Significant reduction in intrapartum-related perinatal brain injury in infants born at ≥35weeks' gestation: A regional population-based study over 15years.

Digital literacy training within interventions to support older adults with cardiovascular disease in using technologies: a systematic review.

• Inequitable Access : MyHealth adoption favours women, Spanish nationals, and wealthier users, leaving older adults, migrants, and rural residents behind. • Wealth Bias : Digital health use skews toward higher-income groups, disproving assumptions that tech alone ensures equity. • COVID-19 Surge : Pandemic-driven platform growth didn’t reduce disparities—marginalised groups still lagged. • Key Barriers : Digital literacy, language, and rural infrastructure hinder access, requiring systemic solutions. • Call to Action : Policies must prioritise inclusive design, multilingual support, and rural investment to close gaps. The rapid expansion of digital health technologies during and after the COVID-19 pandemic has reshaped how individuals access healthcare services. While these innovations promise greater efficiency and reach, questions remain about their equitable uptake across different population groups. This study investigates the adoption and sustained use of MyHealth, a digital health platform introduced by the Catalan National Health System in 2015 and widely integrated into primary care delivery. Drawing on longitudinal administrative data covering around 80% of the Catalan population (9.46 million individuals) and over 727 million healthcare records from 2015 to 2023, we analyse patterns of digital engagement using survival analysis and microeconometric modelling. Our findings reveal persistent inequalities in both initial adoption and long-term usage. Engagement is higher among women, Spanish nationals, and individuals from higher-income areas. At the same time, older adults, migrants, and rural residents are significantly less likely to adopt or sustain use of the platform. These disparities were particularly pronounced during the pandemic's peak, when digital health use surged. Concentration indices confirm a clear pro-wealth bias in digital health engagement, challenging the assumption that digitalisation automatically enhances equity. Despite the platform’s wide availability and integration into the healthcare system, structural barriers, including digital literacy, cultural and linguistic accessibility, and infrastructure, continue to shape patterns of use. This study presents one of the first comprehensive, large-scale assessments of digital health inequality in Europe. It highlights the need for targeted, evidence-based policies to ensure that digital health transformation supports rather than undermines equity goals in healthcare access and delivery.

• Synthetic data from generative AI preserves privacy in healthcare modeling. • Generative AI adapts dynamically, surpassing static traditional CEA models. • Enhanced scenario simulations by generative AI aid robust decision-making. • Generative AI integrates real-world evidence, refining predictive accuracy. • Non-linear modeling in AI captures complex healthcare cost-outcome relations. Healthcare economic evaluation increasingly relies on predictive modeling to inform resource allocation decisions. Traditional cost-effectiveness analysis (CEA) methodologies face significant challenges when processing complex, heterogeneous healthcare datasets and accommodating dynamic system variables. This review examines how generative artificial intelligence technologies may transform predictive modeling frameworks in healthcare economics, specifically focusing on potential improvements in accuracy, adaptability, and efficiency in cost-effectiveness analyses. A literature search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore between October 2024 and January 2025, examining publications from 2018-2024. Critically, we identified a near absence of empirical studies that directly apply and validate generative AI technologies within formal health economic modeling or health technology assessment contexts. Most identified literature addresses general AI/ML applications in healthcare or synthetic data generation in adjacent domains, rather than demonstrating validated use in cost-effectiveness analysis. Generative AI demonstrates promising theoretical capabilities in handling non-linear healthcare relationships, generating privacy-preserving synthetic datasets, and enabling dynamic scenario exploration based on performance in related fields. However, direct empirical evidence comparing generative AI to traditional CEA approaches in real-world health technology assessment remains virtually non-existent. Potential advantages include automated model support, enhanced integration of real-world evidence, and improved handling of missing data scenarios. Technologies such as Generative Adversarial Networks and Variational Autoencoders show early-stage promise in addressing traditional modeling limitations in adjacent applications. Generative AI represents a conceptually significant potential advancement in healthcare economic modeling. However, claims presented are predominantly forward-looking and conceptual rather than empirically validated. Implementation challenges including model interpretability, regulatory frameworks, validation requirements, and ethical considerations require substantial empirical research before successful integration into healthcare decision-making processes.

Words without power: Reduced semantic (but not grammatical) agency signals low mood and self-esteem.

