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Background: Since the 20th century, pursuing Universal Health Coverage (UHC) has emerged as an important developmental objective in numerous countries and across the global health community. With the intricate ramifications of population mobility (PM), the government faces a mounting imperative to judiciously deploy health expenditure to realise UHC effectively., Objective: This study aimed to construct a comprehensive UHC index for China, assess the spatial effects of Government Health Expenditure (GHE) on UHC, and explore the moderating effects of PM on this association., Method: A Dynamic Spatial Durbin Model (DSDM) was employed to investigate the influence of the GHE on UHC. Therefore, we tested the moderating effect of PM., Results: In the short-term, the GHE negatively impacted local UHC. However, it enhanced the UHC in neighbouring regions. Over the long term, GHE improved local UHC but decreased UHC in neighbouring regions. In the short-term, when the PM exceeded 1.42, the GHE increased the local UHC. Over the long term, when the PM exceeded 1.107, the GHE impeded local UHC. If the PM exceeded 0.91 in the long term, the GHE promoted UHC in neighbouring regions. The results of this study offer a partial explanation of GHE decisions and behaviours., Conclusions: To enhance UHC, a viable strategy involves augmenting vertical transfer payments from the central government to local governments. Local governments should institute healthcare systems tailored to the urban scale and developmental stages, with due consideration for PM. Optimising the information disclosure mechanism is also a worthwhile endeavour.

Background: Operational research (OR) is a process to improve health system capacity by evaluating interventions to improve health delivery and outcomes. The World Health Organization (WHO) Structured Operational Research Training Initiative (SORT-IT) programme promotes how OR contributes to improved health care delivery and health outcomes. A partnership project between the International Institute of Primary Health Care (IPHCE) in Ethiopia and The University of Queensland (UQ) in Australia modified the SORT-IT programme to deliver a hybrid Training of Trainers programme and improve OR capacity., Objective: This study was performed to develop and evaluate the effectiveness of Train-the Trainers approach in building capability to expand the capacity of the IPHCE to deliver the SORT-IT programme., Methods: Recruitment of participants and training were aligned with the principles of the SORT-IT programme. Training was face-to-face for the first session with subsequent training sessions delivered via Zoom over a 13-week period. Participants were required to complete all activities in line with SORT-IT deliverables. Slide decks supporting the SORT-IT training videos were developed and adapted to the Ethiopian context., Results: Participants had diverse experience from programme directors to research officers. All training sessions were recorded and available for participants to watch and review when required. All participants completed OR protocols to the draft stage. Course evaluation revealed participants found the content and format of the training useful, pertinent, and interesting., Conclusion: A hybrid model (face-to-face and video platform) for OR training was implemented. Managing contextual challenges such as information technology were managed easily by programme staff. Translating course requirements at a management level proved challenging with data collection for the protocols but provided insight into potential future challenges. This OR Training of Trainers course demonstrated that sharing of skills and knowledge can occur through a hybrid delivery model and contribute to developing capacity.

Mobile health has the potential to revolutionize health care delivery and patient engagement. In this work, we discuss how integrating Artificial Intelligence into digital health applications focused on supply chain operation, patient management, and capacity building, among other use cases, can improve the health system and public health performance. We present the Causal Foundry Artificial Intelligence and Reinforcement Learning platform, which allows the delivery of adaptive interventions whose impact can be optimized through experimentation and real-time monitoring. The system can integrate multiple data sources and digital health applications. The flexibility of this platform to connect to various mobile health applications and digital devices, and to send personalized recommendations based on past data and predictions, can significantly improve the impact of digital tools on health system outcomes. The potential for resource-poor settings, where the impact of this approach on health outcomes could be decisive, is discussed. This framework is similarly applicable to improving efficiency in health systems where scarcity is not an issue.

India launched the Ayushman Bharat Digital Mission (ABDM) in 2021 to strengthen the digital health ecosystem by developing and integrating health data records and registries. We apply the health system control knob framework to assess the progress of ABDM by analyzing five indicators. Data from the ABDM dashboard reveal notable progress in beneficiary registration (400 million, as of June 3, 2023) and health records linkage (273 million). The registrations of over 208,000 health facilities and 190,000 health care professionals have been verified by ABDM. However, inter-state variation in progress is significant, particularly in health facility and health professional registration. Going forward, ABDM should expand its strategic framework to ensure that more health facilities and health professionals are registered, as registration is important to influence the payment, organization, and regulation control knobs. These actions are related to the achievement of final health system goals: improved health status, financial risk protection, and beneficiary satisfaction.

This study proposes an approach for developing or improving performance management and measurement systems (PMMSs) for healthcare organisations. First, data is collected to analyse and understand the current organisation's performance management system. Second, the SWOT (Strengths, Weaknesses, Opportunities, Threats) method is used to identify the main aspects of the performance management system to be improved. Third, based on the scientific literature and SWOT analysis, BSC principles are integrated to this performance management system to better align the organisation's performance objectives and indicators with its strategy. Finally, we develop a performance indicator structure and select indicators to be used as well as how these indicators could be integrated and shared with higher hierarchical levels in the organisation by using AHP (Analytic Hierarchy Process). Our approach is applied to the CIUSSS du Centre-Sud-de-l'île-de-Montreal (CCSMTL), a large healthcare network, in the province of Québec, Canada., Competing Interests: No competing interests were disclosed., (Copyright: © 2024 Ben Fradj A et al.)

