Heart failure (HF) continues to impact the population globally with increasing prevalence. While the pathophysiology of HF is quite complex, the dysregulation of the autonomic nervous system, as evident in heightened sympathetic activity, serves as an attractive pathophysiological target for newer therapies and HF. The degree of neurohormonal activation has been found to correlate to the severity of symptoms, decline in functional capacity, and mortality. Neuromodulation of the autonomic nervous system aims to restore the balance between sympathetic nervous system and the parasympathetic nervous system. Given that autonomic dysregulation plays a major role in the development and progression of HF, restoring this balance may potentially have an impact on the core pathophysiological mechanisms and various HF syndromes. Autonomic modulation has been proposed as a potential therapeutic strategy aimed at reduction of systemic inflammation. Such therapies, complementary to drug and device-based therapies may lead to improved patient outcomes and reduce disease burden. Most professional societies currently do not provide a clear recommendation on the use of neuromodulation techniques in HF. These include direct and indirect vagal nerve stimulation, spinal cord stimulation, baroreflex activation therapy, carotid sinus stimulation, aortic arch stimulation, splanchnic nerve modulation, cardiopulmonary nerve stimulation, and renal sympathetic nerve denervation. In this review, we provide a comprehensive overview of neuromodulation in HF.

OBJECTIVE: The evidence about the acceptability and effectiveness of innovative paediatric models of care for Type 1 diabetes is limited. To address this gap, we synthesised literature on implemented models of care, model components, outcomes, and determinants of implementation and sustainability.
METHODS: A systematic review was conducted and reported in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Database searches of Medline, CINAHL, EMBASE and Scopus were conducted. Empirical studies focused on Type 1 diabetes paediatric models of care, published from 2010 to 2022 in English were included.
RESULTS: Nineteen extant studies reported on models and their associations with health and psychosocial outcomes, patient engagement with healthcare, and healthcare costs. Thirteen studies described multidisciplinary teamwork, education and capacity building that supported self-care. Four studies involved shared decision making between providers and patients, and two discussed outreach support where technology was an enabler. Fourteen studies reported improvements in health outcomes (e.g. glycaemic control), mostly for models that included multidisciplinary teams, education, and capacity building (11 studies), outreach support or shared care (3 studies). Four studies reported improvements in quality of life, three reported increased satisfaction for patients and carers and, and one reported improved communication. Four of five studies describing shared care and decision-making reported improvements in quality of life, support and motivation. Outreach models reported no negative outcomes, however, accessing some models was limited by technological and cost barriers. Eight studies reported on model sustainability, but only half reported implementation determinants; none reported applying a theoretical framework to guide their research.
CONCLUSIONS: Some health and psychosocial benefits were associated with newer models. To address knowledge gaps about implementation determinants and model sustainability, longitudinal studies are needed to inform future adoption of innovative models of care for children with Type 1 diabetes.

BACKGROUND: The leaders of health care organizations are grappling with rising expenses and surging demands for health services. In response, they are increasingly embracing artificial intelligence (AI) technologies to improve patient care delivery, alleviate operational burdens, and efficiently improve health care safety and quality.
OBJECTIVE: In this paper, we map the current literature and synthesize insights on the role of leadership in driving AI transformation within health care organizations.
METHODS: We conducted a comprehensive search across several databases, including MEDLINE (via Ovid), PsycINFO (via Ovid), CINAHL (via EBSCO), Business Source Premier (via EBSCO), and Canadian Business & Current Affairs (via ProQuest), spanning articles published from 2015 to June 2023 discussing AI transformation within the health care sector. Specifically, we focused on empirical studies with a particular emphasis on leadership. We used an inductive, thematic analysis approach to qualitatively map the evidence. The findings were reported in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews) guidelines.
RESULTS: A comprehensive review of 2813 unique abstracts led to the retrieval of 97 full-text articles, with 22 included for detailed assessment. Our literature mapping reveals that successful AI integration within healthcare organizations requires leadership engagement across technological, strategic, operational, and organizational domains. Leaders must demonstrate a blend of technical expertise, adaptive strategies, and strong interpersonal skills to navigate the dynamic healthcare landscape shaped by complex regulatory, technological, and organizational factors.
CONCLUSIONS: In conclusion, leading AI transformation in healthcare requires a multidimensional approach, with leadership across technological, strategic, operational, and organizational domains. Organizations should implement a comprehensive leadership development strategy, including targeted training and cross-functional collaboration, to equip leaders with the skills needed for AI integration. Additionally, when upskilling or recruiting AI talent, priority should be given to individuals with a strong mix of technical expertise, adaptive capacity, and interpersonal acumen, enabling them to navigate the unique complexities of the healthcare environment.