To evaluate the impact of deep learning reconstruction (DLR) on MRI productivity at a tertiary care academic hospital, and to validate a previously published Monte Carlo-based forecast of the productivity enhancement potential of DLR. Scanner log data were analyzed for two periods: pre-DLR adoption (January-October 2023, six scanners) and post-DLR (January-October 2025, five scanners). Examination and sequence durations were obtained from the Siemens Healthcare Teamplay platform. Observed changes were compared with capacity increases predicted by earlier Monte Carlo simulations. The impact of shortened scan durations across different scanner utilization levels was further evaluated using a simulation. Finally, four radiologists assessed image quality to identify potential clinical limitations of DLR. Optimized scanners demonstrated a total reduction between 4.8 and 11.1min (11.5-27.2%) in sequence duration and 5.0-10.7min (9.5-21.2%) in total examination time. Despite operating with one fewer scanner in 2025 (six scanners before DLR adoption, 5 scanners after), the mean hourly throughput of the entire fleet decreased by only 6.4%, indicating improved productivity per scanner. Notably, this throughput was achieved while DLR deployment and protocol optimization were still in progress, underscoring the substantial productivity benefit even at a partial implementation stage. DLR deployment was associated with improved MRI suite productivity, enabling nearly pre-DLR throughput despite operating with one fewer scanner. The results demonstrate the utility of DLR in improving MRI productivity and support the predictive accuracy of simulation-based health technology assessment. Nevertheless, unpredictable performance, particularly in neuroimaging, where artifacts in T2-weighted sequences and reduced quality in contrast-enhanced studies were observed, limits the applicability of DLR and underscores the need for rigorous quality assurance.

The global nursing shortage crisis presents a significant challenge to healthcare systems. Digital health technologies, such as communication tools, automation systems, monitoring devices, and information platforms have been proposed as one solution to alleviate the issue by optimizing nursing resources. However, a comprehensive overview of the use and potential of these technologies in optimizing nurses' work and resources is still lacking. The aim of this review is to provide an overview of (a) the digital health technologies used in nursing that may have potential to save nursing resources, (b) which indicators are used to measure the effectiveness of these technologies, and (c) which technologies are found to be effective in terms of saving nursing resources. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews in accordance with the Joanna Briggs Institute (JBI) Manual for Evidence Synthesis. The databases PubMed, CINAHL, and Web of Science were searched. Studies were included if they were peer-reviewed, quantitative, addressed nursing professionals, and considered digital health technologies that specifically aimed to save nursing resources. Relevant data were extracted and synthesized narratively, using an emerging framework. A total of 115 studies were included in this review. Digital health technologies fell into four categories: communication, automation, monitoring, and information technologies. All showed potential for saving resources, with monitoring technologies most often reported as having high potential. Indicators used to measure effectiveness focused on temporal, workforce, and workload resources, and related outcomes including quality of care, patient safety, and cost efficiency. Workforce resources were the most frequently optimized, followed by improvements in patient safety and quality of care. While positive effects were predominant, some studies reported adverse and insignificant effects, indicating variability in effectiveness. Digital health technologies offer promising opportunities to alleviate nursing resource shortages, but their potential seem to vary by type. Monitoring technologies showed the most consistent benefits, while communication and information technologies had mixed effects and automation technologies require further research to clarify their potential. Given the wide range of indicators used to measure potential resource savings, defining common, standardized indicators is essential for systematically assessing the impact of digital health technologies on nursing work and resources. This would enable comparability across studies, strengthen evidence-based decision-making and guide implementation strategies.

Despite recent advancements in oncology drug development, patient access to innovative cancer therapies remains inadequate. There is an urgent need for more patient-centric approaches, with meaningful patient input from trial design through to health technology assessment (HTA) consultation. Multi-stakeholder consensus calls for better representation of the diversity of the target population and integration of patients' preferences in clinical cancer research by systematically collecting patient-reported outcomes using standardized methods, and acknowledging trade-offs between survival and long-term wellbeing. Furthermore, the generation of insufficiently robust data for regulatory and HTA decision-making continue to delay patient access to innovation. This could be mitigated through smarter study designs, including smaller, fit-for-purpose randomized studies and prospectively designed trials. Finally, concerted efforts are required to develop and validate novel intermediate/surrogate endpoints that enable earlier assessment of treatment outcomes to facilitate timely, evidence-based decisions that improve the patient experience across the cancer care continuum.