Background: The use of low-value care (LVC) is a persistent challenge in health care. Health technology reassessment (HTR) assesses the effects of technologies currently used in the health care system to guide optimal use of these technologies. Consequently, HTR holds promises for identifying and reducing, i.e., de-implementing, the use of LVC. There is limited research on how HTR is executed to support the de-implementation of LVC and whether and how HTR outcomes are translated into practical application. The aim of this study is to investigate how HTR is conducted to facilitate de-implementation of LVC and to investigate how the results of HTR are received and acted on in health care settings., Methods: This study is a qualitative interview study with representatives from health technology assessment agencies (n = 16) that support the regional health care organizations in Sweden and with representatives from the health care organizations (n = 7). Interviews were analysed with qualitative content analysis., Results: We identified three overarching categories for how HTR facilitates de-implementation of LVC and how the results are received and acted on in health care settings: (1) involving key stakeholders to facilitate de-implementation of LVC in identifying potential LVC practices, having criteria for accepting HTR targets, ascertaining high-quality reports and disseminating the reports; (2) actions taken by health care organization to de-implement LVC by priority setting and decision-making, networking between health care organizations and monitoring changes in the use of LVC practices; and (3) sustaining use of LVC by not questioning continued use, continued funding of LVC and by creating opinion against de-implementation., Conclusions: Evidence is not enough to achieve de-implementation of LVC. This has made health technology assessment agencies and health care organizations widen the scope of HTR to encompass strategies to facilitate de-implementation, including involving key stakeholders in the HTR process and taking actions to support de-implementation. Despite these efforts, there can still be resistance to de-implementation of LVC in passive forms, involving continued use of the practice and more active resistance such as continued funding and opinion-making opposing de-implementation. Knowledge from implementation and de-implementation research can offer guidance in how to support the execution phase of HTR., Competing Interests: Declarations. Ethics approval and consent to participate: The study was reviewed by the Swedish Ethical Review Authority, which judged that approval was not required under Swedish national law [reference number 2019:02467]. All participants were treated in accordance with ethical guidelines. Written informed consent was obtained from all study participants. Consent for publication: Informed written consent was obtained from all study participants. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Purpose: Digital platforms are essential for fostering innovation in first-line healthcare. These platforms require openness, allowing external parties to utilize, enhance, or profit from them. Yet, knowledge about barriers to realizing platform openness is lacking. This research investigates the barriers to realizing platform openness in first-line healthcare., Method: This research employed a qualitative exploratory approach. We collected data through thirteen semi-structured interviews with platform experts, application developers, and healthcare practitioners. As a study setting, we focused on Dutch first-line healthcare. We then analyzed the data using thematic analysis., Result: We identify barriers in three main categories that hinder platform openness: technology-related (e.g., redundancy in development work), business-related (e.g., profit-maximizing strategy), and healthcare-related (e.g., reluctance to change)., Scientific Contribution: We contribute to the platform literature in medical informatics by being among the first to examine openness barriers that hinder platform-based innovation. We thus explain why platform implementations often do not result in substantial improvements in healthcare delivery despite their transformative impact in other industries., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Food is Medicine (FIM) interventions reflect the critical links between food security, nutrition security, health, and health equity, integrated into health care delivery. They comprise programs that provide nutritionally tailored food, free of charge or at a discount, to support disease management, disease prevention, or optimal health, linked to the health care system as part of a patient's treatment plan. Such programs often prioritize health equity. On 26-27 April, 2023, Tufts University's Gerald J. and Dorothy R. Friedman School of Nutrition Science and Policy and Food & Nutrition Innovation Institute held a 2-day National Food is Medicine Summit with leaders, practitioners, and individuals with diverse lived experiences in health care, research, government, advocacy, philanthropy, and the private sector to identify challenges and opportunities to sustainably incorporate FIM services into the health care system and at scale. This report of a meeting describes key themes of the Summit, based on presentations and discussions on momentum around FIM, incorporating FIM in health care, tradeoffs and unintended consequences of various FIM models, scaling of programs, financing and payment mechanisms, educating and engaging the health care workforce, and federal and state government actions and opportunities on FIM. Speakers highlighted examples of recent public and private sector actions on FIM and innovative cross-sector partnerships, including state Medicaid waivers, academic and philanthropic research initiatives, health care system screenings and interventions, and collaborations including community-based organizations and/or entities outside of the food and health care sectors. Challenges and opportunities to broader implementation and scaling of FIM programs identified include incorporating FIM into health care business models, educating the health care workforce, and sustainably scaling FIM programs while leveraging the local connections of community-based organizations. This meeting report highlights recent advances, best practices, challenges, and opportunities discussed at the National Summit to inform future actions on FIM., Competing Interests: Conflict of interest The authors report no conflicts of interest., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Aims: Aging populations require adapting healthcare systems for older adult's specific needs. Numerous initiatives to improve older-patient care have emerged, but the field lacks a unified framework. The current study aims to provide a systematic concept analysis of 'age-friendly healthcare', examining its characteristics, components and structure., Design: Rodger's evolutionary concept analysis., Data Sources: Searches were conducted in ProQuest, CINAHL, PubMed and Scopus databases between November 2022 and October 2023, utilising the PRISMA 2020 reporting checklist., Methods: A literature search using specific terms relevant to age-friendly healthcare retrieved 1407 articles. After screening for duplicates and relevance, 140 articles were examined for eligibility based on inclusion criteria for age-friendly care, language and full-text availability. Following full-text screening, 65 articles were included for data extraction by multiple researchers to synthesise theoretical, methodological and design elements., Results: Our findings highlight key attributes of age-friendly healthcare: Respect for older adults' autonomy and needs; leadership and organisational knowledge and support; Proactive policies and processes of care; holistic care environments; and communication and follow-up with awareness of challenges and barriers as well as prioritisation of continuity-of-care., Conclusion: The concept of age-friendly healthcare is still developing, with much research focused on development and implementation rather than evaluation of real-world patient and health-system outcomes. Our analysis of the concept may help unify the field and clarify future research directions through identification of areas requiring further study and enable development of improved practices and policies for implementing age-friendly healthcare in a variety of settings., No Patient or Public Contribution: This concept analysis did not include any patient or public involvement., Reporting Method: This study utilised the PRISMA reporting checklist., (© 2024 The Author(s). Journal of Clinical Nursing published by John Wiley & Sons Ltd.)

Purpose: Multiple knowledge sources inform healthcare. In healthcare encounters, patients and health professionals' ideas intersect to understand illness and disease. Exploring what is thought of as legitimate knowledge, and where those reflections come from is central to the process of improving and developing healthcare. Within this context, we aim to explore how knowledge about hand osteoarthritis (OA) is constructed and negotiated in clinical consultations., Methodology: The article is based on interviews with 21 patients and 14 health professionals in combination with observation in 16 clinical consultations. Reflexive thematic analysis was used to interpret the data., Results: We generated four themes from codes to tell an interpretive story about how hand OA meaning-making is "talked into being" in patient-provider encounters: from the dominant voice of health professionals, from patients as knowers in the chronic healthcare dialogue, from health professionals and patients constructing knowledge together and from the construction of knowledge in hybrid positions when patients are health professionals and health professionals have hand OA., Conclusion: New knowledge about hand OA is co-constructed in the situated context of the clinical encounter through a polyphony of voices-some of which are dominant, while others occupy the periphery-within and between the interactants in dialgue.

Purpose: To investigate opinions among healthcare stakeholders whether implementation of the N-of-1 trial approach in clinical practice is a feasible way to optimize evidence-based treatment results for unique patients., Methods: We interviewed clinicians, researchers, and a patient advocate ( n  = 13) with an interest in or experience with N-of-1 trials on the following topics: experience with N-of-1, measurement, validity and reliability, informally gathered data usability, and influence on physician-patient relationship. Interviews were analysed using qualitative, thematic analysis., Results: The N-of-1 approach has the potential to shift the current healthcare system towards embracing personalized medicine. However, its application in clinical practice carries significant challenges in terms of logistics, time investment and acceptability. New skills will be required from patients and healthcare providers, which may alter the patient-physician relationship. The rise of consumer technology enabling self-measurement may leverage the uptake of N-of-1 approaches in clinical practice., Conclusions: There is a strong belief that the N-of-1 approach has the potential to play a prominent role in transitioning the current healthcare system towards embracing personalized medicine. However, there are many barriers deeply ingrained in our healthcare system that hamper the uptake of the N-of-1 approach, making it momentarily only interesting for research purposes.

Purpose: The use of narration in healthcare has been accentuated as a response to the requested shift towards person-centred care. The notion of narrative relations refers to a process of involving several people in mutual and ongoing narrative exchange. This study aimed to explore how and where narrative relations may be adopted and enacted in everyday healthcare practice., Methods: The study has a qualitative, explorative design. Seven interprofessional focus group discussions with healthcare staff were prompted by vignettes. A multidisciplinary team of healthcare staff ( n  = 31) were recruited on a geriatric ward. Data were analysed using a constant comparative method., Results: A core theme shows how narrative relations are adopted and enacted both as part of an approved practice -the work procedures commonly approved as part of healthcare, and as a disregarded practice where covert but important narrative relations take place to support fundamental qualities of healthcare. Moreover, the findings consider arenas of healthcare practice where approved or disregarded practices are enacted in the clinic frontstage and the clinic backstage., Conclusions: Narrative relations may take place in different arenas of healthcare practice yet simultaneously become a cohesive force interconnecting those arenas and uphold continuity. Impeded narrative relations in one arena may have unintended consequences in another.

Inuit living in the northern region of Nunavik continue to experience significant health inequalities, which are rooted in colonialism that still have repercussions on their health-related perceptions and practices, including vaccination. This study aimed to explore the perceptions and determinants of routine vaccination among the Inuit of Nunavik by describing factors influencing vaccination decisions from the perspective of community members and health professionals. Semi-structured interviews focusing on the perception of vaccination and experience with vaccination and health services were conducted with 18 Inuit and 11 non-Inuit health professionals. Using the socio-ecological model, factors acting at the community and public policy (e.g. rumours and misinformation about vaccination, language barrier), organisational (e.g. complexity of the vaccination process, staff turnover, lack of specialised vaccination workers and interpreters), and intrapersonal and interpersonal (e.g. past experiences with vaccination, vaccine attitudes, social norms) levels were identified as having an impact on vaccination decisions. Improving vaccination coverage in Nunavik requires a more global reflection on how to improve and culturally adapt the health care and services offered to the Inuit population.

