Background: As familiarity with and convenience of using personal devices in hospitals help improve the productivity, efficiency, and workflow of hospital staff, the health care bring-your-own-device (BYOD) market is growing consistently. However, security concerns owing to the lack of control over the personal mobile devices of staff, which may contain sensitive data such as personal health information of patients, make it one of the biggest health care information technology (IT) challenges for hospital administrations. Objective: Given that the hospital BYOD security has not been adequately addressed in peer-reviewed literature, the aim of this paper was to identify key security challenges associated with hospital BYOD usage as well as relevant solutions that can cater to the identified issues by reviewing gray literature. Therefore, this research will provide additional practical insights from current BYOD practices. Methods: A comprehensive gray literature review was conducted, which followed the stepwise guidelines and quality assessment criteria set out by Garousi et al. The searched literature included tier 1 sources such as health care cybersecurity market reports, white papers, guidelines, policies, and frameworks as well as tier 2 sources such as credible and reputed health IT magazines, databases, and news articles. Moreover, a deductive thematic analysis was conducted to organize the findings based on Schlarman’s People Policy Technology model, promoting a holistic understanding of hospitals’ BYOD security issues and solutions. Results: A total of 51 sources were found to match the designed eligibility criteria. From these studies, several sociotechnical issues were identified. The major challenges identified were the use of devices with insufficient security controls by hospital staff, lack of control or visibility for the management to maintain security requirements, lack of awareness among hospital staff, lack of direction or guidance for BYOD usage, poor user experience, maintenance of legal requirements, shortage of cybersecurity skills, and loss of devices. Although technologies such as mobile device management, unified endpoint management, containerization, and virtual private network allow better BYOD security management in hospitals, policies and people management measures such as strong security culture and staff awareness and training improve staff commitment in protecting hospital data. Conclusions: The findings suggest that to optimize BYOD security management in hospitals, all 3 dimensions of the security process (people, policy, and technology) need to be given equal emphasis. As the nature of cybersecurity attacks is becoming more complex, all dimensions should work in close alignment with each other. This means that with the modernization of BYOD technology, BYOD strategy, governance, education, and relevant policies and procedures also need to adapt accordingly.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: The use of artificial intelligence (AI) in health care has been steadily increasing for over 2 decades. Integrating AI into neonatal intensive care units (NICUs) has promise as it has the potential to reshape neonatal care and improve outcomes. However, challenges such as data quality, clinical interpretation, and ethical considerations may hinder AI’s practical implementation in NICUs. Objective: This study aims (1) to analyze the current AI research landscape for predicting clinical outcomes and length of stay in the NICU and (2) to explore the benefits and challenges of using AI in the NICU for these predictions. Methods: A systematic review was conducted across 6 databases—PubMed, Embase, CINAHL, Cochrane Library, Informit, and La Trobe Library—to identify English-language peer-reviewed articles published between January 2017 and March 2023 that focused on the use of AI for predicting length of stay and clinical outcomes for NICU patients. Eligibility criteria excluded studies outside the NICU context or lacking predictive focus. Both prospective and retrospective designs were included. A thematic analysis of AI applications in NICUs from the articles identified was conducted. Results: A total of 24 studies were included in the review, comprising 15 retrospective and 9 prospective designs. These studies primarily originated from the United States (13 studies), with others from Austria, Taiwan, and other countries. The studies evaluated AI applications in NICU settings to predict comorbidities (18/24), mortality (4/24), and length of stay (2/24). Sixteen studies were in the exploration stage, lacking cohesive AI strategies, while 8 demonstrated systematic exploration but no fully integrated solutions. The synthesis of results identified key applications of AI in NICU care, including data-driven insights and predictive models, advancements in medical imaging, improved risk stratification, and personalized neonatal care. AI showed promise in enhancing diagnostic accuracy and care planning, but significant challenges persist, such as data quality, model generalization, and ethical concerns. No studies reported a fully integrated AI ecosystem, highlighting the need for further research to bridge gaps and realize AI’s transformative potential in neonatal care. Conclusions: This review highlights the potential of AI in improving NICU care, particularly through predictive models, medical imaging, and personalized interventions. However, the evidence is limited by significant methodological variability, small sample sizes, risk of bias, and a lack of external validation in included studies. Many studies remain in exploratory phases without cohesive AI strategies or integration into clinical practice, limiting the practical applicability of findings. These results underscore the importance of addressing challenges such as data quality, model generalization, and ethical considerations to fully realize AI’s potential in neonatal care. Future research should focus on robust validation, comprehensive implementation strategies, and ethical frameworks to ensure AI's effective and responsible integration into NICU settings. Trial Registration:

Background: News of the impact of COVID-19 around the world delivered a brief opportunity for Australian health services to plan new ways of delivering care to large numbers of people while maintaining staff safety through greater physical separation. The rapid pivot to telemedicine and virtual care provided immediate and longer term benefits; however, such rapid-cycle development also created risks. Objective: The aim of this study was to understand the sociotechnical aspects of the rapid-cycle development of seven different COVID-19 virtual care tools, and to identify enablers, barriers, and risks at three health services in Victoria, Australia. Methods: A qualitative, embedded, multiple case study design was adopted. Researchers from three health services collaborated with university researchers who were independent from those health services to gather and analyze structured interview data from key people involved in either clinical or technical aspects of designing and deploying seven different virtual care tools. Results: The overall objectives of each health service reflected the international requirements for managing large numbers of patients safely but remotely and for protecting staff. However, the governance, digital maturity, and specific use cases at each institution shaped the methodology and specific outcomes required. Dependence on key individuals and their domain knowledge within an existing governance framework generally enabled rapid deployment, but sometimes posed barriers. Existing relationships with technical service developers enabled strong solutions, which in some cases were highly scalable. Conventional project methodologies such as steering committees, scope, budget control, tight functional specification, consumer engagement and codesign, universal accessibility, and postimplementation evaluation were ignored almost universally in this environment. Conclusions: These three health services took a variety of approaches to the rapid-cycle development of virtual care tools to meet their urgent needs for triaging and remote monitoring during the first year of the COVID-19 pandemic. Their experiences provided insights into many social and technical barriers and enablers to the development of virtual care tools. If these are addressed proactively, they will improve clinical governance and technical management of future virtual care. Some changes can be made within individual health services, while others entail health system policy reforms. Enhancing the environment for virtual care tool design and implementation now will yield returns not only during future health emergencies but also in many more routine care settings.