Vosoritide, a biotechnological therapy designed to increase growth in children with achondroplasia, has introduced new pressures and bodily possibilities for families navigating this rare genetic condition. While debates around its use often centre on its efficacy as a non-surgical growth treatment for the most common form of non-lethal human dwarfism, far less attention has been paid to how the medication (re)shapes the temporal landscape of maternal decision-making, children's bodily autonomy, and community dynamics. Drawing on qualitative interviews with mothers from UK dwarfism communities, comprising both average-statured and dwarf mothers, this research locates maternal decision-making within broader regimes of health governance, biosocial communities, and concepts of 'good' mothering. Conceptually, the article foregrounds how Vosoritide functions as a future-oriented health technology and a site of anticipatory biopolitics; governing decision-making through overlapping and complex regimes of temporality, maternal responsibilisation, and biosociality. Vosoritide emerges not only as a site of biomedical possibility, but also as a biopolitical discourse, shaping how mothers of children with dwarfism (re)imagine and manage their child's body, future, and identity. In doing so, this research advances sociological scholarship by exposing the temporal and anticipatory 'logics' through which biopower operates in the governance of dwarfism.

Technological advancements driven by engineering have become key factors in elevating Türkiye's global standing in health tourism, greatly strengthening its competitiveness and attracting a growing number of international patients. Emin Taner Elmas is not a dentist or endodontist, but a Mechanical Engineer and academic. His work focuses not directly on classical dental clinical practice or endodontics, but rather on interdisciplinary fields such as thermodynamics, energy transfer, fluid mechanics, and biomedical engineering. However, Elmas's engineering approach has the potential to contribute to the medical field, including dentistry, indirectly through biomedical and health technologies: Biomedical Approach: Treating the human body as a "bio-machine," Elmas develops theories on the natural vibration frequencies of organs and tissues. This "bio-robotic resonance" theory can inspire the design of next-generation devices for tissue healing or disease diagnosis at a theoretical level. Medical Device Modeling: His expertise in thermodynamics and fluid mechanics is used in the design and simulation of medical devices (e.g., hemodialysis machines or drug delivery algorithms). The mechanical strength of surgical instruments used in dentistry or the thermal effects of dental lasers are engineering problems that fall within Elmas's area of ​​expertise. Interdisciplinary Technologies: He has studies on machine learning and artificial intelligence-supported diagnostic systems. These technologies are increasingly used in the field of endodontics today, such as caries detection and root canal anatomy analysis. In summary, Emin Taner Elmas is not a dentist, therefore he does not develop clinical endodontic procedures. However, his work applying engineering principles to the biomedical field has the potential to contribute to the scientific infrastructure of future dental technologies (device design, diagnostic algorithms, etc.). The "Bio-robotic Resonance and Thermodynamic Interaction" theory and medical technology models developed by Emin Taner Elmas can be indirectly adapted to the fields of dentistry and endodontics. The potential contributions of Elmas's work to dental technologies can be evaluated under the following headings: Bio-robotic Resonance and Diagnosis: Elmas views the body as a "bio-machine," arguing that each tissue has its own unique natural vibration frequency. This approach could form the basis for the development of next-generation diagnostic devices that can detect the condition of tooth canals or microcracks in the tooth root using acoustic signal analysis and Fourier transforms in endodontics. Smart Drug Algorithms: His work focuses on smart drug algorithms and simulations via "Frequency Modulation". This modeling can be used to optimize the thermodynamic interaction of disinfectants or drugs applied into the root canal with the tissue in endodontic treatments. Medical Device Modeling: As a thermodynamics and fluid mechanics specialist, Elmas works on the prototype design and simulation of medical devices (such as hemodialysis machines). This engineering knowledge can directly address specific engineering problems in dentistry, such as controlling the thermal effects of dental lasers or increasing the mechanical efficiency of surgical instruments. Interdisciplinary Approach: His work generally focuses on "Medical Technology," combining mechanical engineering and medical sciences. This perspective contributes to the development of the mechanical and software infrastructure of advanced technologies such as digital intraoral scanners and robotic surgical support systems, which are becoming increasingly common in dentistry today. In summary, Elmas's contribution focuses on the engineering design and theoretical physics of smart devices and diagnostic systems used in dentistry, rather than a clinical application.[1-73]