Background: Scaling is typically discussed as a way to amplify or expand a health innovation. However, there is limited knowledge about the specific techniques that can enhance access to or improve the quality of innovations, aiming to increase their positive impacts for the public good. We sought to identify, compare, and contrast scaling frameworks to advance the science and practice of scaling., Methods: Using a scoping review we asked: 1) What are the attributes of scaling frameworks for innovations that support health outcomes? and 2) What are the similarities and differences of these attributes? Inclusion criteria were 1) primary studies or review articles, 2) a primary focus on scaling innovations for health and social care, 3) articles that developed a framework, and 4) articles were concerned with a health outcome. Starting from an umbrella review, we identified relevant studies and extracted data about the characteristics of the articles, attributes of framework development, attributes of framework components, transferability, and the framework's underlying ethical lens. Grey literature was included through expert consultation. Data were summarized using frequencies and qualitative description., Results: From 94 potentially eligible articles, we identified 9 unique frameworks and included 4 additional frameworks from the grey literature, resulting in a total of 13 frameworks. Seven frameworks include a definition of scaling, and eight are designed for public health settings. Five of the frameworks were developed for the US/Canada/UK and Australia. Six of the lead authors' primary institutional affiliation are from North America. Framework developers involved diverse stakeholders in a number of ways to develop their framework. Eight frameworks were developed, but not yet tested or applied, while the remaining frameworks were in the process of being applied or had already been applied to cases. All frameworks use a consequentialist-utilitarian ethical lens. Lastly, a comparison between frameworks found in the grey or published literature show important differences., Conclusion: Much may be learned through further support for, and development of, scaling frameworks by primary authors affiliated with the Global South. Important aspects of framework development were identified, especially understanding the nuances of diverse stakeholder involvement in development., Competing Interests: Declarations. Ethics approval and consent to participate: Ethics approval and consent to participate are not required for this research as it is a scoping review and uses secondary data. Human Ethics and Consent to Participate declarations: not applicable. Consent for publication: Not applicable. Competing interests: Three of the authors (AE, MGI, RKDM) work for the International Development Research Centre whose scaling science framework was found in the parent umbrella review, and therefore included in this analysis., (© 2024. The Author(s).)

Digital health innovations have emerged globally as a transformative force for addressing health system challenges, particularly in resource-constrained settings. The COVID-19 pandemic underscored the critical importance of these innovations for enhancing public health. In South and Southeast Asia, a region known for its cultural diversity and complex health care landscape, digital health innovations present a dynamic interplay of challenges and opportunities. We advocate for ongoing research built into system development and an evidence-based strategy focusing on designing and scaling national digital health infrastructures combined with a vibrant ecosystem or "marketplace" of local experiments generating shared experience about what works in which settings. As the global digital health revolution unfolds, the perspectives drawn from South and Southeast Asia-including the importance of local partnerships-may provide valuable insights for shaping future strategies and informing similar initiatives in low- and middle-income countries, contributing to effective digital health strategies across diverse global health contexts., (©Siyan Yi, Esabelle Lo Yan Yam, Kochukoshy Cheruvettolil, Eleni Linos, Anshika Gupta, Latha Palaniappan, Nitya Rajeshuni, Kiran Gopal Vaska, Kevin Schulman, Karen N Eggleston. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 25.11.2024.)

Background: Formal evidence regarding the effects of sanctions on population health status and the health system is scarse in Iran. Given the intricate and multifaceted nature of sanctions, a nuanced understanding of their impact is imperative. We aimed to investigate the magnitude and effects of sanctions on population health and healthcare system during the last two decades in Iran., Design: This is a mixed-methods research. We quantified the impact of sanctions using 28 indicators, i.e. macroeconomic, healthcare resources and health outcomes indicators from 2000 to 2020. The concurrent qualitative study aimed to explore the pathway of the effect by considering perceptions of both patients and health policy makers towards sanctions; Interview data was analyzed using content analysis., Setting: This study was conducted in Iran at both national and sub-national levels., Results: Our findings revealed that the trend of 11 indicators (39.2%) had changed after the change point in 2009; four indicators (14.2%) significantly deteriorated after the change point. Further, five indicators revealed significant increases during the sanctions period: Out-of-Pocket payment (OOP), household expenditure on food and mortality rates due to Chronic Obstructive Pulmonary Disease (COPD), thalassemia and hypertension. Our qualitative analysis revealed that patients identified availability, cost and quality of medicines and healthcare services as the most significant challenges that compromised population's health. From the policy makers' perspective, the effect of sanctions on health system functions and infrastructures, i.e. economic, political, social, educational and research had significant repercussions on population health., Conclusion: We could not find compelling evidence to establish significant associations between the imposition of sanctions and the trend of population health. Nevertheless, our qualitative study revealed people's deteriorating life experiences, e.g. increasing catastrophic health expenditure, limited access to necessary medicine, medical equipment, procedures and interventions, imposed by sanctions, with ultimate reducing impact on the overall quality of life. It seems that sanctions have negatively affected financial and physical access to medication and healthcare services. Nonetheless, Iran has demonstrated remarkable resilience against their highly detrimental effects, maybe due to its established economic infrastructure and healthcare system., Competing Interests: Declarations. Ethics approval and consent to participate: The ethics approval ID is: IR.NIMAD.REC.1399.111. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Background: Chatbots are increasingly integrated into the lives of older adults to assist with health and wellness tasks. This study aimed to understand the factors that enhance older adults' acceptance of chatbots in healthcare delivery., Methods: This study proposed an extended Unified Theory of Acceptance and Use of Technology model (UTAUT), including aging factors of perceived physical condition, self-actualization needs, and technology anxiety. The model was tested by PLS (Partial Least Squares) with data collected from 428 Chinese citizens aged 60 and above., Results: The results reveal that performance expectancy, effort expectancy, and social influence significantly affected older adults' behavioral intention to use chatbots. The facilitating conditions, self-actualization needs, and perceived physical condition significantly affected the actual use behavior of chatbots by older adults, whereas technology anxiety did not. Furthermore, the influence of effort expectancy and social influence on behavioral intention were moderated by experience., Conclusion: The behavioral intentions of older adults with low experience are more strongly influenced by social influences and effort expectancy. Furthermore, healthcare providers, designers, and policymakers should emphasize the impact of facilitating conditions, self-actualization needs, and perceived physical conditions on chatbot applications among older adults., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Yu and Chen.)

This brief commentary describes the reflections on a fundamental question by the Public Interest Group on Cancer Research, a successful academic-community partnership focused on cancer research, education, public engagement, and advocacy in Canada's Eastern province of Newfoundland and Labrador. Our Group has achieved some success in a short time with very limited funding. It has successfully created public spaces for conversations about cancer care and priorities for research and regularly advocated for health service change prioritized by input from patients and family members. However, we remain challenged in our understanding of how to truly implement change within oncology care contexts that is informed by patients and families affected by cancer. In this short reflection, we hope to raise awareness of this important issue and question whose responsibility it is to work with patients and families and follow through on prioritized healthcare issues and services. We suggest this may be a matter of integrated knowledge translation and a better understanding of where patients and families fit in this space. We hope to encourage reflection and conversation among all relevant stakeholders about how best to implement patient-prioritized change in oncology care and policy.

Background: Transformer-based language models have shown great potential to revolutionize health care by advancing clinical decision support, patient interaction, and disease prediction. However, despite their rapid development, the implementation of transformer-based language models in health care settings remains limited. This is partly due to the lack of a comprehensive review, which hinders a systematic understanding of their applications and limitations. Without clear guidelines and consolidated information, both researchers and physicians face difficulties in using these models effectively, resulting in inefficient research efforts and slow integration into clinical workflows., Objective: This scoping review addresses this gap by examining studies on medical transformer-based language models and categorizing them into 6 tasks: dialogue generation, question answering, summarization, text classification, sentiment analysis, and named entity recognition., Methods: We conducted a scoping review following the Cochrane scoping review protocol. A comprehensive literature search was performed across databases, including Google Scholar and PubMed, covering publications from January 2017 to September 2024. Studies involving transformer-derived models in medical tasks were included. Data were categorized into 6 key tasks., Results: Our key findings revealed both advancements and critical challenges in applying transformer-based models to health care tasks. For example, models like MedPIR involving dialogue generation show promise but face privacy and ethical concerns, while question-answering models like BioBERT improve accuracy but struggle with the complexity of medical terminology. The BioBERTSum summarization model aids clinicians by condensing medical texts but needs better handling of long sequences., Conclusions: This review attempted to provide a consolidated understanding of the role of transformer-based language models in health care and to guide future research directions. By addressing current challenges and exploring the potential for real-world applications, we envision significant improvements in health care informatics. Addressing the identified challenges and implementing proposed solutions can enable transformer-based language models to significantly improve health care delivery and patient outcomes. Our review provides valuable insights for future research and practical applications, setting the stage for transformative advancements in medical informatics., (©Ha Na Cho, Tae Joon Jun, Young-Hak Kim, Heejun Kang, Imjin Ahn, Hansle Gwon, Yunha Kim, Jiahn Seo, Heejung Choi, Minkyoung Kim, Jiye Han, Gaeun Kee, Seohyun Park, Soyoung Ko. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 18.11.2024.)

