Explore the vast range of titles available.

Health systems worldwide face growing pressure from population aging, multimorbidity, and rising emergency admissions, prompting reconsideration of traditional inpatient care models. In response, digitally enabled models such as tele–intensive care unit (tele-ICU) programs, hospital-at-home services, virtual wards, and other remote specialist pathways have expanded, particularly after the COVID-19 pandemic accelerated telemedicine adoption and cross-site virtual staffing. However, nationally coordinated, multispecialty virtual hospitals remain uncommon worldwide, and robust evidence on their system-level effects is still limited. As a result, policy discussions about national virtual hospitals must often draw on evidence from related virtual-care models rather than from mature national implementations. This viewpoint synthesizes representative international evidence from tele-ICU systems, hospital-at-home programs, virtual wards, telestroke networks, and other condition-specific virtual-care pathways, and examines Saudi Arabia’s Seha Virtual Hospital (SVH) as a national case study to identify policy lessons relevant to the design, governance, and evaluation of national virtual hospitals. Across settings, these models suggest that remote and digitally supported care can achieve outcomes comparable to in-person hospital care when patient selection is appropriate, escalation and transfer pathways are explicit, monitoring intensity matches clinical risk, and multidisciplinary teams are integrated into local workflows. Tele-ICU programs have reported reductions in intensive care mortality and length of stay under well-structured organizational models, while hospital-at-home and virtual-ward programs have shown comparable safety, reduced hospital usage, and improved patient experience among selected patient groups. Telestroke networks likewise demonstrate outcomes comparable to specialist in-person care in acute stroke pathways. Nevertheless, the evidence base remains heterogeneous and strongly context-dependent. Much of the literature is short-term, with limited consistent evidence on long-term outcomes, caregiver burden, cost-effectiveness, workforce implications, and digital equity. SVH illustrates the emerging implementation of a centralized national virtual hospital model. Launched in 2022 under Saudi Arabia’s Vision 2030 Health Sector Transformation Program, SVH operates as a national telehealth hub embedded within the country’s broader digital-health ecosystem and links hospitals across the Kingdom to specialized clinical expertise. Its service portfolio includes urgent and critical care consultations, specialized virtual clinics, multidisciplinary case discussions, and supportive diagnostic services. Early reports indicate rapid operational expansion, broad institutional participation, and national-scale feasibility. However, independent comparative evidence evaluating SVH’s effects on mortality, readmissions, length of stay, cost-effectiveness, equity, and workforce sustainability remains limited. National virtual hospitals should therefore be understood as evidence-generating health-system innovations rather than fully validated care models. Sustainable scale-up requires embedding rigorous prospective evaluation within implementation, aligning financing mechanisms with substitution of inpatient care, establishing clear governance and regulatory frameworks, and addressing digital inclusion and workforce sustainability. These considerations can help guide policymakers and health-system leaders in the accountable, equitable, and evidence-informed development of national virtual hospital programs.

