Hasil untuk "Medical legislation"

Angela Tembo, Andy Gray, Tsitsi Nyamuzihwa et al.

Access to HIV services in South Africa remains challenging, despite their availability in the public healthcare sector. While the legislative framework allows for the provision of these services in community pharmacies, the process is often complex. This article describes various models for the provision of HIV services in community pharmacies in South Africa through a review of existing policies and legislation. It further discusses barriers and opportunities for the expansion of services. The existing legal framework enables prescribing by healthcare professionals other than medical practitioners through authorisations issued under either the Medicines and Related Substances Act of 1965 or the Nursing Act of 2005. Community pharmacies have extended their role beyond dispensing medication, with the emergence of telehealth and potential initiatives such as Pharmacist-Initiated Management of Antiretroviral Therapy (PIMART). Telehealth, accelerated by the COVID-19 pandemic, provides remote consultations and electronic prescriptions. PIMART, on the other hand, can empower pharmacists to initiate and manage antiretroviral therapy (ART) for HIV patients, a role traditionally reserved for clinicians. Extending Nurse-Initiated Management of Antiretroviral Therapy (NIMART) into the private sector could further increase ART rollout. Despite these advancements made in the last two decades, legislative reforms are necessary to fully realise the potential of community pharmacies for providing HIV services.

Asli Zeybek, Nguyen-Son Le, Utz Settmacher et al.

In early 2024, 8,394 patients were waitlisted for solid organ transplantation in Germany. Long waiting times and declining donor numbers highlight the urgency for political measures to improve the organ donation system. This retrospective analysis examined the attitudes of the 19th German Bundestag members towards organ donation and their voting behavior on the opt-out system, which was decided on January 6, 2020. The results were analyzed in relation to party affiliation, age, gender, and educational background. Among members of parliament (MP), 34% were in favor for organ donation, 8% were critical, and 58% made no statement on organ donation at all. Younger members were less likely to express an opinion than older ones (p < 0.001). CDU/CSU (50%) and members of the SPD (48%) showed the highest approval, while The Green Party (39%) showed the lowest approval rate. AfD members had the highest abstention rate (96%, p < 0.001). SPD (66%, OR 33.24) and CDU/CSU (63%, OR 28.32) strongly supported the opt-out system, while AfD (94%) and The Green Party (88%) strongly rejected. Overwhelming majorities of the AfD (94%), The Green Party (89%), and FDP (81%) members who had not previously expressed an opinion to organ donation and legislation voted against the opt-out system, whereas majorities of SPD (66%) and CDU/CSU (66%) voted in favor. Most members who held opposing views on organ donation voted against the opt-out solution. Party affiliation was strongly correlated with both attitudes towards organ donation and voting behavior as well as a considerable lack of in-depth knowledge regarding transplant legislation. A fact-based discussion involving medical professionals, who play a key role in the organ donation process, is essential, along with a thorough understanding of the organ transplant law.

Jingyun Chen, Yading Yuan

Federated Learning (FL) presents a promising avenue for collaborative model training among medical centers, facilitating knowledge exchange without compromising data privacy. However, vanilla FL is prone to server failures and rarely achieves optimal performance on all participating sites due to heterogeneous data distributions among them. To overcome these challenges, we propose Gossip Contrastive Mutual Learning (GCML), a unified framework to optimize personalized models in a decentralized environment, where Gossip Protocol is employed for flexible and robust peer-to-peer communication. To make efficient and reliable knowledge exchange in each communication without the global knowledge across all the sites, we introduce deep contrast mutual learning (DCML), a simple yet effective scheme to encourage knowledge transfer between the incoming and local models through collaborative training on local data. By integrating DCML with other efforts to optimize site-specific models by leveraging useful information from peers, we evaluated the performance and efficiency of the proposed method on three publicly available datasets with different segmentation tasks. Our extensive experimental results show that the proposed GCML framework outperformed both centralized and decentralized FL methods with significantly reduced communication overhead, indicating its potential for real-world deployment. Upon the acceptance of manuscript, the code will be available at: https://github.com/CUMC-Yuan-Lab/GCML.

Matias Etcheverry, Thibaud Real, Pauline Chavallard

This study introduces a method for automating the consolidation process in a legal context, a time-consuming task traditionally performed by legal professionals. We present a generative approach that processes legislative texts to automatically apply amendments. Our method employs light quantized generative model, fine-tuned with LoRA, to generate accurate and reliable amended texts. To the authors knowledge, this is the first time generative models are used on legislative text consolidation. Our dataset is publicly available on HuggingFace1. Experimental results demonstrate a significant improvement in efficiency, offering faster updates to legal documents. A full automated pipeline of legislative text consolidation can be done in a few hours, with a success rate of more than 63% on a difficult bill.