The judicialization of public health consists of resorting to the Judiciary to obtain actions, services, medications, and health technologies from the Unified Health System (SUS), a right guaranteed by the 1988 Federal Constitution. In this scenario, the Supreme Federal Court (STF), as guardian of the Constitution, plays a central role in standardizing the interpretation of the right to health through its jurisprudence. In 2024, the STF ruled on Topics 6 and 1234, which redefined the parameters for the judicial granting of medications by the SUS. Topic 6 established rigorous cumulative criteria, requiring proof of the efficacy and safety of the medication based on Evidence-Based Medicine, the non-existence of an incorporated therapeutic alternative, and the financial incapacity of the plaintiff. Theme 1234, in turn, regulated the judicial competence and passive legitimacy of federative entities, in addition to linking judicial provision to the Maximum Price for Sale to the Government (PMVG). These understandings were consolidated in Binding Precedents 60 and 61, whose non-compliance authorizes the filing of a Constitutional Claim with the STF, an instrument intended to preserve the authority of the Court's decisions. However, recent decisions, such as Constitutional Claim No. 87,745/RJ, highlight difficulties in assimilating the new parameters. The study points out that the Constitutional Claim has consolidated itself as a relevant mechanism for the process of assimilating the recent jurisprudence of the STF, revealing interpretative tensions and the need for greater decisional harmonization to ensure legal certainty, equity, and sustainability to the SUS. Received: December 19, 2025 | Revised: February 23, 2026 | Accepted: February 27, 2026

Hospital acquired infections (HAIs) remain a prevailing issue in clinical settings. These challenges are associated with the health and economic burdens of complications such as bloodstream infections, biomedical device fouling, and antimicrobial resistance. Nitric oxide (NO) is an endogenous gasotransmitter that has vasodilatory, antimicrobial, and antiplatelet effects. This has allowed NO to surface as a potential bioactive strategy for integration into medical device technologies. When combined with other surface modifications, such as liquid infusion (LI) and copper nanoparticles(CuNPs), this approach yields slippery, antifouling surfaces with dual antibacterial and NO-catalysis properties. Herein, we combined S-nitroso-N-acetylpenicillamine (SNAP), a promising nitric oxide donor that releases NO at physiological levels for 7 days in the presence of heat and light, with catalytic tuning via CuNPs with a LI polymeric surface. In addition, SNAP Cu LI samples exhibited minimal donor leaching and slippery behavior with sliding angles of <20° across a 7-day test period. These propertiesof NO support its antimicrobial and antiplatelet effects in addition to enhanced catalysis induced by metal-donor interactions. This eradicates microorganisms such as methicillin-resistant Staphylococcus aureus (MRSA) and Escherichia coli with >99.8% and 98.7% reduction in bacterial cell viability, respectively. In addition, LI reduced blood protein adsorption by >80% and prevented platelet activation by >72%. By integrating nitric oxide-releasing silicone rubber with copper nanoparticles and a liquid-infused layer, these surface modifications produce a multifunctional medical device surface with antibacterial, antithrombotic, and antifouling capabilities.

ABSTRACT Background Stroke remains a leading cause of long‐term disability worldwide, and rehabilitation is essential for recovery. Although artificial intelligence (AI)‐related technologies have received growing attention in stroke rehabilitation, the knowledge structure and thematic evolution of this interdisciplinary field remain unclear. Objective To conduct a bibliometric analysis of AI‐related research in stroke rehabilitation from 2005 to 2024 and map publication trends, major contributors, thematic clusters, and emerging topics. Methods Relevant publications were retrieved from the Web of Science Core Collection (WoSCC), including SCI‐Expanded and SSCI, on November 30, 2024. Only English‐language articles and review articles published between January 1, 2005, and November 30, 2024 were included. A total of 3436 records were analyzed using CiteSpace 6.4.R1 Basic, GraphPad Prism 10.1.2, and biblioshiny in R. Analyses covered publication trends, collaboration networks, journal distribution, keyword co‐occurrence, clustering, and burst detection. Results Publication output increased markedly over time, with the United States contributing the largest number of publications. The Swiss Federal Institutes of Technology Domain was among the leading institutions, and Rocco Salvatore Calabrò was among the most productive and highly cited authors. Core publication venues included the Journal of NeuroEngineering and Rehabilitation and IEEE Transactions on Neural Systems and Rehabilitation Engineering . The literature mainly focused on virtual reality, upper‐limb rehabilitation, rehabilitation robotics, machine learning, cognitive rehabilitation, and transcranial direct current stimulation. Recent burst terms, including machine learning, artificial intelligence, and deep learning, indicated growing attention to data‐driven rehabilitation approaches. Conclusions AI‐related research in stroke rehabilitation has expanded substantially, with increasing emphasis on adaptive, data‐driven, and technology‐assisted approaches. This study provides a descriptive overview of the field's major trajectories, emerging gaps, and interdisciplinary directions, and may help inform future research and translational exploration.

