Frontiers in digital health最新文献

{"title":"Human-centered design for smart home technologies: a framework for aging and mental health.","authors":"Mohammad Mahdi Fakhimi, Adriana Hughes, Allison M Gustavson","doi":"10.3389/fdgth.2025.1555569","DOIUrl":"10.3389/fdgth.2025.1555569","url":null,"abstract":"<p><p>Smart home technologies (SHTs) offer promising ways to support older adults with both mobility challenges and mental health needs, yet high costs, complex interfaces, and uncertain data practices often limit adoption. This paper addresses these challenges by proposing a human-centered design (HCD) framework focused on affordability, inclusive design for physical and cognitive variations, and transparent data governance. Through illustrative examples of low-cost sensor networks and culturally tailored voice interfaces, we argue that thoughtfully designed SHTs can promote independent living, strengthen mental health interventions, and foster user trust. We conclude by highlighting policy incentives and cross-sector collaboration as critical levers for making SHTs an accessible, sustainable tool for aging populations.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1555569"},"PeriodicalIF":3.2,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12158959/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144287348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Balancing risks and benefits: clinicians' perspectives on the use of generative AI chatbots in mental healthcare.","authors":"Lyndsey Hipgrave, Jessie Goldie, Simon Dennis, Amanda Coleman","doi":"10.3389/fdgth.2025.1606291","DOIUrl":"10.3389/fdgth.2025.1606291","url":null,"abstract":"<p><strong>Introduction: </strong>The use of generative-AI chatbots has proliferated in mental health, to support both clients and clinicians across a range of uses. This paper aimed to explore the perspectives of mental health clinicians regarding the risks and benefits of integrating generative-AI chatbots into the mental health landscape.</p><p><strong>Methods: </strong>Twenty-three clinicians participated in a 45-minute virtual interview, in which a series of open-ended and scale-based questions were asked, and a demonstration of a mental health chatbot's potential capabilities was presented.</p><p><strong>Results: </strong>Participants highlighted several benefits of chatbots, such as their ability to administer homework tasks, provide multilingual support, enhance accessibility and affordability of mental healthcare, offer access to up-to-date research, and increase engagement in some client groups. However, they also identified risks, including the lack of regulation, data and privacy concerns, chatbots' limited understanding of client backgrounds, potential for client over-reliance on chatbots, incorrect treatment recommendations, and the inability to detect subtle communication cues, such as tone and eye contact. There was no significant finding to suggest that participants viewed either the risks or benefits as outweighing the other. Moreover, a demonstration of potential chatbot capabilities was not found to influence whether participants favoured the risks or benefits of chatbots.</p><p><strong>Discussion: </strong>Qualitative responses revealed that the balance of risks and benefits is highly contextual, varying based on the use case and the population group being served. This study contributes important insights from critical stakeholders for chatbot developers to consider in future iterations of AI tools for mental health.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1606291"},"PeriodicalIF":3.2,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12158938/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144287346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging machine learning in nursing: innovations, challenges, and ethical insights.","authors":"Sophie So Wan Yip, Sheng Ning, Niki Yan Ki Wong, Jeffrey Chan, Kei Shing Ng, Bernadette Oi Ting Kwok, Robert L Anders, Simon Ching Lam","doi":"10.3389/fdgth.2025.1514133","DOIUrl":"10.3389/fdgth.2025.1514133","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Aim/objective: &lt;/strong&gt;This review aims to provide a comprehensive analysis of the integration of machine learning (ML) (1) in nursing by exploring its implications on patient care, nursing practices, and healthcare delivery. It highlights current applications, challenges, ethical considerations, and the potential future developments of ML in nursing.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;With the advent of ML in healthcare, the nursing profession stands on the cusp of a transformative era. Despite the technological advancements, discussions on the utilization of ML in nursing, which are crucial for advancing the profession, are lacking. This review seeks to fill this gap by examining the balance between technological innovation and the human-centric nature of nursing.