Maxim V. Polyakov, Danila S. Sirotin

This article presents a study on the influence of antenna parameters in medical microwave radiometers on brightness temperature. A series of computational experiments was conducted to analyse the dependence of brightness temperature on antenna characteristics. Various antenna parameters and their effect on the distribution of electromagnetic fields in biological tissues were examined. It was demonstrated that considering the antenna mismatch parameter is crucial when modelling the brightness temperature of biological tissues, contributing about 2 percent to its formation. The depth range of brightness temperature measurement was determined. The dependence of brightness temperature on the antenna diameter and frequency was established. The findings of this study can be applied to improve medical microwave radiometers and enhance their efficiency in the early diagnosis of various diseases.

Jiseon Kim, Dongkwan Kim, Joohye Jeong et al.

Special interest groups (SIGs) in the U.S. participate in a range of political activities, such as lobbying and making campaign donations, to influence policy decisions in the legislative and executive branches. The competing interests of these SIGs have profound implications for global issues such as international trade policies, immigration, climate change, and global health challenges. Despite the significance of understanding SIGs' policy positions, empirical challenges in observing them have often led researchers to rely on indirect measurements or focus on a select few SIGs that publicly support or oppose a limited range of legislation. This study introduces the first large-scale effort to directly measure and predict a wide range of bill positions-Support, Oppose, Engage (Amend and Monitor)- across all legislative bills introduced from the 111th to the 117th Congresses. We leverage an advanced AI framework, including large language models (LLMs) and graph neural networks (GNNs), to develop a scalable pipeline that automatically extracts these positions from lobbying activities, resulting in a dataset of 42k bills annotated with 279k bill positions of 12k SIGs. With this large-scale dataset, we reveal (i) a strong correlation between a bill's progression through legislative process stages and the positions taken by interest groups, (ii) a significant relationship between firm size and lobbying positions, (iii) notable distinctions in lobbying position distribution based on bill subject, and (iv) heterogeneity in the distribution of policy preferences across industries. We introduce a novel framework for examining lobbying strategies and offer opportunities to explore how interest groups shape the political landscape.

Eremina Svetlana, Volkodavova Elena

In today’s unstable business environment, there is active development in the medical services market. Competition, as one of the main features of market relations, has a significant impact on medical organizations. The healthcare market has its specific characteristics, as the outcomes of medical organizations’ activities include not only economic but also certain medical effects. The state, in turn, engages in creating and maintaining a competitive environment in the medical services sector through the enforcement of antitrust legislation. Consequently, the management of the organization needs to constantly monitor and maintain competitiveness in the healthcare market to ensure its development and successful functioning. The managerial team of a medical organization plays a crucial role in accomplishing this task. This article explores the problem of forming an effective management team that, despite changes in external environmental factors and the internal state of the medical organization, organizes the work of the team aimed at the successful implementation of both general and key competencies of the organization and its individual specialists, thereby enhancing the competitiveness and stability of the organization in its market segments. Methodological approaches to forming an effective management team are presented. Field research on the stages and results of forming a management team in a Russian medical organization is provided. Directions and recommendations for forming an effective management team are developed. The conclusion emphasizes the necessity of using technologies for forming an effective management team in the practice of Russian medical organizations.

Jaime Sevilla, Nikolay Babakov, Ehud Reiter et al.

In this paper, we propose a model for building natural language explanations for Bayesian Network Reasoning in terms of factor arguments, which are argumentation graphs of flowing evidence, relating the observed evidence to a target variable we want to learn about. We introduce the notion of factor argument independence to address the outstanding question of defining when arguments should be presented jointly or separately and present an algorithm that, starting from the evidence nodes and a target node, produces a list of all independent factor arguments ordered by their strength. Finally, we implemented a scheme to build natural language explanations of Bayesian Reasoning using this approach. Our proposal has been validated in the medical domain through a human-driven evaluation study where we compare the Bayesian Network Reasoning explanations obtained using factor arguments with an alternative explanation method. Evaluation results indicate that our proposed explanation approach is deemed by users as significantly more useful for understanding Bayesian Network Reasoning than another existing explanation method it is compared to.

Kasra Borazjani, Naji Khosravan, Leslie Ying et al.