Abstract Digital health technologies (DHTs) are revolutionizing medical research, offering unprecedented insights into health monitoring and disease detection through continuous, real-world data collection. Here we characterize the data in one of the largest and most demographically rich DHT datasets as part of the All of Us Research Program. Through a historic device distribution effort, the program reached a broad range of participants nationwide, yielding a DHT dataset with an expanded a large demographic scope. This dataset contains Fitbit data from more than 59,000 participants spanning 14 years with more than 39 million step observations and 31 million sleep observations. Nearly half (46%) of participants with Fitbit data also contributed electronic health records, physical measurements, genomics and survey data. This resource enables researchers to study relationships between digital health metrics and clinical outcomes, advancing DHT methodologies through its large size, broad representation and multi-modal data linkage.

Abstract Background Everyday digital technologies such as social media, gaming, and internet use are deeply integrated into the lives of children, adolescents, and young adults. While these platforms can foster connection, learning, and entertainment, concerns have grown about their potential to influence mental, physical, and social well-being. Research on this topic has expanded rapidly over the past decade, yet much of it remains cross-sectional, limiting insights into long-term outcomes. Longitudinal studies are essential to capture evolving patterns of digital engagement, identify causal relationships, and guide effective policies and interventions that support youth in navigating digital environments. In particular, evidence is needed to distinguish between beneficial and harmful forms of digital engagement, such as social connection versus problematic use, and to understand how these impacts differ across diverse populations and contexts. The COVID-19 pandemic further accelerated young people’s technology use, underscoring the urgency of examining both risks and opportunities. This review, therefore, synthesizes longitudinal research to map trends, identify knowledge gaps, and inform future directions. Objective The study aimed to systematically identify and map longitudinal studies examining associations between everyday digital technology use (eg, social media, gaming, and internet use) and the health and well-being of youth (25 years or younger) and to chart the types of evidence available by technology category, outcomes, and geographical setting in order to highlight key gaps for future research. Methods A systematic search of PubMed, Embase, and PsycArticles (2014‐2024) was conducted and reported in accordance with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). Data extraction covered demographics, digital technology categories, and health outcomes. Studies were grouped into 6 key themes: social media use and mental health, digital addiction and behavioral outcomes, physical activity and digital technology, digital health technologies and cognitive development, parental influence and digital technology, and digital well-being and risk behaviors. Results Of the 456 studies identified, 267 were longitudinal studies relevant to our research aims. Internet use (n=201 studies), social media (n=140 studies), and gaming (n=83 studies) dominated the themes. Mental health was the most frequently assessed outcome, with a focus on anxiety and depression. Geographically, 15% (40/267) of studies originated from low- and middle-income countries, with the majority from high-income settings such as the United States (n=76 studies) and Australia (n=15 studies). Nearly half (131/267, 49%) were published post 2020, reflecting heightened interest during the COVID-19 pandemic. Conclusions Longitudinal evidence on everyday digital technology use and youth health is growing but remains concentrated in mental health outcomes and high-income settings, with notable gaps in physical health, educational outcomes, and equity-focused research. These findings highlight the need for more diverse, methodologically robust longitudinal studies to inform context-sensitive policies and interventions that balance the risks and benefits of digital engagement for young people.

Thailand's peritoneal dialysis (PD) policy evolution illustrates how apparent policy success can mask underlying system vulnerabilities. Between 2008 and 2021, Thailand's PD-First policy under the Universal Coverage Scheme (UCS) became an international model, expanding PD utilization from 2760 to over 25,000 patients while maintaining outcomes comparable to global benchmarks. In February 2022, the 2022 Policy removed PD-First restrictions to enhance patient choice. Within 3 years, PD utilization among incident UCS patients declined from 50% to 14%, 90-day mortality risk rose from 9% to 13%, and dialysis expenditures increased to 12% of the total UCS budget. These outcomes prompted the 2025 Reform in April 2025, following a review commissioned by the National Health Security Office and conducted by the Health Intervention and Technology Assessment Program (HITAP), with expert input from the Nephrology Society of Thailand (NST) and other stakeholders. Using aggregated national registry data across three periods-the PD-First period (2008-2021), the 2022 Policy period (2022-2024), and the 2025 Reform period (2025)-we examined utilization patterns, clinical outcomes, and fiscal impacts associated with these transitions. The temporal patterns suggest that rapid policy liberalization may expose and amplify existing capacity imbalances. Following the 2022 transition, utilization shifted toward existing hemodialysis infrastructure despite inadequate vascular surgery capacity, coinciding with reduced rates of permanent access and increased early mortality. Preliminary post-reform data show encouraging recovery, with 90-day mortality risk falling to 7%. Thailand's experience may inform countries expanding home dialysis through policy reforms, highlighting that sustainable choice requires sequenced policy liberalization, infrastructure capacity alignment, and responsive governance to maintain dialysis quality, equity, and fiscal stability.