Medication abortion accounts for the majority of abortions in the United States, driven in part by the growth in access to telehealth provision of medication abortion. While research indicates high patient satisfaction with telehealth medication abortion care, research on care preferences of patients who use medication abortion remains understudied. Understanding these preferences is essential to informing evidence-based policies that enable people to access person-centered abortion services that meet their values, preferences, and needs. The aim of this study is to compare the abortion care features rated as most important among patients receiving medication abortion via telehealth vs in-person care and to assess participants' access to technologies required for telehealth care. We hypothesized that participants receiving telehealth care would place greater importance on features supporting limited in-person clinic interaction. From May 2021 to March 2023, we surveyed participants (≤70 d of gestation, English or Spanish speakers, aged ≥15 y) obtaining medication abortion at 4 organizations providing care in 6 US states. Participants rated the importance of 12 abortion care features (ie, getting care at home, convenience, cost, safety, effectiveness, and privacy) and described access to technologies required for telehealth. We used bivariable logistic and ordinal regressions with robust SEs to assess whether features rated as \"extremely important\" differed by the medication abortion model received (telehealth vs in-person). Among 1017 patients approached, 876 were eligible, 583 participants enrolled, 487 initiated a survey, 477 (242 telehealth and 235 in-person) completed survey questions regarding access to technologies for telehealth, and 397 completed questions about abortion care features. Across groups, the features most often rated as \"extremely important\" included effectiveness (340/397, 85.6%), safety (326/397, 82.1%), timeliness (307/397, 77.3%), and privacy (165/211, 78.2%), with no significant differences (P>.05) between groups. Compared to in-person participants, telehealth participants were more likely to report getting care at home (65.9% vs 43.5%; odds ratio [OR] 2.48, 95% CI 1.57-3.89; P<.001) and having a medication abortion (62.1% vs 51.1%; OR 1.58, 95% CI 1.11-2.23; P=.01) as extremely important. They were less likely to report having an in-person meeting with a clinician (16.1% vs 45.7%; OR 0.23, 95% CI 0.19-0.26; P<.001) and ultrasonography (15.2% vs 38.2%; OR 0.28, 95% CI 0.20-0.40; P<.001) as extremely important. Abortion care features rated as extremely important were sometimes discordant with the care received. Almost all participants had access to the technologies required for telehealth. While telehealth abortion services offer many features that people find important, the availability of both telehealth and in-person abortion care remains critical to ensuring that care aligns with patient preferences. In addition to efforts focused on expanding access to telehealth medication abortion services, advocates and policymakers should continue their work to ensure access to in-person care for those who need or prefer this model.

Case definitions are essential for effectively communicating public health threats. However, the absence of a standardized, machine-readable format poses significant challenges to interoperability, epidemiological research, data sharing, and the application of computational methods, including artificial intelligence. These barriers complicate collaboration across regions and organizations and hinder technological progress in public health. This study aims to propose and release the first open, machine-readable format for representing case and syndrome definitions, together with tools and resources that enable their standardized and scalable use. We developed the Open Syndrome Definition, a structured, machine-readable schema for representing case and syndrome definitions. We compiled official public health case definitions from multiple institutions and converted them into standardized, machine-readable representations using open-source tools. These tools, available through GitHub under the Massachusetts Institute of Technology license, automate the translation of narrative definitions into structured data. We also created a platform for browsing, analyzing, and contributing new definitions on our initiative website. The Open Syndrome Definition format enabled consistent, automated representation of case definitions across different diseases and jurisdictions. The conversion tools achieved high semantic fidelity, as assessed by qualitative expert review, between narrative and structured representations, supporting human verification and automated analysis. The dataset and accompanying tools demonstrated structural and semantic interoperability by standardizing definitions from various health systems into a unified format and integrating existing medical ontologies through JSON for Linked Data. To further illustrate practical applicability and downstream usage, we introduced a data filtering prototype that allows users to upload their own datasets and verify the results against the standardized definitions. The Open Syndrome Definition establishes a foundation for consistent and machine-readable public health definitions, facilitating reproducible research and interoperability at scale. By enabling systematic data exchange and artificial intelligence-driven analysis, it strengthens public health preparedness and supports more rapid, coordinated responses to emerging health threats.