The use of machine learning (ML) for cancer staging through medical image analysis has gained substantial interest across medical disciplines. When accompanied by the innovative federated learning (FL) framework, ML techniques can further overcome privacy concerns related to patient data exposure. Given the frequent presence of diverse data modalities within patient records, leveraging FL in a multi-modal learning framework holds considerable promise for cancer staging. However, existing works on multi-modal FL often presume that all data-collecting institutions have access to all data modalities. This oversimplified approach neglects institutions that have access to only a portion of data modalities within the system. In this work, we introduce a novel FL architecture designed to accommodate not only the heterogeneity of data samples, but also the inherent heterogeneity/non-uniformity of data modalities across institutions. We shed light on the challenges associated with varying convergence speeds observed across different data modalities within our FL system. Subsequently, we propose a solution to tackle these challenges by devising a distributed gradient blending and proximity-aware client weighting strategy tailored for multi-modal FL. To show the superiority of our method, we conduct experiments using The Cancer Genome Atlas program (TCGA) datalake considering different cancer types and three modalities of data: mRNA sequences, histopathological image data, and clinical information. Our results further unveil the impact and severity of class-based vs type-based heterogeneity across institutions on the model performance, which widens the perspective to the notion of data heterogeneity in multi-modal FL literature.

Mai Mahgoub Ziyada, R. Elise B Johansen, Mona Berthelsen et al.

Abstract Background Female genital cutting (FGC) may cause a series of health problems that require specialized healthcare. General practitioners (GPs) are gatekeepers to specialized healthcare services in Norway. To refer girls and women subjected to FGC to appropriate services, GPs need to assess whether the health problems reported by these patients are related to FGC. However, we do not know to what degree GPs assess FGC as a potential cause of the patients' health problems. We also know little about the GPs' patterns of training and knowledge of FGC and their effect on the GPs' assessment of FGC as a potential cause of health problems. Method We employed a cross-sectional online survey among GPs in Norway to examine: 1) patterns of received training on FGC, self-assessed knowledge, and experiences with patients with FGC-related problems and 2) the association between these three factors and the GPs' assessment of FGC as a potential cause of patients' health problems. A total of 222 GPs completed the survey. Data were analysed using binary logistic regression, where we also adjusted for sociodemographic characteristics. Results Two-third of the participants had received training on FGC, but only over half received training on FGC-related health problems. Over 75% of the participants stated a need for more knowledge of FGC typology and Norwegian legislation. While the majority of the participants assessed their knowledge of FGC medical codes as inadequate, this was not the case for knowledge of the cultural aspects of FGC. Female GPs were more likely to have experience with patients with FGC-related health problems than male GPs. Among GPs with experience, 46% linked health problems to FGC in patients unaware of the connection between FGC and such health problems. GPs were more likely to assess FGC as a potential cause of health problems when they had experience with patients having FGC-related problems and when they assessed their knowledge of FGC typology and FGC-related medical codes as adequate. Conclusion To improve their assessment of FGC as a potential cause of patients' health problems, GPs should receive comprehensive training on FGC, with particular emphasis on typology, health problems, and medical codes.

Güvenç Koçkaya, Gülpembe Oguzhan, Selin Ökçün et al.

IntroductionThis study aims to determine the out-of-pocket health expenditures of households in Turkey where individuals with rare diseases are residing.MethodsThe research population consisted registered members of associations who are members of the Rare Diseases Network. In addition to the general analysis including all participants, expenditures based on characteristics of disease holders were also calculated.ResultsA total of 439 participants were included in the analysis. We determined that special nutrition was the highest expenditure group and emergency departments were the lowest expenditure group. When all the participants were evaluated, the average cost of rare diseases was found to be Ł22,743 (€2,877). A significant relationship was found between income status and out-of-pocket health expenditures (p = 0.012).DiscussionPolicy makers should consider inclusion of special nutritional products and medical/non-medical devices used in treatment of rare diseases within the scope of reimbursement and the development of orphan drug legislation as the first actions to be taken.

Florentin Bieder, Julia Wolleb, Alicia Durrer et al.

Denoising diffusion models have recently achieved state-of-the-art performance in many image-generation tasks. They do, however, require a large amount of computational resources. This limits their application to medical tasks, where we often deal with large 3D volumes, like high-resolution three-dimensional data. In this work, we present a number of different ways to reduce the resource consumption for 3D diffusion models and apply them to a dataset of 3D images. The main contribution of this paper is the memory-efficient patch-based diffusion model \textit{PatchDDM}, which can be applied to the total volume during inference while the training is performed only on patches. While the proposed diffusion model can be applied to any image generation tasks, we evaluate the method on the tumor segmentation task of the BraTS2020 dataset and demonstrate that we can generate meaningful three-dimensional segmentations.

Srirang Manohar, Ioannis Sechopoulos, Mark A. Anastasio et al.

