Fog computing is a decentralized computing infrastructure that processes data at or near its source, reducing latency and bandwidth usage. This technology is gaining traction in healthcare due to its potential to enhance real-time data processing and decision-making capabilities in critical medical scenarios. A systematic review of existing literature on fog computing in healthcare was conducted. The review included searches in major databases such as PubMed, IEEE Xplore, Scopus, and Google Scholar. The search terms used were \"fog computing in healthcare,\" \"real-time diagnostics and fog computing,\" \"continuous patient monitoring fog computing,\" \"predictive analytics fog computing,\" \"interoperability in fog computing healthcare,\" \"scalability issues fog computing healthcare,\" and \"security challenges fog computing healthcare.\" Articles published between 2010 and 2023 were considered. Inclusion criteria encompassed peer-reviewed articles, conference papers, and review articles focusing on the applications of fog computing in healthcare. Exclusion criteria were articles not available in English, those not related to healthcare applications, and those lacking empirical data. Data extraction focused on the applications of fog computing in real-time diagnostics, continuous monitoring, predictive analytics, and the identified challenges of interoperability, scalability, and security. Fog computing significantly enhances diagnostic capabilities by facilitating real-time data analysis, crucial for urgent diagnostics such as stroke detection, by processing data closer to its source. It also improves monitoring during surgeries by enabling real-time processing of vital signs and physiological parameters, thereby enhancing patient safety. In chronic disease management, continuous data collection and analysis through wearable devices allow for proactive disease management and timely adjustments to treatment plans. Additionally, fog computing supports telemedicine by enabling real-time communication between remote specialists and patients, thereby improving access to specialist care in underserved regions. Fog computing offers transformative potential in healthcare, improving diagnostic precision, patient monitoring, and personalized treatment. Addressing the challenges of interoperability, scalability, and security will be crucial for fully realizing the benefits of fog computing in healthcare, leading to a more connected and efficient healthcare environment.

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BACKGROUND: Assessment of artificial intelligence (AI)-based models across languages is crucial to ensure equitable access and accuracy of information in multilingual contexts. This study aimed to compare AI model efficiency in English and Arabic for infectious disease queries.
METHODS: The study employed the METRICS checklist for the design and reporting of AI-based studies in healthcare. The AI models tested included ChatGPT-3.5, ChatGPT-4, Bing, and Bard. The queries comprised 15 questions on HIV/AIDS, tuberculosis, malaria, COVID-19, and influenza. The AI-generated content was assessed by two bilingual experts using the validated CLEAR tool.
RESULTS: In comparing AI models\' performance in English and Arabic for infectious disease queries, variability was noted. English queries showed consistently superior performance, with Bard leading, followed by Bing, ChatGPT-4, and ChatGPT-3.5 (P = .012). The same trend was observed in Arabic, albeit without statistical significance (P = .082). Stratified analysis revealed higher scores for English in most CLEAR components, notably in completeness, accuracy, appropriateness, and relevance, especially with ChatGPT-3.5 and Bard. Across the five infectious disease topics, English outperformed Arabic, except for flu queries in Bing and Bard. The four AI models\' performance in English was rated as \"excellent\", significantly outperforming their \"above-average\" Arabic counterparts (P = .002).
CONCLUSIONS: Disparity in AI model performance was noticed between English and Arabic in response to infectious disease queries. This language variation can negatively impact the quality of health content delivered by AI models among native speakers of Arabic. This issue is recommended to be addressed by AI developers, with the ultimate goal of enhancing health outcomes.

Over the past two decades, there has been a growing scholarly interest in the adoption of technology in healthcare. While numerous studies have delved into the effects of specific technologies on the performance of different organizational units and medical specialties, the findings have often been divergent. Unlike the established literature, our approach focuses on the organization\'s perspective to analyze how technology impacts process performance in hospital settings. More precisely, we compiled a tailored dataset from 56 healthcare organizations in Italy and conducted a comprehensive analysis of panel data from 2016 to 2019, utilizing Ordinary Least Squares (OLS) regression as our main analytical tool. The data shows a clear relationship between an organization\'s use of medical devices and its overall process performance. Our research highlights the importance of achieving substantial improvements in process performance by strategically integrating new technologies and devices. Policymakers are encouraged to consider introducing incentives to drive hospitals to invest in innovative technologies. Furthermore, monitoring expenditures on new devices could serve as a valuable metric for assessing the extent of technology adoption within clinical practices.

It is since the beginning of the so-called \'digital revolution\' in the 1950s that technological tools have been developed to simplify and optimise traditional, time-consuming, and laborious anamnestic collection for many physicians. In recent years, more and more sophisticated \'automated\' anamnestic collection systems have been developed, to the extent that they can actually enter daily clinical practice. This article not only provides a historical overview of the evolution of such tools, but also explores the ethical and medico-legal implications of the transition from traditional to digital anamnesis, including the protection of data confidentiality, the preservation of the communicative effectiveness of the doctor-patient dialogue and the safety of care in patients with poor digital and health literacy.

