The sleep status of patients in the surgical intensive care unit (ICU) significantly impacts their recoveries. However, the effects of surgical procedures on sleep are rarely studied.

European health care systems regard information and communication technology as a necessity in supporting future health care provision by community home care services to home-dwelling older adults. Communication technology enabling synchronous communication between 2 or more human actors at a distance constitutes a significant component of this ambition, but few reviews have synthesized research relating to this particular type of technology. As evaluations of information and communication technology in health care services favor measurements of effectiveness over the experiences and dynamics of putting these technologies into use, the nuances involved in technology implementation processes are often omitted.

The high prevalence of noncommunicable diseases and the growing importance of social media have prompted health care professionals (HCPs) to use social media to deliver health information aimed at reducing lifestyle risk factors. Previous studies have acknowledged that the identification of elements that influence user engagement metrics could help HCPs in creating engaging posts toward effective health promotion on social media. Nevertheless, few studies have attempted to comprehensively identify a list of elements in social media posts that could influence user engagement metrics.

Health care organizations globally have seen a significant increase in the frequency of cyberattacks in recent years. Cyberattacks cause massive disruptions to health service delivery and directly impact patient safety through disruption and treatment delays. Given the increasing number of cyberattacks in low- and middle-income countries (LMICs), there is a need to explore the interventions put in place to plan for cyberattacks and develop cyber resilience.

Social determinants of health (SDoH) such as housing insecurity are known to be intricately linked to patients' health status. More efficient methods for abstracting structured data on SDoH can help accelerate the inclusion of exposome variables in biomedical research and support health care systems in identifying patients who could benefit from proactive outreach. Large language models (LLMs) developed from Generative Pre-trained Transformers (GPTs) have shown potential for performing complex abstraction tasks on unstructured clinical notes.

Given the complexity and diversity of lichenoid vulvar disease (LVD) risk factors, it is crucial to actively explore these factors and construct personalized warning models using relevant clinical variables to assess disease risk in patients. Yet, to date, there has been insufficient research, both nationwide and internationally, on risk factors and warning models for LVD. In light of these gaps, this study represents the first systematic exploration of the risk factors associated with LVD.

Postpartum anxiety and depression are common in new parents. While effective interventions exist, they are often delivered in person, which can be a barrier for some parents seeking help. One approach to overcoming these barriers is the delivery of evidence-based self-help interventions via websites, smartphone apps, and other digital media.

Lack of adherence to prescribed medication is common among patients with depression in China, posing serious challenges to the health care system. Online health communities have been found to be effective in enhancing patient compliance. However, empirical evidence supporting this effect in the context of depression treatment is absent, and the influence of online health community content on patients' attitudes toward medication adherence is also underexplored.

The massive increase in the number of published scientific articles enhances knowledge but makes it more complicated to summarize results. The Medical Subject Headings (MeSH) thesaurus was created in the mid-20th century with the aim of systematizing article indexing and facilitating their retrieval. Despite the advent of search engines, few studies have questioned the relevance of the MeSH thesaurus, and none have done so systematically.

Cognitive biases in clinical decision-making significantly contribute to errors in diagnosis and suboptimal patient outcomes. Addressing these biases presents a formidable challenge in the medical field.

Recent studies offer conflicting conclusions about the effectiveness of digital health interventions in changing physical activity behaviors. In addition, research focusing on digital health interventions for college students remains relatively scarce.

The advancement of large language models (LLMs) offers significant opportunities for health care, particularly in the generation of medical documentation. However, challenges related to ensuring the accuracy and reliability of LLM outputs, coupled with the absence of established quality standards, have raised concerns about their clinical application.

Health information technologies, including electronic health records (EHRs), have revolutionized health care delivery. These technologies promise to enhance the efficiency and quality of care through improved patient health information management. Despite the transformative potential of EHRs, the extent to which patient access contributes to increased engagement with health care services within different clinical setting remains a distinct and underexplored facet.

With the extensive volume of information from various and diverse data sources, it is essential to present information in a way that allows for quick understanding and interpretation. This is particularly crucial in health care, where timely insights into a patient's condition can be lifesaving. Holistic visualizations that integrate multiple data variables into a single visual representation can enhance rapid situational awareness and support informed decision-making. However, despite the existence of numerous guidelines for different types of visualizations, this study reveals that there are currently no specific guidelines or principles for designing holistic integrated information visualizations that enable quick processing and comprehensive understanding of multidimensional data in time-critical contexts. Addressing this gap is essential for enhancing decision-making in time-critical scenarios across various domains, particularly in health care.

Studies show that the use of information and communications technologies (ICTs), including smartphones, tablets, computers, and the internet, varies by demographic factors such as age, gender, and educational attainment. However, the connections between ICT use and factors such as ethnicity and English proficiency, especially among Asian American older adults, remain less explored. The technology acceptance model (TAM) suggests that 2 key attitudinal factors, perceived usefulness (PU) and perceived ease of use (PEOU), influence technology acceptance. While the TAM has been adapted for older adults in China, Taiwan, Singapore, and Korea, it has not been tested among Asian American older adults, a population that is heterogeneous and experiences language barriers in the United States.

The World Health Organization has set a global strategy to eliminate cervical cancer, emphasizing the need for cervical cancer screening coverage to reach 70%. In response, China has developed an action plan to accelerate the elimination of cervical cancer, with Hubei province implementing China's first provincial full-coverage screening program using an artificial intelligence (AI) and cloud-based diagnostic system.

The COVID-19-Curated and Open Analysis and Research Platform (CO-CONNECT) project worked with 22 organizations across the United Kingdom to build a federated platform, enabling researchers to instantaneously and dynamically query federated datasets to find relevant data for their study. Finding relevant data takes time and effort, reducing the efficiency of research. Although data controllers could understand the value of such a system, there were significant challenges and delays in setting up the platform in response to COVID-19. This paper aims to present the challenges and lessons learned from the CO-CONNECT project to support other similar initiatives in the future. The project encountered many challenges, including the impacts of lockdowns on collaboration, understanding the new architecture, competing demands on people's time during a pandemic, data governance approvals, different levels of technical capabilities, data transformation to a common data model, access to granular-level laboratory data, and how to engage public and patient representatives meaningfully on a highly technical project. To overcome these challenges, we developed a range of methods to support data partners such as explainer videos; regular, short, "touch base" videoconference calls; drop-in workshops; live demos; and a standardized technical onboarding documentation pack. A 4-stage data governance process emerged. The patient and public representatives were fully integrated team members. Persistence, patience, and understanding were key. We make 8 recommendations to change the landscape for future similar initiatives. The new architecture and processes developed are being built upon for non-COVID-19-related data, providing an infrastructural legacy.

Adherence to oral anticoagulant therapy is essential to prevent ischemic stroke in patients with atrial fibrillation (AF).

Speech sound disorders (SSDs) are common communication challenges in children, typically assessed by speech-language pathologists (SLPs) using standardized tools. However, traditional evaluation methods are time-intensive and prone to variability, raising concerns about reliability.

Accurate hospital length of stay (LoS) prediction enables efficient resource management. Conventional LoS prediction models with limited covariates and nonstandardized data have limited reproducibility when applied to the general population.

The prevalence of overweight and obesity is increasing at an alarming rate in children and adolescents worldwide. Given the dimension of the problem, treatments of childhood obesity are recognized as of extreme importance. Current evidence indicates that behavioural and cognitive behavioural strategies combined with diet and physical activity approaches may assist in reducing adolescent obesity.