Phantoms are test objects used for initial testing and optimization of medical imaging techniques, but these rarely capture the complex properties of the tissue. Here we introduce super phantoms, that surpass standard phantoms being able to replicate complex anatomic and functional imaging properties of tissues and organs. These super phantoms can be computer models, inanimate physical objects, or ex-vivo organs. Testing on these super phantoms, will enable iterative improvements well before in-vivo studies, fostering innovation. We illustrate super phantom examples, address development challenges, and envision centralized facilities supporting multiple institutions in applying these models for medical advancements.

Xuanang Xu, Hannah H. Deng, Tianyi Chen et al.

Federated learning (FL) can collaboratively train deep learning models using isolated patient data owned by different hospitals for various clinical applications, including medical image segmentation. However, a major problem of FL is its performance degradation when dealing with data that are not independently and identically distributed (non-iid), which is often the case in medical images. In this paper, we first conduct a theoretical analysis on the FL algorithm to reveal the problem of model aggregation during training on non-iid data. With the insights gained through the analysis, we propose a simple yet effective method, federated cross learning (FedCross), to tackle this challenging problem. Unlike the conventional FL methods that combine multiple individually trained local models on a server node, our FedCross sequentially trains the global model across different clients in a round-robin manner, and thus the entire training procedure does not involve any model aggregation steps. To further improve its performance to be comparable with the centralized learning method, we combine the FedCross with an ensemble learning mechanism to compose a federated cross ensemble learning (FedCrossEns) method. Finally, we conduct extensive experiments using a set of public datasets. The experimental results show that the proposed FedCross training strategy outperforms the mainstream FL methods on non-iid data. In addition to improving the segmentation performance, our FedCrossEns can further provide a quantitative estimation of the model uncertainty, demonstrating the effectiveness and clinical significance of our designs. Source code is publicly available at https://github.com/DIAL-RPI/FedCross.

Chen Chen, Matin Yarmand, Varun Singh et al.

Contouring is an indispensable step in Radiotherapy (RT) treatment planning. However, today's contouring software is constrained to only work with a 2D display, which is less intuitive and requires high task loads. Virtual Reality (VR) has shown great potential in various specialties of healthcare and health sciences education due to the unique advantages of intuitive and natural interactions in immersive spaces. VR-based radiation oncology integration has also been advocated as a target healthcare application, allowing providers to directly interact with 3D medical structures. We present VRContour and investigate how to effectively bring contouring for radiation oncology into VR. Through an autobiographical iterative design, we defined three design spaces focused on contouring in VR with the support of a tracked tablet and VR stylus, and investigating dimensionality for information consumption and input (either 2D or 2D + 3D). Through a within-subject study (n = 8), we found that visualizations of 3D medical structures significantly increase precision, and reduce mental load, frustration, as well as overall contouring effort. Participants also agreed with the benefits of using such metaphors for learning purposes.

Evangelos Mantzaris, Pregala Pillay

The article is an empirical attempt to research, analyse, and dissect the corrupt involvement of legal practitioners in illegal and fraudulent acts, and mainly their involvement in litigation associated with issues of medical negligence. This is done primarily but not exclusively through the utilisation of several qualitative research methods, including the content analysis of primary literary sources such as official state documents, existing legislation and court proceedings and personal interviews with senior public servants, as well as secondary sources. Beginning with a short exploration of South Africa's public legal terrain and the fears of sections of the statutory leadership of the legal profession, the article continues with the identification of key findings in several the country's provinces, the modus operandi of the corrupt individuals and groups, as well as the monetary, financial and social repercussions of such actions.

Francesca Baratta, Marco Simiele, Irene Pignata et al.

Current legislation in Italy provides that medical <i>Cannabis</i> may be administered orally or by inhalation. One of the fundamental criteria for the administration of oral formulations is that they deliver a known consistent quantity of the active ingredients to ensure uniform therapies leading to the optimisation of the risks/benefits. In 2018, our group developed an improved <i>Cannabis</i> oil extraction technique. The objective of the present work was to carry out a stability study for the oil extracts obtained by this method. Furthermore, in order to facilitate the consumption of the prescribed medical <i>Cannabis</i> therapy by patients, a standard procedure was defined for the preparation of a single-dose preparation for oral use (hard capsules) containing the oil extract; thereafter, the quality and stability were evaluated. The hard capsules loaded with the oil extract were analysed and found to be uniform in content. The encapsulation process did not alter the quantity of the active molecule present in the oil. The stability tests yielded excellent results. Since the capsule dosage form is easily transported and administered, has pleasant organoleptic properties and is stable at room temperature for extended periods of time, this would facilitate the adherence to therapy by patients in treatment.