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Design: &lt;/strong&gt;This narrative review employs a detailed search strategy across several databases, including PubMed, Embase, MEDLINE, Scopus, and Web of Science. It focuses on articles that were published from January 2019 to December 2023. Moreover, this review aims to illustrate the current use, challenges, and future potential of ML applications in nursing.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Inclusion criteria targeted articles that focus on ML application in nursing, challenges, ethical considerations, and future directions. Exclusion criteria omitted opinion pieces and nonrelevant studies. Articles were categorized into themes, such as patient care, nursing education, operational efficiency, ethical considerations, and future potential, thus facilitating a structured analysis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Findings demonstrate that ML has significantly enhanced patient monitoring, predictive analytics, and preventive care. For example, the COMPOSER deep learning model for early sepsis prediction was associated with a 1.9% absolute reduction (17% relative decrease) in in-hospital sepsis mortality and a 5.0% absolute increase (10% relative increase) in sepsis bundle compliance. In nursing education, ML has improved simulation-based training by facilitating adaptive learning experiences that support continual skill development. Furthermore, ML contributes to operational efficiency through automated staffing optimization and administrative task automation, thus reducing nurse workload and enhancing patient care outcomes. However, key challenges include ethical considerations, such as data privacy, algorithmic bias, and patient autonomy, which necessitate ongoing research and regulatory oversight.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;ML in nursing offers transformative potential across patient care, education, and operational efficiency, which is balanced by significant challenges and ethical considerations. Future directions include expanding clinical and community applications, integrating emerging technologies, and enhancing nursing education. Continuous research, ethical oversight, and interdisciplinary collaboration are essential for harnessing ML's full ","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1514133"},"PeriodicalIF":3.2,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12141303/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144251085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Co-design of a digital 24-hour time-use intervention with older adults and allied health professionals.","authors":"Henry T Blake, Aaron Davis, Maddison L Mellow, Melissa Hull, Bethany Robins, Kate Laver, Dorothea Dumuid, Timothy Olds, Hannah A D Keage, Lui Di Venuto, Ashleigh E Smith","doi":"10.3389/fdgth.2025.1544489","DOIUrl":"10.3389/fdgth.2025.1544489","url":null,"abstract":"<p><p>Using co-design principles based on the Health CASCADE framework, we aimed to describe the collaborative process undertaken to develop a 24-hour time-use intervention, called Small Steps, which promoted gradual and incremental health-behavior change. A secondary aim was to reflect on the challenges and benefits of co-design in this project, offering insights into the \"why\" and \"how\" to co-design 24-hour time-use interventions with priority populations. Twelve participants were invited and participated in 6 co-design workshops (June 2023-January 2024). To prioritize older adults' views in the end-product, 8 adults aged >65 years (the target population) and 4 allied health professionals with >2 years' experience working with the target population were recruited. Workshops and activities were structured using the British Design Council's Double Diamond Design Process to stimulate design thinking. Where possible, participant-led documentation was used to reduce the bias associated with academic scribing and empower participants to provide input and facilitate ownership for the project. Workshop activities and discussions were captured through printouts, audio and iPad screen recordings and analyzed through reflexive thematic and content analysis. Co-designers contributed to all elements of the intervention including the website design, the content, and the level of researcher input during the intervention. Iterative improvements were made based on the unique perspectives and needs of the community experts. During the action planning process, older adults wanted both support and autonomy, while maintaining the freedom to adapt these options to their individual needs. Older adults also preferred a step-by-step approach, allowing for gradual behavior changes across the intervention to avoid feelings of becoming overwhelmed. The co-design process enabled the tailoring of the Small Steps intervention to the specific needs of its intended audience. Key factors contributing to the co-design included flexibility in the design process, fostering a supportive environment, and empowering participants through activities that guided and stimulated their thinking. These elements not only helped shape the development of Small Steps but reinforced the value of co-design in developing personalised interventions for older adults.