Artificial intelligence (AI) integration in healthcare has emerged as a transformative force, promising to enhance medical diagnosis, treatment, and overall healthcare delivery. Hence, this study investigates university students' perceptions of using AI in healthcare. A cross-sectional survey was conducted at two major universities using a paper-based questionnaire from September 2023 to November 2023. Participants' views regarding using artificial intelligence in healthcare were investigated using 25 items distributed across five domains. The Mann-Whitney U test was applied to compare variables. Two hundred seventy-nine (279) students completed the questionnaire. More than half of the participants (52%, n = 145) expressed their belief in AI's potential to reduce treatment errors. However, about (61.6%, n = 172) of participants fear the influence of AI that could prevent doctors from learning to make correct patient care judgments, and it was widely agreed (69%) that doctors should ultimately maintain final control over patient care. Participants with experience with AI, such as engaging with AI chatbots, significantly reported higher scores in both the "Benefits and Positivity Toward AI in Healthcare" and "Concerns and Fears" domains (p = 0.024) and (p = 0.026), respectively. The identified cautious optimism, concerns, and fears highlight the delicate balance required for successful AI integration. The findings emphasize the importance of addressing specific concerns, promoting positive experiences with AI, and establishing transparent communication channels. Insights from such research can guide the development of ethical frameworks, policies, and targeted interventions, fostering a harmonious integration of AI into the healthcare landscape in developing countries., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Large language models (LLMs) continue to exhibit noteworthy capabilities across a spectrum of areas, including emerging proficiencies across the health care continuum. Successful LLM implementation and adoption depend on digital readiness, modern infrastructure, a trained workforce, privacy, and an ethical regulatory landscape. These factors can vary significantly across health care ecosystems, dictating the choice of a particular LLM implementation pathway. This perspective discusses 3 LLM implementation pathways-training from scratch pathway (TSP), fine-tuned pathway (FTP), and out-of-the-box pathway (OBP)-as potential onboarding points for health systems while facilitating equitable adoption. The choice of a particular pathway is governed by needs as well as affordability. Therefore, the risks, benefits, and economics of these pathways across 4 major cloud service providers (Amazon, Microsoft, Google, and Oracle) are presented. While cost comparisons, such as on-demand and spot pricing across the cloud service providers for the 3 pathways, are presented for completeness, the usefulness of managed services and cloud enterprise tools is elucidated. Managed services can complement the traditional workforce and expertise, while enterprise tools, such as federated learning, can overcome sample size challenges when implementing LLMs using health care data. Of the 3 pathways, TSP is expected to be the most resource-intensive regarding infrastructure and workforce while providing maximum customization, enhanced transparency, and performance. Because TSP trains the LLM using enterprise health care data, it is expected to harness the digital signatures of the population served by the health care system with the potential to impact outcomes. The use of pretrained models in FTP is a limitation. It may impact its performance because the training data used in the pretrained model may have hidden bias and may not necessarily be health care-related. However, FTP provides a balance between customization, cost, and performance. While OBP can be rapidly deployed, it provides minimal customization and transparency without guaranteeing long-term availability. OBP may also present challenges in interfacing seamlessly with downstream applications in health care settings with variations in pricing and use over time. Lack of customization in OBP can significantly limit its ability to impact outcomes. Finally, potential applications of LLMs in health care, including conversational artificial intelligence, chatbots, summarization, and machine translation, are highlighted. While the 3 implementation pathways discussed in this perspective have the potential to facilitate equitable adoption and democratization of LLMs, transitions between them may be necessary as the needs of health systems evolve. Understanding the economics and trade-offs of these onboarding pathways can guide their strategic adoption and demonstrate value while impacting health care outcomes favorably., (©Radha Nagarajan, Midori Kondo, Franz Salas, Emre Sezgin, Yuan Yao, Vanessa Klotzman, Sandip A Godambe, Naqi Khan, Alfonso Limon, Graham Stephenson, Sharief Taraman, Nephi Walton, Louis Ehwerhemuepha, Jay Pandit, Deepti Pandita, Michael Weiss, Charles Golden, Adam Gold, John Henderson, Angela Shippy, Leo Anthony Celi, William R Hogan, Eric K Oermann, Terence Sanger, Steven Martel. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 14.11.2024.)

Background: Low-quality care for low back pain (LBP) is pervasive in Australia. Drivers of low-quality care have been identified elsewhere and include misconceptions about LBP, vested interests and limited funding for evidence-based interventions. Yet, the literature that identified such drivers is not specific to the Australian context, and therefore, it is likely to represent only part of the local problem. This study aimed to determine where the most influential drivers of LBP care are in the Australian healthcare system and what could be done to address them., Methods: Clinical leaders from various disciplines, academics, hospital managers, policy-makers, consumers involved in LBP advocacy, board members of relevant health profession boards and private insurers were invited to participate in one-on-one interviews. Interviews were transcribed verbatim. Interview data were analysed using content analysis., Results: We interviewed 37 stakeholders. Challenges that hinder LBP care in Australia included variability in care and inconsistent messages, funding models that are not supportive of appropriate care for LBP, the community's understanding of LBP, vested interests and commercial forces, difficulties in accessing timely and affordable conservative care, neglect of social determinants and health inequities, short consultations, siloed practices, uncertainties that stem from gaps in evidence and the experience of having LBP, individual and contextual variability, the mismatch between evidence and practice, the Australian healthcare system itself, the lack of political will and acknowledgement of LBP as a public health issue, stigma, the need to improve human aspects and the compensation system. When discussing factors that could improve LBP care, participants raised collaboration, changes in funding, improvement of access to - and affordability of - models of care and care pathways, public health campaigns targeting LBP, enhancement of policy and governance, increasing and better training the workforce, consideration of inequities, making improvements in information sharing and reforming the worker's compensation sector., Conclusions: LBP is a wicked problem, influenced by several systemic factors. An agenda for system change in the LBP landscape should be guided by a collaborative, coherent and integrated approach across sectors to enhance quality of care and system efficiency for those who seek and provide care., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Background: The launch of ChatGPT (OpenAI) in November 2022 attracted public attention and academic interest to large language models (LLMs), facilitating the emergence of many other innovative LLMs. These LLMs have been applied in various fields, including health care. Numerous studies have since been conducted regarding how to use state-of-the-art LLMs in health-related scenarios., Objective: This review aims to summarize applications of and concerns regarding conversational LLMs in health care and provide an agenda for future research in this field., Methods: We used PubMed, ACM, and the IEEE digital libraries as primary sources for this review. We followed the guidance of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) to screen and select peer-reviewed research articles that (1) were related to health care applications and conversational LLMs and (2) were published before September 1, 2023, the date when we started paper collection. We investigated these papers and classified them according to their applications and concerns., Results: Our search initially identified 820 papers according to targeted keywords, out of which 65 (7.9%) papers met our criteria and were included in the review. The most popular conversational LLM was ChatGPT (60/65, 92% of papers), followed by Bard (Google LLC; 1/65, 2% of papers), LLaMA (Meta; 1/65, 2% of papers), and other LLMs (6/65, 9% papers). These papers were classified into four categories of applications: (1) summarization, (2) medical knowledge inquiry, (3) prediction (eg, diagnosis, treatment recommendation, and drug synergy), and (4) administration (eg, documentation and information collection), and four categories of concerns: (1) reliability (eg, training data quality, accuracy, interpretability, and consistency in responses), (2) bias, (3) privacy, and (4) public acceptability. There were 49 (75%) papers using LLMs for either summarization or medical knowledge inquiry, or both, and there are 58 (89%) papers expressing concerns about either reliability or bias, or both. We found that conversational LLMs exhibited promising results in summarization and providing general medical knowledge to patients with a relatively high accuracy. However, conversational LLMs such as ChatGPT are not always able to provide reliable answers to complex health-related tasks (eg, diagnosis) that require specialized domain expertise. While bias or privacy issues are often noted as concerns, no experiments in our reviewed papers thoughtfully examined how conversational LLMs lead to these issues in health care research., Conclusions: Future studies should focus on improving the reliability of LLM applications in complex health-related tasks, as well as investigating the mechanisms of how LLM applications bring bias and privacy issues. Considering the vast accessibility of LLMs, legal, social, and technical efforts are all needed to address concerns about LLMs to promote, improve, and regularize the application of LLMs in health care., (©Leyao Wang, Zhiyu Wan, Congning Ni, Qingyuan Song, Yang Li, Ellen Clayton, Bradley Malin, Zhijun Yin. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 07.11.2024.)