The Impact of Health Technology Assessment on Pharmaceutical Prices: Evidence from Germany.

Passive digital health technologies (DHTs) are increasingly promoted as scalable tools for detecting Alzheimer's disease and related dementias (ADRD) earlier than routine clinic visits. We searched six major databases for English-language studies published between January 2014 and July 2024 that used passively collected, real-world DHT data for ADRD screening or diagnosis. Thirty studies met the criteria. Population sizes were highly skewed (median = 87; range 12-82,829), and most designs were longitudinal (53%) and fully passive (68%). Wrist-worn accelerometers and photoplethysmography sensors dominated, though several studies also used gait, sleep, voice, radar, or posture-tracking devices. A cross-study synthesis showed that those two modalities were primarily applied to memory, attention, and language tasks. Nineteen studies reported median accuracy, sensitivity, specificity, and precision between 80-90%, with F1-score and AUC medians approaching 78%, though relying on in-sample cross-validation rather than external cohorts. Reference standards varied widely, data-quality criteria were seldom reported, and fewer than 5% shared datasets publicly. Classification was the predominant modeling strategy, with regression emerging only in recent years. Overall, passive DHTs show promise as low-burden triage tools for population-level ADRD screening, but routine deployment will require more diverse cohorts, harmonized reporting, multimodal privacy-preserving analytics, and rigorous human-factors evaluation.

Pulmonary fibrosis, encompassing idiopathic pulmonary fibrosis (IPF) and other fibrosing interstitial lung diseases (ILDs) with a progressive phenotype (PPF), represents a group of chronic, life-threatening conditions associated with significant morbidity, mortality, and socioeconomic burden. Despite advances in antifibrotic therapies, traditional disease-centered management alone is insufficient to address the multidimensional needs of affected patients. This comprehensive review advocates for a holistic, patient-centered approach to the management of pulmonary fibrosis, integrating pharmacological interventions with systematic comorbidity assessment, pulmonary rehabilitation, psychosocial support, nutritional optimization, early palliative care, social and community reinforcement, and digital health technologies. We examine the evidence supporting each dimension of holistic care, discuss current barriers to implementation—including healthcare fragmentation, limited multidisciplinary protocols, and scarce resources—and outline future perspectives centered on precision medicine and integrated care models. By shifting from a purely organ-focused paradigm to a comprehensive, multidisciplinary strategy, clinicians can improve not only disease outcomes but also quality of life and overall well-being for patients living with fibrosing ILDs.

The population of children and youth with special health care needs (CYSHCN) has grown significantly. This includes children with medical complexity (CMC), those with chronic conditions, functional limitations, or reliance on medical technology. Due to advances in medical care, these children are now able to live at home, making encounters with emergency medical services (EMS) more likely. EMS clinicians receive limited pediatric training and have infrequent encounters with this population, resulting in variable comfort and preparedness. Utilization of emergency care among CMC is further influenced by social determinants of health, with disadvantaged communities experiencing higher emergency department use and increased child mortality. To bridge these gaps, the Special Needs Tracking and Awareness Response System (STARS) was developed to enhance EMS readiness, promote health equity, and improve prehospital care for CMC. Launched in 2014 as an EMS-driven initiative, STARS has evolved into a hospital-based, physician-led program with individualized emergency care plans stored in a secure electronic system. A major focus of STARS is to create and provide emergency care education to EMS and community EDs in their catchment area regarding STARS. As of 2025, STARS has enrolled 2424 patients. The program has reduced unnecessary transports, strengthened disaster response, and offered an opportunity to address health inequities in CMC. STARS provides a scalable and collaborative model that prioritizes medically complex, high-risk pediatric populations through targeted EMS training, interdisciplinary care coordination, and real-time access to patient-specific plans. This approach offers a unique opportunity to advance prehospital care and improve health outcomes for CMC.