Mingquan Lin, Jacob Wynne, Yang Lei et al.

Medical imaging is widely used in cancer diagnosis and treatment, and artificial intelligence (AI) has achieved tremendous success in various tasks of medical image analysis. This paper reviews AI-based tumor subregion analysis in medical imaging. We summarize the latest AI-based methods for tumor subregion analysis and their applications. Specifically, we categorize the AI-based methods by training strategy: supervised and unsupervised. A detailed review of each category is presented, highlighting important contributions and achievements. Specific challenges and potential AI applications in tumor subregion analysis are discussed.

Rongguang Wang, Pratik Chaudhari, Christos Davatzikos

Domain shift, the mismatch between training and testing data characteristics, causes significant degradation in the predictive performance in multi-source imaging scenarios. In medical imaging, the heterogeneity of population, scanners and acquisition protocols at different sites presents a significant domain shift challenge and has limited the widespread clinical adoption of machine learning models. Harmonization methods which aim to learn a representation of data invariant to these differences are the prevalent tools to address domain shift, but they typically result in degradation of predictive accuracy. This paper takes a different perspective of the problem: we embrace this disharmony in data and design a simple but effective framework for tackling domain shift. The key idea, based on our theoretical arguments, is to build a pretrained classifier on the source data and adapt this model to new data. The classifier can be fine-tuned for intra-site domain adaptation. We can also tackle situations where we do not have access to ground-truth labels on target data; we show how one can use auxiliary tasks for adaptation; these tasks employ covariates such as age, gender and race which are easy to obtain but nevertheless correlated to the main task. We demonstrate substantial improvements in both intra-site domain adaptation and inter-site domain generalization on large-scale real-world 3D brain MRI datasets for classifying Alzheimer's disease and schizophrenia.

Padayachee N, Rothberg A, Butkow N et al.

N Padayachee,1 A Rothberg,2 N Butkow,1 I Truter3 1Department of Pharmacy and Pharmacology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; 2School of Therapeutic Sciences, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; 3Department of Pharmacy and Pharmacology, Nelson Mandela University, Port Elizabeth, South AfricaCorrespondence: N Padayachee 7 York Road, Parktown, Johannesburg 2193, South AfricaTel +27 842 302364Email Neelaveni.padayachee@wits.ac.zaBackground: South African medical insurance schemes (known as medical schemes) cover about 17% of the population. Within these schemes, access to medicines for a defined set of chronic diseases is mandated by legislation. However, much of the responsibility for treatment of minor conditions with non-prescription over-the-counter (OTC) medicines has been transferred to the individuals within the medical schemes. The overall expenditure on pharmacist-assisted therapy (PAT)/OTC medicines in South Africa is considerable and medical schemes endeavor to limit amounts paid out by devising strategies that will limit their financial exposure.Aim: To investigate how benefit design and other factors within two medical schemes influenced access to and payment for OTC medicines and to explore whether access to OTC medicines by individuals impacted on utilization of other health-care services.Methods: Medical scheme data were obtained from a leading administrator for two health plans: one with comprehensive benefits covering 4593 beneficiaries (designated HI)&nbsp;and the other with lower benefits covering 54,374 beneficiaries (LO). Extracted data included beneficiary demographics, OTC medicines prescribed by doctors and/or dispensed by pharmacists, and monetary amounts claimed by individuals and paid by the medical schemes. Doctor consultations, costs and payments were also extracted, as were beneficiaries&rsquo; records of their chronic disease(s) and any episode(s) requiring hospitalization.Results: Some 60&ndash; 70% of beneficiaries submitted claims for OTC medicines accessed directly or recommended by a pharmacist, and 80&ndash; 90% claimed OTC medicines that were prescribed by a doctor during a consultation. Amounts claimed and percentages of original products prescribed were substantially higher when accessed directly by beneficiaries or recommended by pharmacists than when doctors prescribed the medicines. In multivariate analysis, there was no clear advantage of offering access to OTC medicines in order to reduce visits to general practitioners, although in the LO plan it appeared that beneficiaries with chronic diseases made less use of the OTC benefit and more use of medical specialists.Conclusion: Within these two plans, there were higher costs and greater use of original products when beneficiaries or pharmacies accessed OTC medicines than when these medicines were prescribed by doctors. A key question is whether access to these medicines and the costs thereof would be managed better if paid for directly by individuals and not as insured benefits through the medical scheme.Keywords: medical schemes, over-the-counter benefit, acute medicines, pharmacist-assisted benefit, over-the-counter medicines