BACKGROUND: In an era marked by rapid technological advancements, changing demographics, and evolving healthcare needs, the landscape of health services has been undergoing a profound transformation. Innovation has emerged as a central force driving change in the healthcare sector, as stakeholders across the globe strive to enhance the quality, accessibility, and efficiency of healthcare services.
OBJECTIVE: Within this dynamic context, this systematic literature review explored the barriers and driving forces behind successful health service innovation.
METHODS: A comprehensive systematic literature review was conducted using the Griffith University Library search engine and databases that included PubMed, ProQuest, Web of Science, Scopus, and CINHAL. To achieve the study goal, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and the associated PRISMA checklist guided the review and reporting method.
RESULTS: Findings from this review identified a need for a universal definition of health innovation that encompasses the unique complexities and challenges within this context. In our comprehensive analysis of healthcare innovation, we have uncovered pivotal findings that underscore the indispensable nature of a well-structured framework.
CONCLUSIONS: To succeed in fostering innovation within the health and social care sectors, it is imperative to establish an overarching organisational culture that meticulously addresses the following key components: team challenges; communication and collaboration; governance goals and authentic leadership, environmental engagement; and innovation endurance. Through systematic analysis of existing literature, this review offers a definition of health innovation, covering its conceptual foundations, determinants, and barriers, and provides a framework for creating an innovative culture.

Robotic Roux-en-Y gastric bypass (RRYGB) is an innovative alternative to traditional laparoscopic approaches. Literature has been published investigating its safety/efficacy; however, the quality of reporting is uncertain. This systematic review used the Idea, Development, Exploration, Assessment and Long-term follow-up (IDEAL) framework to assess the reporting quality of available literature. A narrative summary was formulated, assessing how comprehensively governance/ethics, patient selection, demographics, surgeon expertise/training, technique description and outcomes were reported. Forty-seven studies published between 2005 and 2024 were included. There was incomplete/inconsistent reporting of governance/ethics, patient selection, surgeon expertise/training and technique description, with heterogenous outcome reporting. RRYGB reporting was poor and did not align with IDEAL guidance. Robust prospective studies reporting findings using IDEAL/other guidance are required to facilitate safe widespread adoption of RRYGB and other surgical innovations.

BACKGROUND: The world has witnessed increased adoption of large language models (LLMs) in the last year. Although the products developed using LLMs have the potential to solve accessibility and efficiency problems in health care, there is a lack of available guidelines for developing LLMs for health care, especially for medical education.
OBJECTIVE: The aim of this study was to identify and prioritize the enablers for developing successful LLMs for medical education. We further evaluated the relationships among these identified enablers.
METHODS: A narrative review of the extant literature was first performed to identify the key enablers for LLM development. We additionally gathered the opinions of LLM users to determine the relative importance of these enablers using an analytical hierarchy process (AHP), which is a multicriteria decision-making method. Further, total interpretive structural modeling (TISM) was used to analyze the perspectives of product developers and ascertain the relationships and hierarchy among these enablers. Finally, the cross-impact matrix-based multiplication applied to a classification (MICMAC) approach was used to determine the relative driving and dependence powers of these enablers. A nonprobabilistic purposive sampling approach was used for recruitment of focus groups.
RESULTS: The AHP demonstrated that the most important enabler for LLMs was credibility, with a priority weight of 0.37, followed by accountability (0.27642) and fairness (0.10572). In contrast, usability, with a priority weight of 0.04, showed negligible importance. The results of TISM concurred with the findings of the AHP. The only striking difference between expert perspectives and user preference evaluation was that the product developers indicated that cost has the least importance as a potential enabler. The MICMAC analysis suggested that cost has a strong influence on other enablers. The inputs of the focus group were found to be reliable, with a consistency ratio less than 0.1 (0.084).
CONCLUSIONS: This study is the first to identify, prioritize, and analyze the relationships of enablers of effective LLMs for medical education. Based on the results of this study, we developed a comprehendible prescriptive framework, named CUC-FATE (Cost, Usability, Credibility, Fairness, Accountability, Transparency, and Explainability), for evaluating the enablers of LLMs in medical education. The study findings are useful for health care professionals, health technology experts, medical technology regulators, and policy makers.

