Health Care Science最新文献

There is increasing research into the potential benefits of incorporating artificial intelligence (AI) and machine learning algorithms into emergency medical services. AI is finding new applications across a wide range of sectors, one of which is healthcare, where it is being used to enhance clinical diagnostics. AI solutions have enormous untapped potential to improve healthcare efficiency and quality, thus researchers have focused heavily on emergency medicine (EM). Many individuals without prior experience with any physician often receive their initial medical care in the emergency room. Two areas that could benefit from the implementation of AI are reducing waiting times and enhancing diagnostic capabilities. This study provides further explanation of how AI is used in emergency rooms. Several machine learning-based algorithms are also addressed. In this research, we summarise recent developments in the use of AI in EM. This research tries to summarise the usefulness of AI in EM by looking at recent developments in emergency department operations and clinical patient management.

This study examines the UK's May 2023 judgment in an international organ trafficking and organ tourism case. Human trafficking for organ removal is one of the least understood but growing forms of trafficking worldwide. Countries in the Middle East, Asia, and the Americas are often widely criticized by the international transplant community as sites for organ trafficking. However, we believe that when discussing this issue, it is not just these areas that need to be addressed. What is particularly special is that this case not only involves transnational human trafficking, organ trafficking, and illegal organ transplantation interest chains but also involves the participation of national political officials and complex social and humanistic factors. This article focuses on the current ethical and policy issues involved in organ transplant tourism and organ trafficking and analyzes the implications of this case for our country's donation and transplantation work.

Noncommunicable diseases (NCDs) are a significant public concern, greatly impacting the economic and social development in China. In 2019, NCDs accounted for a staggering 88.5% of total deaths in China, with cardiovascular diseases, cancer, chronic respiratory diseases, and diabetes—the four major chronic diseases—contributing to a premature mortality rate of 16.5% [1]. The complexity of NCDs arises from the involvement of multiple genetic and environmental factors that interact in intricate ways. The complexity is characterized by a multitude of interactions among genes, proteins, and metabolic pathways throughout the various stages of life. Furthermore, these interactions demonstrate time-dependent specificity during the different phases of the life course. Prior research on the etiology of NCDs tended to focus on “specificity,” which overlooked the concept of “universality.” Studies are often conducted from one risk factor, one disease, or one dimension, leading to an insufficient understanding of NCD etiology and less than satisfactory outcomes in prevention and control efforts. Therefore, the aim of this review is to highlight and propose a new trend in NCD etiology research, considering the research focus and research methodology.

The relationships among NCDs are intricate, and patients often show distinct patterns of multiple diseases, reflecting population heterogeneity in comorbidity. The study of comorbidity patterns among populations affected by NCDs can offer valuable insights for developing effective prevention and management strategies. In a retrospective study by Jansana et al. [2] using electronic health records, five multimorbidity clusters were identified among breast cancer survivors in Spain; notably, the “musculoskeletal and cardiovascular disease” pattern showed a significantly higher risk of mortality than other NCDs. Advancements in computational science contribute to the emergence of network analysis based on graph theory as a powerful tool for understanding the complexity of comorbidity from a holistic and systemic perspective. Graph theory in network analysis facilitates the construction of comorbidity networks in which disease status is represented as nodes and risk associations are shown as edges, thereby visualizing the co-occurrence of diseases in a concise and intuitive manner. Such topological approaches enable the prioritization of disease severity and identification of the core disease within a comorbidity network. Furthermore, network clustering techniques have been applied to identify specific comorbidity patterns in NCDs. However, cautiousness in interpreting the identified patterns is essential because some network topology indexes may lack practical significance. The challenge in interpreting the identified patterns can be addressed by considering association rules. Typically, association rule mining is used to identify comorbidity patterns, and network analysis is used to

The purpose of the National Digital Health Mission (or more precisely, the Ayushman Bharat Digital Mission) is to promote and facilitate the evolution of the National Digital Health Ecosystem in India. The Health Facility Registry, the Healthcare Professionals Registry, and the Unified Health Interface are the major components of the proposed system—which is intended to be a co-operative federated architecture with optimal interoperability provision coupled with authorized access.

China's rapid population aging and remarkable family-level changes have raised concerns about the weakening of its family-based elderly care. The last decade indeed has seen a clear departure from multigenerational living to alternative living arrangements such as living with spouse only and solo living. However, ample evidence suggests that Chinese families have demonstrated considerable resilience amidst profound sociodemographic changes. This review article highlights the importance of government–society cooperation in meeting the social challenges of population aging. A key factor is the persistient filial piety norms, which enable children living far or close, migrant or nonmigrant, to rearrange financial, instrumental, and emotional support to aging parents. Equally important is the step-in of the government to share elderly care responsibilities, provide support through deepening pension and healthcare reforms, and implement the active and healthy aging agenda. How the two factors play out over the next decade and beyond will have profound implications on the living arrangement, intergenerational support, and wellbeing of older adults in China.