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1544489"},"PeriodicalIF":3.2,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12141298/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144251084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A labeled dataset for osteoporosis screening based on electromagnetic attenuation.","authors":"Dionísio D A Carvalho, Mikael M R Costa da Silva, Gabriela A Albuquerque, Agnaldo S Cruz, Felipe Fernandes, Ingridy M P Barbalho, João Paulo Q Santos, Antonio H F Morais, Karilany D Coutinho, Antonio L P S Campos, Paulo Gil, César Teixeira, Jorge Henriques, Guilherme M Machado, Ricardo A M Valentim","doi":"10.3389/fdgth.2025.1538477","DOIUrl":"10.3389/fdgth.2025.1538477","url":null,"abstract":"","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1538477"},"PeriodicalIF":3.2,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12137236/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144236100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting chronic pain using wearable devices: a scoping review of sensor capabilities, data security, and standards compliance.","authors":"Johannes C Ayena, Amina Bouayed, Myriam Ben Arous, Youssef Ouakrim, Karim Loulou, Darine Ameyed, Isabelle Savard, Leila El Kamel, Neila Mezghani","doi":"10.3389/fdgth.2025.1581285","DOIUrl":"10.3389/fdgth.2025.1581285","url":null,"abstract":"<p><strong>Background: </strong>Wearable devices offer innovative solutions for chronic pain (CP) management by enabling real-time monitoring and personalized pain control. Although they are increasingly used to monitor pain-related parameters, their potential for predicting CP progression remains underutilized. Current studies focus mainly on correlations between data and pain levels, but rarely use this information for accurate prediction.</p><p><strong>Objective: </strong>This study aims to review recent advancements in wearable technology for CP management, emphasizing the integration of multimodal data, sensor quality, compliance with data security standards, and the effectiveness of predictive models in identifying CP episodes.</p><p><strong>Methods: </strong>A systematic search across six major databases identified studies evaluating wearable devices designed to collect pain-related parameters and predict CP. Data extraction focused on device types, sensor quality, compliance with health standards, and the predictive algorithms employed.</p><p><strong>Results: </strong>Wearable devices show promise in correlating physiological markers with CP, but few studies integrate predictive models. Random Forest and multilevel models have demonstrated consistent performance, while advanced models like Convolutional Neural Network-Long Short-Term Memory have faced challenges with data quality and computational demands. Despite compliance with regulations like General Data Protection Regulation and ISO standards, data security and privacy concerns persist. Additionally, the integration of multimodal data, including physiological, psychological, and demographic factors, remains underexplored, presenting an opportunity to improve prediction accuracy.</p><p><strong>Conclusions: </strong>Future research should prioritize developing robust predictive models, standardizing data protocols, and addressing security and privacy concerns to maximize wearable devices' potential in CP management. Enhancing real-time capabilities and fostering interdisciplinary collaborations will improve clinical applicability, enabling personalized and preventive pain management.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1581285"},"PeriodicalIF":3.2,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12137249/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144236101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influences on trust in the use of AI-based triage-an interview study with primary healthcare professionals and patients in Sweden.","authors":"Emilie Steerling, Petra Svedberg, Per Nilsen, Elin Siira, Jens Nygren","doi":"10.3389/fdgth.2025.1565080","DOIUrl":"10.3389/fdgth.2025.1565080","url":null,"abstract":"<p><strong>Introduction: </strong>Artificial intelligence (AI) has the potential to improve the quality and efficiency of medical triage in primary care. However, there are many uncertainties related to its use. Trust in these systems is important for successful integration and advancement into healthcare, yet this remains an understudied issue. Understanding the influences on trust in the actual use of AI is necessary for developing effective implementation strategies.</p><p><strong>Objective: </strong>This study aimed to explore the influences on trust of healthcare professionals and patients in the use of AI-based triage in primary care in Sweden.</p><p><strong>Methods: </strong>We applied qualitative study design using an inductive approach based on semi-structured interviews with 14 healthcare professionals and 12 patients in two regions in Sweden. The participants had experience of using AI-based triage in primary care. The interviews were transcribed verbatim and analyzed with reflexive thematic analysis to explore the influences on trust.