The complex nature of verbal patient-nurse communication holds valuable insights for nursing research, but traditional documentation methods often miss these crucial details. This study explores the emerging role of speech processing technology in nursing research, emphasizing patient-nurse verbal communication. We conducted case studies across various healthcare settings, revealing a substantial gap in electronic health records for capturing vital patient-nurse encounters. Our research demonstrates that speech processing technology can effectively bridge this gap, enhancing documentation accuracy and enriching data for quality care assessment and risk prediction. The technology\'s application in home healthcare, outpatient settings, and specialized areas like dementia care illustrates its versatility. It offers the potential for real-time decision support, improved communication training, and enhanced telehealth practices. This paper provides insights into the promises and challenges of integrating speech processing into nursing practice, paving the way for future patient care and healthcare data management advancements.

In response to challenges associated with extensive documentation practices within the NHS, this paper presents the outcomes of a structured brainstorming session as part of the Chief Nurse Fellows project titled \'Digital Documentation in Healthcare: Empowering Nurses and Patients for Optimal Care.\" Grounded in Dr. Rozzano Locsin\'s theory of \"Technological Competency as Caring in Nursing,\" this project leverages a Venn diagram framework to integrate Digital Maturity Assessment (DMA) results with the \"What Good Looks Like\" (WGLL) Framework, the ANCC Pathway to Excellence, and the eHospital EPR program vision of University Hospitals of Leicester NHS Trust. Participants, including Clinical IT facilitators and nursing leaders, engaged in identifying synergies and gaps across digital proficiency, nursing excellence, and patient-centric care, contributing actionable insights towards an optimized digital patient care model. The findings emphasize the need for holistic digital solutions that enhance documentation efficiency, support staff excellence, and improve patient outcomes.

UNASSIGNED: The global estimate of the aging population is progressively increasing in low and middle-income countries and this is accompanied by the limitations associated with the need for equitable and efficient healthcare delivery among this dire population. Unfortunately, despite the increasing numbers, the adoption of mobile phones is not balanced in the different populations with research showing young persons\' adoption rate is higher than that of elderly persons.
UNASSIGNED: This current study was conducted to identify elderly people\'s perceptions of the use of mobile phones to support the self-management of long-term illnesses at Kiruddu National Referral Hospital.
UNASSIGNED: This descriptive-cross-sectional design study was conducted on a sample population of 30 elderly individuals older than 60 years admitted at the outpatient department of Kiruddu National Referral Hospital, Kampala, Uganda. We conducted face-to-face interviews following an interview guide and one focus group discussion. We later used a feature mobile phone and a tablet mobile phone to assess the individual ease of use of each device. The audio recordings were professionally transcribed and transcripts were coded into NVIVO version 12 analysis software for thematic analysis.
UNASSIGNED: Almost all of the respondents who visited the facility had an ailment that hindered their full utilization of the mobile phone to support their self-care. This together with other factors like financial constraints, lack of support from the health workers on how to use mobile phones to support health, inadequate support from the facility, and cost of mobile data among others.
UNASSIGNED: This study provides empirical evidence that there is hardly a known mobile phone adoption model to enable policymakers, systems developers, and health workers to promote the elderly population\'s use of mobile phones to manage their long-term illnesses in Uganda.

In a prior practice and policy article published in Healthcare Science, we introduced the deployed application of an artificial intelligence (AI) model to predict longer-term inpatient readmissions to guide community care interventions for patients with complex conditions in the context of Singapore\'s Hospital to Home (H2H) program that has been operating since 2017. In this follow on practice and policy article, we further elaborate on Singapore\'s H2H program and care model, and its supporting AI model for multiple readmission prediction, in the following ways: (1) by providing updates on the AI and supporting information systems, (2) by reporting on customer engagement and related service delivery outcomes including staff-related time savings and patient benefits in terms of bed days saved, (3) by sharing lessons learned with respect to (i) analytics challenges encountered due to the high degree of heterogeneity and resulting variability of the data set associated with the population of program participants, (ii) balancing competing needs for simpler and stable predictive models versus continuing to further enhance models and add yet more predictive variables, and (iii) the complications of continuing to make model changes when the AI part of the system is highly interlinked with supporting clinical information systems, (4) by highlighting how this H2H effort supported broader Covid-19 response efforts across Singapore\'s public healthcare system, and finally (5) by commenting on how the experiences and related capabilities acquired from running this H2H program and related community care model and supporting AI prediction model are expected to contribute to the next wave of Singapore\'s public healthcare efforts from 2023 onwards. For the convenience of the reader, some content that introduces the H2H program and the multiple readmissions AI prediction model that previously appeared in the prior Healthcare Science publication is repeated at the beginning of this article.

Inefficient patient transport in hospitals often leads to delays, overworked staff, and suboptimal resource utilization, ultimately impacting patient care. Existing dispatch management algorithms are often evaluated in simulation environments, raising concerns about their real-world applicability. This study presents a real-world experiment that bridges the gap between theoretical dispatch algorithms and real-world implementation. It applies process capability analysis at Taichung Veterans General Hospital in Taichung, Taiwan, and utilizes IoT for real-time tracking of staff and medical devices to address challenges associated with manual dispatch processes. Experimental data collected from the hospital underwent statistical evaluation between January 2021 and December 2021. The results of our experiment, which compared the use of traditional dispatch methods with the Beacon dispatch method, found that traditional dispatch had an overtime delay of 41.0%; in comparison, the Beacon dispatch method had an overtime delay of 26.5%. These findings demonstrate the transformative potential of this solution for not only hospital operations but also for improving service quality across the healthcare industry in the context of smart hospitals.