BACKGROUND: In the rush to develop health technologies for the COVID-19 pandemic, the unintended consequence of digital health inequity or the inability of priority communities to access, use, and receive equal benefits from digital health technologies was not well examined.
OBJECTIVE: This scoping review will examine tools and approaches that can be used during digital technology innovation to improve equitable inclusion of priority communities in the development of digital health technologies. The results from this study will provide actionable insights for professionals in health care, health informatics, digital health, and technology development to proactively center equity during innovation.
METHODS: Based on the Arksey and O\'Malley framework, this scoping review will consider priority communities\' equitable involvement in digital technology innovation. Bibliographic databases in health, medicine, computing, and information sciences will be searched. Retrieved citations will be double screened against the inclusion and exclusion criteria using Covidence (Veritas Health Innovation). Data will be charted using a tailored extraction tool and mapped to a digital health innovation pathway defined by the Centre for eHealth Research roadmap for eHealth technologies. An accompanying narrative synthesis will describe the outcomes in relation to the review\'s objectives.
RESULTS: This scoping review is currently in progress. The search of databases and other sources returned a total of 4868 records. After the initial screening of titles and abstracts, 426 studies are undergoing dual full-text review. We are aiming to complete the full-text review stage by May 30, 2024, data extraction in October 2024, and subsequent synthesis in December 2024. Funding was received on October 1, 2023, from the Centre for Health Equity Incubator Grant Scheme, University of Melbourne, Australia.
CONCLUSIONS: This paper will identify and recommend a series of validated tools and approaches that can be used by health care stakeholders and IT developers to produce equitable digital health technology across the Centre for eHealth Research roadmap. Identified evidence gaps, possible implications, and further research will be discussed.
UNASSIGNED: DERR1-10.2196/53855.

BACKGROUND: While the concept of hacking in education has gained traction in recent years, there is still much uncertainty surrounding this approach. As such, this scoping review seeks to provide a detailed overview of the existing literature on hacking in health profession education and to explore what we know (and do not know) about this emerging trend.
METHODS: This was a scoping review study using specific keywords conducted on 8 databases (PubMed, Embase, Scopus, Web of Science, ERIC, PsycINFO, Education Source, CINAHL) with no time limitation. To find additional relevant studies, we conducted a forward and backward searching strategy by checking the reference lists and citations of the included articles. Studies reporting the concept and application of hacking in education and those articles published in English were included. Titles, abstracts, and full texts were screened and the data were extracted by 2 authors.
RESULTS: Twenty-two articles were included. The findings are organized into two main categories, including (a) a Description of the interventions and expected outcomes and (b) Aspects of hacking in health profession education.
CONCLUSIONS: Hacking in health profession education refers to a positive application that has not been explored before as discovering creative and innovative solutions to enhance teaching and learning. This includes implementing new instructional methods, fostering collaboration, and critical thinking to utilize unconventional approaches.

OBJECTIVE: The integration of AI and natural language processing technologies, such as ChatGPT, into surgical practice has shown promising potential in enhancing various aspects of abdominopelvic surgical procedures. This systematic review aims to comprehensively evaluate the current state of research on the applications and impact of artificial intelligence (AI) and ChatGPT in abdominopelvic surgery summarizing existing literature towards providing a comprehensive overview of the diverse applications, effectiveness, challenges, and future directions of these innovative technologies.
METHODS: A systematic search of major electronic databases, including PubMed, Google Scholar, Cochrane Library, Web of Science, was conducted from October to November 2023, to identify relevant studies. Inclusion criteria encompassed studies that investigated the utilization of AI and ChatGPT in abdominopelvic surgical settings, including, but not limited to preoperative planning, intraoperative decision-making, postoperative care, and patient communication.
RESULTS: Fourteen studies met the inclusion criteria and were included in this review. The majority of the studies were analysing ChatGPT\'s data output and decision making while two studies reported patient and general surgery resident perception of the tool applied to clinical practice. Most studies reported a high accuracy of ChatGPT in data output and decision-making process, however with an unforgettable number of errors.
CONCLUSIONS: This systematic review contributes to the current understanding of the role of AI and ChatGPT in abdominopelvic surgery, providing insight into their applications and impact on clinical practice. The synthesis of available evidence will inform future research directions, clinical guidelines, and development of these technologies to optimize their potential benefits in enhancing surgical care within the abdominopelvic domain.

Knowledge Translation (KT) aims to convey novel ideas to relevant stakeholders, motivating their response or action to improve people\'s health. Initially, the KT literature focused on evidence-based medicine, applying findings from laboratory and clinical research to disease diagnosis and treatment. Since the early 2000s, the scope of KT has expanded to include decision-making with health policy implications.This systematic scoping review aims to assess the evolving knowledge-to-policy concepts, that is, macro-level KT theories, models and frameworks (KT TMFs). While significant attention has been devoted to transferring knowledge to healthcare settings (i.e. implementing health policies, programmes or measures at the meso-level), the definition of \'context\' in the realm of health policymaking at the macro-level remains underexplored in the KT literature. This study aims to close the gap.A total of 32 macro-level KT TMFs were identified, with only a limited subset of them offering detailed insights into contextual factors that matter in health policymaking. Notably, the majority of these studies prompt policy changes in low- and middle-income countries and received support from international organisations, the European Union, development agencies or philanthropic entities.