</p><p><strong>Results: </strong>Healthcare professionals and patients experienced three types of influences on their trust in the use of AI-based triage in primary care: (1) provision of accurate patient information, (2) alignment with clinical expertise, and (3) supervision of patients' health and safety. Their experiences across these themes varied only in terms of the influence of experience-based knowledge. Both healthcare professionals and patients emphasized the importance of constructive dialogue, along with clear instructions for the use and storage of information.</p><p><strong>Conclusions: </strong>The results demonstrate that building trust in AI requires improved interaction to ensure that the system is adapted to the users' competencies and level of expertise. The generalizability of these insights is limited to AI-based triage in primary care in Sweden. Future research should explore trust in AI across different healthcare settings to inform policy, as well as to ensure safe use and design of AI applications.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1565080"},"PeriodicalIF":3.2,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12129910/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144217797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The development of a World Health Organization transdiagnostic chatbot intervention for distressed adolescents and young adults.","authors":"Dharani Keyan, Jennifer Hall, Stewart Jordan, Sarah Watts, Teresa Au, Katie S Dawson, Rajiah Abu Sway, Joy Crawford, Katherine Sorsdahl, Nagendra P Luitel, Anne M de Graaff, Heba Ghalayini, Rand Habashneh, Hafsa El-Dardery, Sarah Fanatseh, Aiysha Malik, Chiara Servili, Muhannad Faroun, Adnan Abualhaija, Ibrahim Said Aqel, Syed Usman Hamdani, Latefa Dardas, Aemal Akhtar, Richard A Bryant, Kenneth Carswell","doi":"10.3389/fdgth.2025.1528580","DOIUrl":"10.3389/fdgth.2025.1528580","url":null,"abstract":"<p><strong>Background: </strong>Common mental disorders are prevalent in young people in low- and middle-income countries (LMICs). Digitally delivered interventions have the potential to overcome many structural and psychosocial barriers to mental health care. Chatbots have been proposed as one potentially acceptable and feasible method that may increase engagement. Yet, there is currently limited evidence for their efficacy in reducing psychological distress. This paper summarises the development of a World Health Organization digital psychological intervention for young people experiencing impairing psychological distress, developed in line with Human Centred Design (HCD) principles.</p><p><strong>Objective: </strong>This study refined and adapted a chatbot intervention initially developed for adolescents aged 15-18 years that was completed in consultation with end-users in this age group (<i>N</i> = 236), community members (<i>N</i> = 73), and psychology intervention experts (<i>N</i> = 9) across varied settings. The purpose was to create an adaptation fit for use by young adults aged 18-21 years experiencing psychological distress in Jordan.</p><p><strong>Methods: </strong>The current study followed a limited user-centred design process involving focus groups and key informant interviews with stakeholders including young adults aged 18-21 years (<i>N</i> = 33), community members (<i>N</i> = 13), and psychology intervention experts (<i>N</i> = 11). Iterative design development occurred throughout the cultural adaptation and refinement process.</p><p><strong>Results: </strong>There was a clear preference for a chatbot based intervention that included interactions with fictional characters with relatable problems. The chatbot content followed a transdiagnostic model that addressed common problems including low mood, stress and anger with reference to vocational, familial and interpersonal stressors that the target population commonly faced. It followed a non-AI decision tree format with multiple sessions and was designed to be adaptable for use in different countries with different populations and software systems. Prototype versions of the chatbot were well-received by adolescents (15-18-year-old) and young adults (18-21-year-old).</p><p><strong>Conclusions: </strong>This is the first report of the development of a chatbot intervention for adolescents and young adults in LMICs that was designed using a HCD framework. Systematic end-user engagement through all phases of the research aimed to make this intervention acceptable and useable for adolescents and young adults in a wide variety of settings. The chatbot is currently being tested in randomised controlled trials in Jordan and Lithuania.