Public awareness campaigns and testing promotion must be strengthened to eliminate infections with hepatitis B and C viruses (HBV and HCV, respectively) by 2030. Although public health campaigns using various forms of advertising are widely implemented, the most appropriate channels for viral hepatitis testing remain unclear. This study aims to identify web services and digital advertising channels appropriate for promoting HBV and HCV testing, segmented by prior testing history and the desire for hepatitis virus testing. A nationwide cross-sectional online survey of Japanese adults aged 20 to 69 years was conducted. The respondents answered questions regarding viral hepatitis testing status, routinely used web services (180 options), and exposure to digital advertising (25 options). Correspondence analysis was used to visualize relationships among testing segments, web services, and digital advertising. For individuals classified as \"never having been tested and wishing to be tested,\" channel-specific alignment was quantified using cosine θ. Sensitivity analyses were conducted by repeating the correspondence analysis after excluding respondents uncertain about their testing history and by fitting modified Poisson regression models with robust variance to estimate prevalence ratios and 95% CIs. Of the 2000 respondents (1011 male and 989 female), 18% (n=359) reported prior HBV and HCV testing, and 22.1% (n=441) were unsure whether they had ever been tested. Web services characteristically associated with \"never having been tested and wishing to be tested\" included Lawson (convenience store: cosine θ=0.989) and Cosme (shopping: cosine θ=0.987). The corresponding digital advertising channels included in-store and storefront screens at Welcia (pharmacy chain: θ=0.994) and Lawson (cosine θ=0.937). Segment-specific patterns varied according to age group and sex. Sensitivity analyses excluding the unsure group showed similar patterns. Modified Poisson regression results were also consistent; for example, Lawson web service use was associated with a desire for hepatitis virus testing (prevalence ratio 1.75, 95% CI 1.22-2.52). In Japan, the convenience store chain Lawson was a frequently used touchpoint, both online and offline, among individuals seeking viral hepatitis testing. Future studies are needed to determine whether implementing awareness-raising activities through Lawson can increase the uptake of testing and subsequent treatment.

Electronic health records are often extracted and combined with environmental data to conduct research or public health surveillance. However, to date, electronic health record systems do not integrate environmental data to aid real-time decision-making that could mitigate the health impacts of environmental hazards, including those related to climate change. Pursuing this goal requires enhancements to health record systems and modifications to the financial incentives driving health care innovation and delivery.

Medical applications of mathematical modeling, including machine learning models, knowledge graphs, and health digital twins, primarily involve the prediction of patient outcomes. This expert perspective examines how mathematical modeling can contribute to health care quality management. Definitions of procedures, patient outcomes, and quality metrics are provided with a quantitative focus. The emphasis is subsequently placed on 3 categories of patient-centered quality of care, namely, patient safety, procedure accuracy, and procedure efficacy, for which a conceptual and mathematical description is provided. Different levels of modeling tasks essential for managing patient-centered quality of care are identified. This article facilitates a deeper understanding of the topic by assigning relevant publications to these 3 quality categories. Focus is placed on the applicability of graph-based methods, including knowledge graphs and health digital twins, to improve quality management in health care. We have presented a clinical scenario and provided information on methodological limitations, future research directions, and practical implications.

The use of electronic medical records (EMRs) and clinical registries has transformed health care delivery by improving data management, care coordination, and research capacity. However, the full potential of these technologies can only be realized through effective interoperability, thereby reducing the burden of manual data entry and enhancing the use of real-world clinical data. This review examines technologies that enable automated data extraction and transfer, which promote interoperability between EMRs and clinical registries. A search of PubMed, CINAHL, Embase, and Web of Science, including studies published between January 2013 and April 2025, was registered with Open Science Framework a priori and involved three key concepts: (1) \"registry,\" (2) \"electronic medical records,\" and (3) \"interoperability.\" A 2-phase screen identified studies evaluating technologies that facilitate automated data extraction or interoperability. Automation was defined as fully automated, where data are extracted and transferred without human intervention, or semiautomated, where extraction or transfer is predominantly automated but may include manual validation. Only technologies supporting ongoing database integration were eligible for inclusion. Screening, data extraction, and synthesis were conducted by multiple independent reviewers. Technology experts provided extensive input and guidance throughout to ensure the accuracy and relevance of the extracted information. Overall, 36 studies met the inclusion criteria, representing 12 countries across 5 continents and addressing a wide range of acute and chronic health conditions. Epic was the most frequently reported EMR system, while the most common registry platforms were REDCap (Research Electronic Data Capture; Vanderbilt University), structured query language (SQL) server database, and EMR-embedded solutions. Most approaches centered around extracting data from structured formats (n=18), or a combination of both structured and unstructured formats (n=10), emphasizing the central role of structured EMR data in current automated extraction approaches. This review advances understanding of interoperability between EMRs and clinical registries by uniquely examining automated and sustainable solutions for data exchange, extending beyond prior work that has largely focused on technologies designed for isolated systems or study-specific data extraction. A novel contribution of this review is the synthesis of context-specific considerations derived from reported implementations, providing a comprehensive overview of how technology selection and implementation are shaped by the context in which they are deployed. While these advancements have reduced reliance on inefficient, error-prone, and resource-intensive manual processes, ongoing challenges in data standardization, seamless integration, and long-term sustainability are compounded by poor and inconsistent reporting across studies. Future efforts should follow comprehensive reporting guidelines, adhere to robust governance principles, and incorporate implementation science frameworks, to not only enable meaningful comparison and synthesis in future research, but also to ensure that technologies can be effectively, feasibly, and sustainably integrated within health care contexts, while upholding the ethical and equitable use of health care data.