Background: There is persistent pressure on countries with a high burden of HIV infection to reach desired targets for HIV treatment outcomes. This has led to moving from the "one-size-fits-all" model to differentiated service delivery (DSD) models, which are meant to be more patient-centered and efficient but without compromising on the quality of patient care. However, for DSD models to be efficient, facilities should have indicators of HIV services available and ready to provide the DSD models. We aimed to assess the availability of HIV service indicators and the readiness of facilities to provide DSD models for HIV treatment in selected public health facilities in Zambia., Methods: We conducted a nationwide cross-sectional survey among public health facilities in Zambia that provide antiretroviral therapy (ART) services. We used an interviewer-administered questionnaire based on a World Health Organization (WHO) Service Availability Readiness Assessment (SARA) tool to assess the availability of HIV service indicators and the readiness of facilities to implement DSD models for HIV treatment. Availability and readiness were considered latent constructs, and therefore, we used structural equation modeling (SEM) to determine the correlations between them and their respective indicators., Results: Of 60 public health ART facilities, the overall availability of HIV service indicators was 80.0% (48/60), and readiness to provide the DSD models was 81.7% (48/60). However, only 48 and 39% of the facilities had all indicators of availability and readiness, respectively. Retention in care for HIV multidisciplinary teams was more likely to occur in urban areas than in rural areas. SEM showed that the standardized estimate between availability and readiness was significantly and positively correlated ( r  = 0.73, p  < 0.0001). In addition, both availability and readiness were significantly and positively correlated with most of their respective indicators., Conclusion: Although most facilities had available HIV service indicators and were ready to provide DSD models, most facilities did not have all indicators of availability and readiness. In addition, there were differences between rural and urban facilities in some indicators. There is a need for persistent and heightened efforts meant to implement DSD in HIV treatment, especially in rural areas to accelerate reaching the desired HIV treatment outcomes., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Kaonga, Sampa, Musukuma, Mulawa, Mulavu, Sitali, Moonga, Mweemba, Matenga, Zyambo, Hamoonga, Phiri, Halwindi, Chavula, Zulu and Jacobs.)

Underreporting of adverse events in healthcare systems is a global concern. This study aims to address the underreporting of adverse events (AE) by implementing a TRIZ-based model to identify and overcome barriers to reporting, thus filling gaps in current reporting practices and improving incident recognition. A TRIZ (Theory of Inventive Problem Solving) approach was adopted, integrating with SERVQUAL methodologies to design interventions. Preintervention and postintervention surveys were conducted to evaluate changes in the recognition of adverse events and barriers to reporting. Statistical analyses were performed to assess the effectiveness of the interventions. Recognition improved and barriers to reporting AEs significantly decreased. Monthly reported cases rose from 33.7 to 50.3 (p = 0.000), demonstrating the effectiveness of the TRIZ-based interventions. Implementing a TRIZ-based model significantly improved adverse event reporting by enhancing the recognition of reportable events and overcoming identified barriers. Future research should explore the long-term sustainability of these interventions and their broader applicability in diverse healthcare settings., (© 2024. The Author(s).)

Health care and health security are the fundamental pillars of disaster preparedness and crisis management. An established routine health care is necessary for any society, enabling full access to care and fulfilling the rights of every individual. Health security, on the other hand, is what a society needs to be flexible in managing an unexpected situation. To overcome a disaster with minimal damage or to avert such a critical situation, health care and health security should exist simultaneously. Thus, resilience in disaster preparedness and crisis management requires investment in both health care and health security. This ensures local public health services and infrastructure, local ambulances, both acute and chronic care referral systems, prompt vaccinations, and prevention of communicable diseases to name but a few. These measures which have proven to be the most sensitive evaluation of fair governance are critically absent in several nations, particularly in areas with long-standing conflicts. Strengthening health care and health security measures are paramount to the maintenance of the health system in peace and recovery of health delivery post-conflict and require political and economic considerations.

Digital transformation, a term coined by Erik Stolterman and Anna Croon Fors, means the changes of social systems by using information and communication technologies. The healthcare industry has changed through the introduction of hospital information systems, and the Internet of Things is now dissolving hospitals in society. Recent advancements in medical artificial intelligence have increased expectations of disruptive changes in clinical practice and medical science. Therefore, all nations devote their efforts to the establishment of a key factor: a real-world data platform. A clear vision of gradually navigating citizens to a bright future and the courage to change the legislation quickly to realize the vision are keys to success.

Artificial intelligence (AI) has the potential to transform clinical practice and healthcare. Following impressive advancements in fields such as computer vision and medical imaging, AI is poised to drive changes in microbiome-based healthcare while facing challenges specific to the field. This review describes the state-of-the-art use of AI in microbiome-related healthcare. It points out limitations across topics such as data handling, AI modelling and safeguarding patient privacy. Furthermore, we indicate how these current shortcomings could be overcome in the future and discuss the influence and opportunities of increasingly complex data on microbiome-based healthcare., (© 2024 The Author(s). Microbial Biotechnology published by John Wiley & Sons Ltd.)

Introduction: Despite international commitment to achieving the end of HIV as a public health threat, progress is off-track and existing gaps have been exacerbated by COVID-19's collision with existing pandemics. Born out of models of political accountability and historical healthcare advocacy led by people living with HIV, community-led monitoring (CLM) of health service delivery holds potential as a social accountability model to increase the accessibility and quality of health systems. However, the effectiveness of the CLM model in strengthening accountability and improving service delivery relies on its alignment with evidence-based principles for social accountability mechanisms. We propose a set of unifying principles for CLM to support the impact on the quality and availability of health services., Discussion: Building on the social accountability literature, core CLM implementation principles are defined. CLM programmes include a community-led and independent data collection effort, in which the data tools and methodology are designed by service users and communities most vulnerable to, and most impacted by, service quality. Data are collected routinely, with an emphasis on prioritizing and protecting respondents, and are then be used to conduct routine and community-led advocacy, with the aim of increasing duty-bearer accountability to service users. CLM efforts should represent a broad and collective community response, led independently by impacted communities, incorporating both data collection and advocacy, and should be understood as a long-term approach to building meaningful engagement in systems-wide improvements rather than discrete interventions., Conclusions: The CLM model is an important social accountability mechanism for improving the responsiveness of critical health services and systems to communities. By establishing a collective understanding of CLM principles, this model paves the way for improved proliferation of CLM with fidelity of implementation approaches to core principles, rigorous examinations of CLM implementation approaches, impact assessments and evaluations of CLM's influence on service quality improvement., (© 2024 The Author(s). Journal of the International AIDS Society published by John Wiley & Sons Ltd on behalf of International AIDS Society.)

Context: Staff in Canadian Emergency Departments (EDs) face increasing workplace demands arising in part from system-wide shortages in primary and community care. Patients experiencing stigmatizing conditions such as chronic pain, substance use, and psychiatric disorders may turn to the ED as the only open "door" to access care in the community. Objective: To examine staff perceptions about their work and role, including how they may be prepared or not to address issues of health and health care inequities in EDs. Study Design and Analysis: Paper and online surveys were administered to staff. Data were collected as part of a larger mixed-methods organization-level intervention study aimed at enhancing capacity to provide equity-oriented health care in EDs. Pooled, cross-sectional survey data (n=393) were analyzed to examine work experiences, team effectiveness, and domains of equity- oriented care. Descriptive results from survey data are complemented by illustrative excerpts from qualitative interviews conducted with staff and administrative leadership. Setting: Three EDs in British Columbia. Population Studied: All staff working in EDs participating in the EQUIP ED study. Intervention: N/a. Outcome Measures: N/a. Results: Levels of stress, the extent of control over work, sufficient time to complete work, receive feedback about work, and ratings of the quality of care provided in the ED highlight workplace pressures, constraints, and impact on staff. The proportion of staff who have received training on and have confidence in being able to provide equity-oriented care in the ED has implications for how staff may be able to contribute to improved care for patients already experiencing the harmful effects of stigma and other forms of discrimination including fragmentation of care. Conclusions: The impacts of shortages in primary care reverberate throughout the system. EDs often prioritize efficiency, a focus which cannot adequately equip staff to promote equity. Attention and commitment to addressing structural inequities within EDs could support patients and staff to move closer to the quintuple aim., Competing Interests: Authors report none., (2023 Annals of Family Medicine, Inc.)

