation, and that they do not know the types of criminal responsibilities.

A. Badnjević, L. G. Pokvic, Amar Deumić et al.

BACKGROUND: Medical devices (MDs) represent the backbone of the modern healthcare system. Considering their importance in daily medical practice, the process of manufacturing, marketing and usage has to be regulated at all levels. Harmonized evidence-based conformity assessment of MDs during PMS relying on traceability of medical device measurements can contribute to higher reliability of MD performance and consequently to higher reliability of diagnosis and treatments. OBJECTIVE: This paper discusses issues within MD post-market surveillance (PMS) mechanisms in order to set a path to harmonization of MD PMS. METHODS: Medline (1980–2021), EBSCO (1991–2021), and PubMed (1980–2021) as well as national and international legislation and standard databases along with reference lists of eligible articles and guidelines of relevant regulatory authorities such as the European Commission and the Food and Drug Administration were searched for relevant information. Journal articles that contain information regarding PMS methodologies concerning stand-alone medical devices and relevant national and international legislation, standards and guidelines concerning the topic were included in the review. RESULTS: The search strategy resulted in 2282 papers. Out of those only 24 articles satisfied the eligibility criteria and were finally included in the review. Papers were grouped per categories: medical device registry, medical device adverse event reporting, and medical device performance evaluation. In addition to journal articles, national and international legislation, standards, and guidelines were reviewed to assess the state of PMS in different regions of the world. CONCLUSION: Although the regulatory framework prescribes PMS of medical devices, the process itself is not harmonized with international standards. Particularly, conformity assessment of MDs, as an important part of PMS, is not measured and managed in a traceable, evidence-based manner. The lack of harmonization within PMS results in an environment of increased adverse events involving MDs and overall mistrust in medical device diagnosis and treatment results.

Ante B. V. Pettersson, R. Ballardini, M. Mimler et al.

Introduction: 3D printing has quickly found many applications in medicine. However, as with any new technology the regulatory landscape is struggling to stay abreast. Unclear legislation or lack of legislation has been suggested as being one hindrance for wide-scale adoption. Methods: A scoping review was performed in PubMed, Web of Science, SCOPUS and Westlaw International to identify articles dealing with legal issues in medical 3D printing. Results: Thirty-four articles fulfilling inclusion criteria were identified in medical/technical databases and fifteen in the legal database. The majority of articles dealt with the USA, while the EU was also prominently represented. Some common unresolved legal issues were identified, among them terminological confusion between custom-made and patient-matched devices, lack of specific legislation for patient-matched products, and the undefined legal role of CAD files both from a liability and from an intellectual property standpoint. Data protection was mentioned only in two papers and seems an underexplored topic. Conclusion: In this scoping review, several relevant articles and several common unresolved legal issues were identified including a need for terminological uniformity in medical 3D printing. The results of this work are planned to inform our own deeper legal analysis of these issues in the future.

Karl Stöger, David Schneeberger, Andreas Holzinger

Although the European Commission proposed new legislation for the use of "high-risk artificial intelligence" earlier this year, the existing European fundamental rights framework already provides some clear guidance on the use of medical AI.

Angona Biswas, MD Abdullah Al Nasim, Md Shahin Ali et al.

The development of medical science greatly depends on the increased utilization of machine learning algorithms. By incorporating machine learning, the medical imaging field can significantly improve in terms of the speed and accuracy of the diagnostic process. Computed tomography (CT), magnetic resonance imaging (MRI), X-ray imaging, ultrasound imaging, and positron emission tomography (PET) are the most commonly used types of imaging data in the diagnosis process, and machine learning can aid in detecting diseases at an early stage. However, training machine learning models with limited annotated medical image data poses a challenge. The majority of medical image datasets have limited data, which can impede the pattern-learning process of machine-learning algorithms. Additionally, the lack of labeled data is another critical issue for machine learning. In this context, active learning techniques can be employed to address the challenge of limited annotated medical image data. Active learning involves iteratively selecting the most informative samples from a large pool of unlabeled data for annotation by experts. By actively selecting the most relevant and informative samples, active learning reduces the reliance on large amounts of labeled data and maximizes the model's learning capacity with minimal human labeling effort. By incorporating active learning into the training process, medical imaging machine learning models can make more efficient use of the available labeled data, improving their accuracy and performance. This approach allows medical professionals to focus their efforts on annotating the most critical cases, while the machine learning model actively learns from these annotated samples to improve its diagnostic capabilities.