Artificial intelligence (AI)–enabled systems must simultaneously improve the Quintuple Aim and digital health maturity, including equitable access to and quality and interoperability of data, tools, agents, and services. This requires a comprehensive sociotechnical and global approach to cocreation, management, and governance for individuals and organizations in the ecosystem.

Public health is undergoing profound transformation driven by data from the global health sector and related fields. To address systemic health disparities, scholars and health practitioners are increasingly applying a data equity lens, an approach that has become even more urgent as the United States faces the erosion of public health data infrastructure. This paper summarizes insights from an April 2024 convening by the Yale School of Public Health—The Role of Data in Public Health Equity and Innovation—with intersectoral stakeholders from academia, government (local, state, and federal), health care, and private industry. The convening included keynote presentations and roundtables regarding the depiction of social determinants of health in data; effects of artificial intelligence (AI) on health data equity; and community-based models for data, providing a framework for cross-cutting discussions. Through a narrative synthesis, themes were identified and synthesized from systematically gathered information from presentations and roundtables. This process led to a set of actionable, cross-cutting recommendations to guide inclusive and impactful data practices for policymakers, public health professionals, and health innovators across diverse contexts: (1) enable big data and interoperability connecting social determinants of health and health outcomes; (2) include diverse, nontechnical voices in AI and health discussions; (3) fund research on data equity and AI in health sciences; (4) modernize the Health Insurance Portability and Accountability Act (HIPAA) with new guidelines for AI and big data; and (5) research and conceptual frameworks are needed to elucidate interconnections between data equity and health equity.

The adoption of new technologies and data modernization approaches in public health aims to enhance the use of health data to inform decision-making and improve population health. However, public health departments struggle with legacy systems, siloed data, and privacy concerns, which hamper the adoption of new technology and data sharing with stakeholders. This paper maps how to address these shortcomings by identifying data modernization challenges, initiatives, and progress. This study aims to characterize evidence for data modernization-associated gaps and best practices in public health. This scoping review was conducted using the 5-stage framework developed by Arksey and O'Malley and was reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. A structured search was performed in databases PubMed, Scopus, CINAHL, and PsycINFO, and was complemented by a further search in the Google Scholar search engine, covering publications from January 1, 2019, to April 30, 2024. Eligible studies were peer-reviewed, published in English, and focused on data modernization initiatives within US public health system and reported on best practices, challenges, and outcomes. Search terms combined concepts such as \"Data Modernization,\" \"Interoperability,\" and \"Public Health\" using Boolean operators. Two reviewers independently screened titles, abstracts, and full texts using Rayyan QCRI, with conflicts resolved through consultation with a third reviewer. Data were extracted into Microsoft Excel and thematically analyzed. This review analyzed 21 studies focused on public health data modernization. Across the literature, common components included transitioning to cloud-based systems, consolidating fragmented data into unified platforms, applying governance frameworks, and implementing analytics tools to support decision-making. Primary data sources were electronic health records, insurance claims, and disease surveillance registries. Key challenges identified across studies involved data quality issues, lack of interoperability, and limited resources, particularly in underfunded settings. Notable benefits included more timely and accessible data, improved integration across systems, and enhanced analytical capabilities, which collectively support more responsive and effective public health interventions when guided by clear standards and policy alignment. Progress hinges on balancing local adaptability with national coordination, improving data governance practices, and enhancing collaboration across institutions. These steps are vital to ensure that public health systems can deliver timely, accurate, and actionable information to support effective public health efforts.