There is a vast development of artificial intelligence (AI) in recent years. Computational technology, digitized data collection and enormous advancement in this field have allowed AI applications to penetrate the core human area of specialization. In this review article, we describe current progress achieved in the AI field highlighting constraints on smooth development in the field of medical AI sector, with discussion of its implementation in healthcare from a commercial, regulatory and sociological standpoint. Utilizing sizable multidimensional biological datasets that contain individual heterogeneity in genomes, functionality and milieu, precision medicine strives to create and optimize approaches for diagnosis, treatment methods and assessment. With the arise of complexity and expansion of data in the health-care industry, AI can be applied more frequently. The main application categories include indications for diagnosis and therapy, patient involvement and commitment and administrative tasks. There has recently been a sharp rise in interest in medical AI applications due to developments in AI software and technology, particularly in deep learning algorithms and in artificial neural network (ANN). In this overview, we enlisted the major categories of issues that AI systems are ideally equipped to resolve followed by clinical diagnostic tasks. It also includes a discussion of the future potential of AI, particularly for risk prediction in complex diseases, and the difficulties, constraints and biases that must be meticulously addressed for the effective delivery of AI in the health-care sector.

The rapid advancement of artificial intelligence (AI) in healthcare has revolutionized the industry, offering significant improvements in diagnostic accuracy, efficiency, and patient outcomes. However, the increasing adoption of AI systems also raises concerns about their environmental impact, particularly in the context of climate change. This review explores the intersection of climate change and AI in healthcare, examining the challenges posed by the energy consumption and carbon footprint of AI systems, as well as the potential solutions to mitigate their environmental impact. The review highlights the energy-intensive nature of AI model training and deployment, the contribution of data centers to greenhouse gas emissions, and the generation of electronic waste. To address these challenges, the development of energy-efficient AI models, the adoption of green computing practices, and the integration of renewable energy sources are discussed as potential solutions. The review also emphasizes the role of AI in optimizing healthcare workflows, reducing resource waste, and facilitating sustainable practices such as telemedicine. Furthermore, the importance of policy and governance frameworks, global initiatives, and collaborative efforts in promoting sustainable AI practices in healthcare is explored. The review concludes by outlining best practices for sustainable AI deployment, including eco-design, lifecycle assessment, responsible data management, and continuous monitoring and improvement. As the healthcare industry continues to embrace AI technologies, prioritizing sustainability and environmental responsibility is crucial to ensure that the benefits of AI are realized while actively contributing to the preservation of our planet., Competing Interests: Declaration of competing interest The authors have no competing interests to disclose in relation with this article., (Copyright © 2024 The Author(s). Published by Elsevier Masson SAS.. All rights reserved.)

Background: Advanced health technologies that emerge with the development of technology have an impact on health systems. This study aimed to determine the effects of these technologies on Türkiye's health system and present policy recommendations to reshape Türkiye's health system and policies accordingly., Methods: Interviews were conducted with senior managers, bureaucrats, policy-makers and decision-makers from seven different institutions on the subject. Content analysis was performed on the data obtained and evaluative categories were established., Results: It was concluded that these technologies would not have a positive impact on two identified themes, a negative impact on seven themes and a predominant impact on five themes in Türkiye., Conclusions: To adapt to the new health ecosystem in Türkiye, it is recommended to increase digital literacy, conduct economic evaluations of technologies, promote domestic production, ensure up-to-date follow-up, collaborate with the engineering field, enhance health technology evaluation practices, improve access to technologies and ensure that the infrastructures of health institutions are compatible with technologies. Various policy suggestions have been presented for the development of Türkiye's health system., (© 2024. The Author(s).)

As extreme weather events increase in frequency and intensity, the health system faces significant challenges, not only from shifting patterns of climate-sensitive diseases but also from disruptions to healthcare infrastructure, supply chains and the physical systems essential for delivering care. This necessitates the strategic use of geospatial tools to guide the delivery of healthcare services and make evidence-informed priorities, especially in contexts with scarce human and financial resources. In this article, we highlight several published papers that have been used throughout the phases of the disaster management cycle in relation to health service delivery. We complement the findings from these publications with a rapid scoping review to present the body of knowledge for using spatial methods for health service delivery in the context of disasters. The main aim of this article is to demonstrate the benefits and discuss the challenges associated with the use of geospatial methods throughout the disaster management cycle. Our scoping review identified 48 articles employing geospatial techniques in the disaster management cycle. Most of them focused on geospatial tools employed for preparedness, anticipatory action and mitigation, particularly for targeted health service delivery. We note that while geospatial data analytics are effectively deployed throughout the different phases of disaster management, important challenges remain, such as ensuring timely availability of geospatial data during disasters, developing standardized and structured data formats, securing pre-disaster data for disaster preparedness, addressing gaps in health incidence data, reducing underreporting of cases and overcoming limitations in spatial and temporal coverage and granularity. Overall, existing and novel geospatial methods can bridge specific evidence gaps in all phases of the disaster management cycle. Improvement and 'operationalization' of these methods can provide opportunities for more evidence-informed decision making in responding to health crises during climate change.

There have recently been rapid developments in smart healthcare systems, such as precision diagnosis, smart diet management, and drug discovery. These systems require the integration of the Internet of Things (IoT) for data acquisition, Digital Twins (DT) for data representation into a digital replica and Artificial Intelligence (AI) for decision-making. DT is a digital copy or replica of physical entities (e.g., patients), one of the emerging technologies that enable the advancement of smart healthcare systems. AI and Machine Learning (ML) offer great benefits to DT-based smart healthcare systems. They also pose certain risks, including security risks, and bring up issues of fairness, trustworthiness, explainability, and interpretability. One of the challenges that still make the full adaptation of AI/ML in healthcare questionable is the explainability of AI (XAI) and interpretability of ML (IML). Although the study of the explainability and interpretability of AI/ML is now a trend, there is a lack of research on the security of XAI-enabled DT for smart healthcare systems. Existing studies limit their focus to either the security of XAI or DT. This paper provides a brief overview of the research on the security of XAI-enabled DT for smart healthcare systems. It also explores potential adversarial attacks against XAI-enabled DT for smart healthcare systems. Additionally, it proposes a framework for designing XAI-enabled DT for smart healthcare systems that are secure and trusted.

Musculoskeletal (MSK) conditions, particularly shoulders, knees, and the low back issues, place a significant burden on individuals, society, and healthcare systems. There is a lack of attention to negative health effects impacting patients because of their interactions to access appropriate diagnostics, assessments, and treatments. This scoping review intends to search and synthesize peer-reviewed evidence on the negative health impacts associated with navigating the healthcare system for MSK care. A scoping review will be conducted following the PRISMA guidelines for Scoping Reviews and Arksey and O'Malley's 5-step process. Six databases will be searched with no time or geographic limits. Included articles must meet all the following criteria: 1) the patients must be adults, 2) patients must be seeking care for their knee, low-back, or shoulder condition, 3) interacted with the healthcare system, and 4) experienced health impacts due to navigating the healthcare system. Information from each article will be charted in a pre-determined extraction. This protocol aims to share our methods ahead of analysis to increase rigour and transparency. The scoping review results will better elucidate the health impacts of the inaccessibility of high-quality care for MSK conditions. The findings also aim to inform the development of patient-centered outcomes to evaluate alterations to the current MSK pathways., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Jessiman-Perreault et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Due to the weaknesses of the public health system and its low reach, especially in border areas, provision of health services by non-state actors (NSAs) has historically played an important role in Myanmar. NSAs include local and international NGOs and civil society organisations (CSOs), but also Ethnic Health Organisations (EHOs) in the border areas, as well as the private (for profit) sector. This study aims to understand the changing role of NSAs in the shifting political environment of Myanmar between 2010 and 2022, and to explore their contribution to health system resilience., Methods: Our study includes three main components: a documentary review (n = 22), key informant interviews (KIIs) at central level (n = 14) and two township-level case studies (13 KIIs, 4 FGDs). Mostly qualitative data were collected in 2022 and synthesized, using a health system resilience framework to structure the analysis., Results: During the transition period (2010-2014) and the new political era (2015-2020), while the country gradually transitioned to a democratic system, the government increasingly recognized NSAs. Initially, engagement with NSAs remained focused on disease-specific activities and government oversight was limited, but later it expanded to health system strengthening, including the start of a "convergence" with ethnic health systems. Progress was relatively slow, but defined by a clear vision and plans. The military coup of February 2021 brought a halt to this progress. Collaboration between government and NSAs was interrupted, and NSAs restored previous practices and parallel systems. Initially, most health service provision stopped, but with time coping strategies emerged, which showed the capacity of NSAs to absorb the shocks (focusing on basic services; using informal communication channels; maintaining buffer stocks of supplies) and adapt (changing modes of delivery and supply chains, and adjusting HRH training)., Conclusions: The study highlights the role of NSAs during crises, and provides insights on how the resilience capacities built over time by NSAs to provide services in adverse circumstances have informed the response to the latest crisis. While strategies of absorption and adaptation are noted in the study, we did not identify any transformation strategy - which might indicate the difficulty of NSAs to introduce radical changes when subjected to multiple shocks and a hostile political environment., (© 2024. The Author(s).)