Sergio Tascon-Morales, Pablo Márquez-Neila, Raphael Sznitman

Visual Question Answering (VQA) models aim to answer natural language questions about given images. Due to its ability to ask questions that differ from those used when training the model, medical VQA has received substantial attention in recent years. However, existing medical VQA models typically focus on answering questions that refer to an entire image rather than where the relevant content may be located in the image. Consequently, VQA models are limited in their interpretability power and the possibility to probe the model about specific image regions. This paper proposes a novel approach for medical VQA that addresses this limitation by developing a model that can answer questions about image regions while considering the context necessary to answer the questions. Our experimental results demonstrate the effectiveness of our proposed model, outperforming existing methods on three datasets. Our code and data are available at https://github.com/sergiotasconmorales/locvqa.

Mélanie Gaillochet, Christian Desrosiers, Hervé Lombaert

The performance of learning-based algorithms improves with the amount of labelled data used for training. Yet, manually annotating data is particularly difficult for medical image segmentation tasks because of the limited expert availability and intensive manual effort required. To reduce manual labelling, active learning (AL) targets the most informative samples from the unlabelled set to annotate and add to the labelled training set. On the one hand, most active learning works have focused on the classification or limited segmentation of natural images, despite active learning being highly desirable in the difficult task of medical image segmentation. On the other hand, uncertainty-based AL approaches notoriously offer sub-optimal batch-query strategies, while diversity-based methods tend to be computationally expensive. Over and above methodological hurdles, random sampling has proven an extremely difficult baseline to outperform when varying learning and sampling conditions. This work aims to take advantage of the diversity and speed offered by random sampling to improve the selection of uncertainty-based AL methods for segmenting medical images. More specifically, we propose to compute uncertainty at the level of batches instead of samples through an original use of stochastic batches (SB) during sampling in AL. Stochastic batch querying is a simple and effective add-on that can be used on top of any uncertainty-based metric. Extensive experiments on two medical image segmentation datasets show that our strategy consistently improves conventional uncertainty-based sampling methods. Our method can hence act as a strong baseline for medical image segmentation. The code is available on: https://github.com/Minimel/StochasticBatchAL.git.

Md. Zihad Bin Jahangir, Ruksat Hossain, Riadul Islam et al.

Medical imaging informatics is a rapidly growing field that combines the principles of medical imaging and informatics to improve the acquisition, management, and interpretation of medical images. This chapter introduces the basic concepts of medical imaging informatics, including image processing, feature engineering, and machine learning. It also discusses the recent advancements in computer vision and deep learning technologies and how they are used to develop new quantitative image markers and prediction models for disease detection, diagnosis, and prognosis prediction. By covering the basic knowledge of medical imaging informatics, this chapter provides a foundation for understanding the role of informatics in medicine and its potential impact on patient care.

Yutong Xie, Bing Yang, Qingbiao Guan et al.

Medical image segmentation plays an important role in computer-aided diagnosis. Attention mechanisms that distinguish important parts from irrelevant parts have been widely used in medical image segmentation tasks. This paper systematically reviews the basic principles of attention mechanisms and their applications in medical image segmentation. First, we review the basic concepts of attention mechanism and formulation. Second, we surveyed over 300 articles related to medical image segmentation, and divided them into two groups based on their attention mechanisms, non-Transformer attention and Transformer attention. In each group, we deeply analyze the attention mechanisms from three aspects based on the current literature work, i.e., the principle of the mechanism (what to use), implementation methods (how to use), and application tasks (where to use). We also thoroughly analyzed the advantages and limitations of their applications to different tasks. Finally, we summarize the current state of research and shortcomings in the field, and discuss the potential challenges in the future, including task specificity, robustness, standard evaluation, etc. We hope that this review can showcase the overall research context of traditional and Transformer attention methods, provide a clear reference for subsequent research, and inspire more advanced attention research, not only in medical image segmentation, but also in other image analysis scenarios.