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1528580"},"PeriodicalIF":3.2,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12128417/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Critical activities for successful implementation and adoption of AI in healthcare: towards a process framework for healthcare organizations.","authors":"Monika Nair, Jens Nygren, Per Nilsen, Fabio Gama, Margit Neher, Ingrid Larsson, Petra Svedberg","doi":"10.3389/fdgth.2025.1550459","DOIUrl":"10.3389/fdgth.2025.1550459","url":null,"abstract":"<p><strong>Introduction: </strong>Absence of structured guidelines to navigate the complexities of implementing AI-based applications in healthcare is recognized by clinicians, healthcare leaders, and policy makers. AI implementation presents challenges beyond the technology development which necessitates standardized approaches to implementation. This study aims to explore the activities typical to implementation of AI-based systems to develop an AI implementation process framework intended to guide healthcare professionals. The Quality Implementation Framework (QIF) was considered as an initial reference framework.</p><p><strong>Methods: </strong>This study employed a qualitative research design and included three components: (1) a review of 30 scientific articles describing differences empirical cases of real-world AI implementation in healthcare, (2) analysis of qualitative interviews with healthcare representatives possessing first-hand experience in planning, running, and sustaining AI implementation projects, (3) analysis of qualitative interviews with members of the research group´s network and purposively sampled for their AI literacy and academic, technical or managerial leadership roles.</p><p><strong>Results: </strong>The data were deductively mapped onto the steps of QIF using direct qualitative content analysis. All the phases and steps in QIF are relevant to AI implementation in healthcare, but there are specificities in the context of AI that require incorporation of additional activities and phases. To effectively support the AI implementations, the process frameworks should include a dedicated phase to implementation with specific activities that occur after planning, ensuring a smooth transition from AI's design to deployment, and a phase focused on governance and sustainability, aimed at maintaining the AI's long-term impact. The component of continuous engagement of diverse stakeholders should be incorporated throughout the lifecycle of the AI implementation.</p><p><strong>Conclusion: </strong>The value of this study is the identified processual phases and activities specific and typical to AI implementations to be carried out by an adopting healthcare organization when AI systems are deployed. The study advances previous research by outlining the types of necessary comprehensive assessments and legal preparations located in the implementation planning phase. It also extends prior understanding of what the staff's training should focus on throughout different phases of implementation. Finally, the overall processual, phased structure is discussed in order to incorporate activities that lead to a successful deployment of AI systems in healthcare.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1550459"},"PeriodicalIF":3.2,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12122488/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144200984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Role of digital health technologies in improving health financing and universal health coverage in Sub-Saharan Africa: a comprehensive narrative review.","authors":"Olutola Vivian Awosiku, Ibrahim Nafisa Gbemisola, Oluwafiponmile Thomas Oyediran, Oluwaseyi Muyiwa Egbewande, Jibril Habibah Lami, Daniel Afolabi, Melody Okereke, Fortune Effiong","doi":"10.3389/fdgth.2025.1391500","DOIUrl":"10.3389/fdgth.2025.1391500","url":null,"abstract":"<p><p>Digital technologies play a key role in developing a comprehensive and resilient healthcare delivery system in many low and middle-income countries in Sub-Saharan Africa. These technologies aim not only to address the financial accessibility gap for health needs but also to enhance innovation, partnerships, data management, and performance across healthcare stakeholders. By bridging gaps in access and reducing inequities, digital health technologies have the potential to mitigate socioeconomic disparities in healthcare delivery, particularly in resource-limited settings. This paper explores existing data on health challenges, financing, and universal health coverage in sub-Saharan Africa, along with examining digital health technologies, their adoption, and implementation. Case studies from initiatives such as M-TIBA in Kenya, JAMII in Tanzania, and L'UNION TECHNIQUE DE LA MUTUALITÉ MALIENNE in Mali are presented, along with recommendations for scale-up, policy enhancement, collaboration, support, and identification of research gaps and areas for further exploration.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1391500"},"PeriodicalIF":3.2,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12122447/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144200986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}