Objective: The objective of this review was to develop a taxonomy of pressures experienced by health services and an accompanying taxonomy of strategies for adapting in response to these pressures. The taxonomies were developed from a review of observational studies directly assessing care delivered in a variety of clinical environments., Design: In the first phase, a scoping review of the relevant literature was conducted. In the second phase, pressures and strategies were systematically coded from the included papers, and categorised., Data Sources: Electronic databases (MEDLINE, Embase, CINAHL, PsycInfo and Scopus) and reference lists from recent reviews of the resilient healthcare literature., Eligibility Criteria: Studies were included from the resilient healthcare literature, which used descriptive methodologies to directly assess a clinical environment. The studies were required to contain strategies for managing under pressure., Results: 5402 potential articles were identified with 17 papers meeting the inclusion criteria. The principal source of pressure described in the studies was the demand for care exceeding capacity (ie, the resources available), which in turn led to difficult working conditions and problems with system functioning. Strategies for responding to pressures were categorised into anticipatory and on-the-day adaptations. Anticipatory strategies included strategies for increasing resources, controlling demand and plans for managing the workload (efficiency strategies, forward planning, monitoring and co-ordination strategies and staff support initiatives). On-the-day adaptations were categorised into: flexing the use of existing resources, prioritising demand and adapting ways of working (leadership, teamwork and communication strategies)., Conclusions: The review has culminated in an empirically based taxonomy of pressures and an accompanying taxonomy of strategies for adapting in response to these pressures. The taxonomies could help clinicians and managers to optimise how they respond to pressures and may be used as the basis for training programmes and future research evaluating the impact of different strategies., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

The continuous evolution of the IoT paradigm has been extensively applied across various application domains, including air traffic control, education, healthcare, agriculture, transportation, smart home appliances, and others. Our primary focus revolves around exploring the applications of IoT, particularly within healthcare, where it assumes a pivotal role in facilitating secure and real-time remote patient-monitoring systems. This innovation aims to enhance the quality of service and ultimately improve people's lives. A key component in this ecosystem is the Healthcare Monitoring System (HMS), a technology-based framework designed to continuously monitor and manage patient and healthcare provider data in real time. This system integrates various components, such as software, medical devices, and processes, aimed at improvi1g patient care and supporting healthcare providers in making well-informed decisions. This fosters proactive healthcare management and enables timely interventions when needed. However, data transmission in these systems poses significant security threats during the transfer process, as malicious actors may attempt to breach security protocols.This jeopardizes the integrity of the Internet of Medical Things (IoMT) and ultimately endangers patient safety. Two feature sets-biometric and network flow metric-have been incorporated to enhance detection in healthcare systems. Another major challenge lies in the scarcity of publicly available balanced datasets for analyzing diverse IoMT attack patterns. To address this, the Auxiliary Classifier Generative Adversarial Network (ACGAN) was employed to generate synthetic samples that resemble minority class samples. ACGAN operates with two objectives: the discriminator differentiates between real and synthetic samples while also predicting the correct class labels. This dual functionality ensures that the discriminator learns detailed features for both tasks. Meanwhile, the generator produces high-quality samples that are classified as real by the discriminator and correctly labeled by the auxiliary classifier. The performance of this approach, evaluated using the IoMT dataset, consistently outperforms the existing baseline model across key metrics, including accuracy, precision, recall, F1-score, area under curve (AUC), and confusion matrix results.

Evaluating health system interventions for elderly care necessitates a context-specific, credible and dependable instrument. This research was dedicated to creating and validating a tool to assess the health system's age-friendliness. The study unfolded in two pivotal stages: the generation of items utilizing a hybrid model and the psychometric appraisal of the tool, encompassing both validity and reliability assessments. From an initial pool of 522 items derived from a systematic scoping review and qualitative analysis, a concise assessment tool emerged, featuring 52 items across 6 domains: governance, information, resources, service delivery, community engagement and outcomes. The 52-item tool offers a straightforward, substantiated and robust means to gauge age-friendliness, pinpoint health system deficiencies and facilitate strategic planning on the basis of its insights., (© 2024. The Author(s).)

Background: As a result of the recent advancements in technology, the incorporation of digital interventions into the health care system has gained a lot of attention and adoption globally. However, these interventions have not been fully adopted, thereby limiting their impact on health care delivery in West Africa., Objective: This review primarily aims at evaluating the current digital interventions for medication and health care delivery in West Africa. Its secondary aim is to assess the impacts of digital interventions in managing medication and health care service delivery with the intent of providing vital recommendations that would contribute to an excellent adoption of digital intervention tools in the health care space in West Africa., Methods: In line with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), a comprehensive search through various databases yielded 529 results. After a rigorous screening, 29 articles that provided information on 3 broad digital health intervention tools were found eligible for this review., Results: Out of 29 studies, 16 (55%) studies examined phone-based interventions, 9 (31%) studies focused on tele- and e-based interventions, and 4 (14%) studies evaluated digital interventions. These interventions were used for diverse purposes, some of which are monitoring adverse drug reactions, general health, sexual and reproductive health, and training of health care practitioners. The phone-based intervention appears to be the most known and impactful of all the interventions, followed by tele- and e-based, while digital interventions were scarcely used., Conclusions: Digital interventions have had a considerable level of impact on medication and health care delivery across West Africa. However, the overall impact is limited. Therefore, strategies must be developed to address the challenges limiting the use of digital intervention tools so that these tools can be fully incorporated into the health care space in West Africa., (©Emmanuel Oluwatosin Oluokun, Festus Fatai Adedoyin, Huseyin Dogan, Nan Jiang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 09.10.2024.)

Background: Botswana has made significant investments in its health care information infrastructure, including vertical programs for child health and nutrition, HIV care, and tuberculosis. However, effectively integrating the more than 18 systems in place for data collection and reporting has proved to be challenging. The Botswana Health Data Collaborative Roadmap Strategy (2020-24) states that "there exists parallel reporting systems and data is not integrated into the mainstream reports at the national level," seconded by the Botswana National eLearning strategy (2020), which states that "there is inadequate information flow at all levels, proliferation of systems, reporting tools are not synthesized; hence too many systems are not communicating.", Objective: The objectives of this study are to (1) create a visual representation of how data are processed and the inputs and outputs through each health care system level; (2) understand how frontline workers perceive health care data sharing across existing platforms and the impact of data on health care service delivery., Methods: The setting included a varied range of 30 health care facilities across Botswana, aiming to capture insights from multiple perspectives into data flow and system integration challenges. The study design combined qualitative and quantitative methodologies, informed by the rapid assessment process and the technology assessment model for resource limited settings. The study used a participatory research approach to ensure comprehensive stakeholder engagement from its inception. Survey instruments were designed to capture the intricacies of data processing, sharing, and integration among health care workers. A purposive sampling strategy was used to ensure a wide representation of participants across different health care roles and settings. Data collection used both digital surveys and in-depth interviews. Preliminary themes for analysis include perceptions of the value of health care data and experiences in data collection and sharing. Ethical approvals were comprehensively obtained, reflecting the commitment to uphold research integrity and participant welfare throughout the study., Results: The study recruited almost 44 health care facilities, spanning a variety of health care facilities. Of the 44 recruited facilities, 27 responded to the surveys and participated in the interviews. A total of 75% (112/150) of health care professionals participating came from clinics, 20% (30/150) from hospitals, and 5% (8/150) from health posts and mobile clinics. As of October 10, 2023, the study had collected over 200 quantitative surveys and conducted 90 semistructured interviews., Conclusions: This study has so far shown enthusiastic engagement from the health care community, underscoring the relevance and necessity of this study's objectives. We believe the methodology, centered around extensive community engagement, is pivotal in capturing a nuanced understanding of the health care data ecosystem. The focus will now shift to the analysis phase of the study, with the aim of developing comprehensive recommendations for improving data flow within Botswana's health care system., International Registered Report Identifier (irrid): DERR1-10.2196/52411., (©Grey Faulkenberry, Audrey Masizana, Badisa Mosesane, Kagiso Ndlovu. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 09.10.2024.)