Karl Swanson, Akshay Ravi, Sameh Saleh et al.

BackgroundThe Supreme Court ruling in Dobbs v Jackson Women’s Health Organization (Dobbs) overrules precedents established by Roe v Wade and Planned Parenthood v Casey and allows states to individually regulate access to abortion care services. While many states have passed laws to protect access to abortion services since the ruling, the ruling has also triggered the enforcement of existing laws and the creation of new ones that ban or restrict abortion. In addition to denying patients the full spectrum of reproductive health care, one major concern in the medical community is how the ruling will undermine trust in the patient-clinician relationship by influencing perceptions of the privacy of patient health information. ObjectiveThis study aimed to study the effect of recent abortion legislation on Twitter user engagement, sentiment, expressions of trust in clinicians, and privacy of health information. MethodsWe scraped tweets containing keywords of interest between January 1, 2020, and October 17, 2022, to capture tweets posted before and after the leak of the Supreme Court decision. We then trained a Latent Dirichlet Allocation model to select tweets pertinent to the topic of interest and performed a sentiment analysis using Robustly Optimized Bidirectional Encoder Representations from Transformers Pre-training Approach model and a causal impact time series analysis to examine engagement and sentiment. In addition, we used a Word2Vec model to study the terms of interest against a latent trust dimension to capture how expressions of trust for our terms of interest changed over time and used term frequency, inverse-document frequency to measure the volume of tweets before and after the decision with respect to the negative and positive sentiments that map to our terms of interest. ResultsOur study revealed (1) a transient increase in the number of daily users by 576.86% (95% CI 545.34%-607.92%; P<.001), tweeting about abortion, health care, and privacy of health information postdecision leak; (2) a sustained and statistically significant decrease in the average daily sentiment on these topics by 19.81% (95% CI −22.98% to −16.59%; P=.001) postdecision leak; (3) a decrease in the association of the latent dimension of trust across most clinician-related and health information–related terms of interest; (4) an increased frequency of tweets with these clinician-related and health information–related terms and concomitant negative sentiment in the postdecision leak period. ConclusionsThe study suggests that the Dobbs ruling has consequences for health systems and reproductive health care that extend beyond denying patients access to the full spectrum of reproductive health services. The finding of a decrease in the expression of trust in clinicians and health information–related terms provides evidence to support advocacy and initiatives that proactively address concerns of trust in health systems and services.

N. Lintzeris, Llewellyn Mills, Anastasia S. Suraev et al.

Background In 2016, the Australian federal government passed legislation enabling a range of cannabis-based products to be prescribed to patients by registered healthcare professionals. An online survey conducted immediately prior to these legislative changes found that the vast majority of respondents at the time were illicitly sourcing cannabis plant matter, smoking was the preferred route of administration and mental health, chronic pain, and sleep conditions were the most frequently cited reasons for medical cannabis use. This manuscript reports the results of a follow-up survey conducted in 2018–2019, the Cannabis As Medicine Survey (CAMS-18). The goal of this second questionnaire was to examine patterns of use and consumer perspectives regarding medical cannabis use in Australia, 2 years after the introduction of legal access pathways. Methods Anonymous online cross-sectional survey with convenience sample, recruited mainly through online media between September 2018 and March 2019. Participants were adults (18 years or over) residing in Australia who reported using a cannabis product for self-identified therapeutic reasons during the preceding 12 months. The survey measured consumer characteristics, indications and patterns of medical cannabis use, routes and frequency of administration, perceived benefits and harms, experiences and preferred models of access to medical cannabis. Results Data were available for 1388 respondents. The main categories of condition being treated with medical cannabis were pain (36.4%), mental health (32.8%), sleep (9.2%), neurological (5.2%) and cancer (3.8%). Respondents reported using medical cannabis on 15.8 (11.2) days in the past 28, by inhaled (71.4%) or oral (26.5%) routes and spending AUD$82.27 ($101.27) per week. There were high levels of self-reported effectiveness, but also high rates of side effects. There was uncertainty regarding the composition of illicit cannabinoid products and concerns regarding their possible contamination. Few respondents (2.7%) had accessed legally prescribed medical cannabis, with the main perceived barriers being cost, disinterest from the medical profession and stigma regarding cannabis use. Conclusions Chronic pain, mental health and sleep remain the main clinical conditions for which consumers report using medical cannabis. Despite 2 years of legal availability, most consumers in Australia reported accessing illicit cannabis products, with uncertainty regarding the quality or composition of cannabis products.

S. Voidăzan, S. Albu, R. Tóth et al.

Background: Hospital-acquired infections (HAI) contribute to the emotional stress and functional disorders of the patient and in some cases, can lead to a state of disability that reduces quality of life. Often, HAI are one of the factors that lead to death. The purpose of this study was to analyze the cases of HAI identified in public hospitals at the county level, through case report sheets, as they are reported according to the Romanian legislation. Methods: We performed a cross sectional study design based on the case law of the data reported to the Mures Public Health Directorate, by all the public hospitals belonging to this county. We tracked hospital-acquired infections reported for 2017–2018, respectively, a number of 1024 cases, which implies a prevalence rate of 0.44%, 1024/228,782 cases discharged from these hospitals during the studied period. Results: The most frequent HAIs were reported by the intensive care units (48.4%), the most common infections being the following: bronchopneumonia (25.3%), enterocolitis with Clostridioides difficile (23.3%), sepsis, surgical wound infections and urinary tract infections. At the basis of HAI were 22 pathogens, but the five most common germs were Clostridioides difficile, Acinetobacter baumannii, Klebsiella pneumoniae, Pseudomonas aeruginosa and Staphylococcus aureus. Bronchopneumonia have been most frequently reported in intensive care units, the most common being identified the Acinetobacter baumannii agent. Sepsis and central catheter infections also appeared predominantly in intensive care units, more often with Klebsiella pneumoniae. The enterocolitis with Clostridioides difficile, were the apanage of the medical sections. Infections with Staphylococcus aureus have been identified predominantly in the surgical sections at the level of the surgical wounds. Urinary infections had a similar distribution in the intensive care units, the medical and surgical sections, with Klebsiella pneumoniae being the most commonly incriminated agent. Conclusions: We showed a clear correspondence between the medical units and the type of HAI: what recommends the rapid, vigilant and oriented application of the prevention and control strategies of the HAI.

M. Zamparas, V. Kapsalis, Grigorios L. Kyriakopoulos et al.

Abstract At this study a multi-criteria model was developed to examine the available procedures, techniques and methods of handling infectious waste in the large healthcare unit of University Regional General Hospital of Patras, Western Greece. Particularly, this study examined the: a) current legislation and Directives issued for medical waste management at Greece and among the other EU-members, b) contribution of healthcare wastes (HCW) generation rate on social and economic parameters in selected European countries, c) available procedures, techniques, and methods upon the disposal of infectious wastes at the healthcare studied, and, d) propositions for integrated management of such hazardous wastes. Specifically, the Analytic Hierarchy Process (AHP) methodology was applied under pair wise comparison matrices in two stages: 1) the scale factors and the indicators, and 2) the criteria and their sub–criteria. The assessment of these pair wise matrices included the indicators and the sub–criteria. Subsequently, two pair wise comparison matrices, upon a) the “Fulfillment of environmental objectives” indicator and b) the “Energy consumption” sub criterion, were denoted. The AHP methodology yielded good results; however there is still space of improving the environmental performance. The normalized relative weights obtained for the criteria and sub criteria motivated specific actions that have to be handled. Particularly, the results indicated a very good value in environmental management criteria due the values obtained for the commitment towards the environmental policy standards and the waste management procedures. However, further improvements on staff awareness (such as development programs to enhance sensitivity) and more green purchasing suppliers, should be further addressed.