Objective: To review the international literature and assess the ways healthcare systems are mitigating and can mitigate their carbon footprint, which is currently estimated to be more than 4.4% of global emissions., Design: Systematic review of empirical studies and grey literature to examine how healthcare services and institutions are limiting their greenhouse gas (GHG) emissions., Data Sources: Eight databases and authoritative reports were searched from inception dates to November 2023., Eligibility Criteria for Selecting Studies: Teams of investigators screened relevant publications against the inclusion criteria (eg, in English; discussed impact of healthcare systems on climate change), applying four quality appraisal tools, and results are reported in accordance with PRISMA (preferred reporting items for systematic reviews and meta-analyses)., Results: Of 33 737 publications identified, 32 998 (97.8%) were excluded after title and abstract screening; 536 (72.5%) of the remaining publications were excluded after full text review. Two additional papers were identified, screened, and included through backward citation tracking. The 205 included studies applied empirical (n=88, 42.9%), review (n=60, 29.3%), narrative descriptive (n=53, 25.9%), and multiple (n=4, 2.0%) methods. More than half of the publications (51.5%) addressed the macro level of the healthcare system. Nine themes were identified using inductive analysis: changing clinical and surgical practices (n=107); enacting policies and governance (n=97); managing physical waste (n=83); changing organisational behaviour (n=76); actions of individuals and groups (eg, advocacy, community involvement; n=74); minimising travel and transportation (n=70); using tools for measuring GHG emissions (n=70); reducing emissions related to infrastructure (n=63); and decarbonising the supply chain (n=48)., Conclusions: Publications presented various strategies and tactics to reduce GHG emissions. These included changing clinical and surgical practices; using policies such as benchmarking and reporting at a facility level, and financial levers to reduce emissions from procurement; reducing physical waste; changing organisational culture through workforce training; supporting education on the benefits of decarbonisation; and involving patients in care planning. Numerous tools and frameworks were presented for measuring GHG emissions, but implementation and evaluation of the sustainability of initiatives were largely missing. At the macro level, decarbonisation approaches focused on energy grid emissions, infrastructure efficiency, and reducing supply chain emissions, including those from agriculture and supply of food products. Decarbonisation mechanisms at the micro and meso system levels ranged from reducing low value care, to choosing lower GHG options (eg, anaesthetic gases, rescue inhalers), to reducing travel. Based on these strategies and tactics, this study provides a framework to support the decarbonisation of healthcare systems., Systematic Review Registration: PROSPERO: CRD42022383719., Competing Interests: Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: financial support from the National Health and Medical Research Council (NHMRC), Australia, for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Competing Interests: Competing interests: None.

Introduction: Legitimacy and trust are crucial for resilient health systems in fragmented conflict zones. This study evaluates the legitimacy of health systems in northwest Syria under different governance models., Methods: Using a deductive and inductive mixed-methods approach, the research team developed a framework with an index, 4 sub-indices and 18 indicators to assess the legitimacy of health systems using different governance models - top-down, bottom-up, and hybrid - in the context of the response to the earthquake that hit Syria in February 2023. The study includes surveys, workshops, stakeholder consultations, and an expert panel conducted in northwest Syria and online., Results: The findings indicate that bottom-up health governance model is perceived as the most legitimate, followed by the mixed model, while top-down model is perceived as the least legitimate. This preference is measured across all legitimacy source sub-indices, including legality, justification, consent and performance and across the overall Health System Legitimacy Index (HSLI). However, the hybrid governance approach showed limited superiority at two indicator levels regarding long-term health system response., Conclusion: This study highlights the importance of considering the legitimacy of the health system in fragmented conflict zones. It helps explain the effectiveness of the bottom-up approach and community-based governance in enhancing trust, cooperative behaviour, health interventions and achieving sustainability. Additionally, the study highlighted the role of legitimate health systems in practising civic virtue and promoting social justice, thus contributing to peace-building efforts. These insights are crucial for policymakers and development donors to strengthen health systems in challenging contexts., (© 2024. The Author(s).)

Introduction: The satisfaction of the public with the healthcare system of Ethiopia is a crucial but unanswered question. This is an essential issue since recent trends indicate that the demographic and epidemiological makeup of the population is changing. Therefore, the aim of this study was to assess the overall satisfaction of the public with the Ethiopian healthcare system in Addis Ababa, Ethiopia, in 2022., Methods: A community-based convergent parallel mixed methods study was conducted. Bivariable and multivariable logistic regression analyses were used to determine the factors associated with public satisfaction. A 95% confidence interval along with a p < 0.05 was deemed sufficient to declare a significant association. For the qualitative component, we used thematic analysis., Results: The vast majority, 77.2% (95% CI: 76.18-78.22%), of respondents were dissatisfied with the existing healthcare system. Moreover, Community-Based Health Insurance (CBHI) enrollment (2.35; 95% CI: 1.32-4.19), poor linkage to social capital (0.46; 95% CI: 0.25-0.83), poor access to healthcare services (0.39; 95% CI: 0.21-0.76), and absence of satisfactory responses to complaints (0.11; 95% CI: 0.04-0.27) were significantly associated with public dissatisfaction., Conclusion: Public satisfaction with the Ethiopian healthcare system is notably low and is affected by various factors, including enrollment in the CBHI, linkage to social capital, accessibility to healthcare, and satisfactory response to issues. Therefore, the Ethiopian government should focus on expanding CBHI coverage, improving access to healthcare services, and developing an immediate compliant solution strategy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Getahun, Demissie and Baraki.)

Objective: Informal healthcare providers (IHCPs) play a big role in health systems in low-and middle-income countries (LMICs) and are often the first point of contact for healthcare in rural and underserved areas where formal healthcare infrastructure is insufficient or absent. This study was performed to systematically review the literature on interventions targeting IHCPs in improving the delivery of healthcare services in LMICs., Methods: PubMed, Embase, and Cochrane CENTRAL databases were searched for studies that assessed any type of intervention among IHCPs to improve the delivery of healthcare services in any LMIC. Outcomes included changes in knowledge, attitude, and reported practice of appropriate case diagnosis and management; improved referral services; effective contraceptive use; and medication appropriateness (PROSPERO ID: CRD42024521739)., Results: A total of 7,255 studies were screened and 38 were included. Most of the studies were conducted in Africa and Asia. The IHCPs who were trained included medicine sellers, community health workers/traditional healers, and traditional birth attendants. The main intervention used was educational programs in the form of training. The other interventions were health services, policy and guidelines, and community-based interventions. Most of the interventions were multi-faceted. The disease/service areas targeted were mainly maternal and child health, sexually transmitted diseases, common infectious diseases, medicine use/dispensing practices, and contraception. The outcomes that showed improvements were knowledge, attitude, and reported practice; diagnosis and case management; improved referral services; contraceptive uses; and medication appropriateness. Around one-fourth of the studies reported negative results. The certainty of evidence generated (GRADE criteria) was very low., Conclusion: Some multifaceted interventions coupled with training showed improvements in the delivery of healthcare services by IHCPs. However, the improvements were inconsistent. Hence, it is unclear to identify any context-specific optimum intervention to improve the delivery of healthcare services by IHCPs., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Das, Khare, Eriksen, Diwan, Stålsby Lundborg and Skender.)