Journal of Medical Internet Research

Analogous to checklists of recommendations such as the CONSORT statement (for randomized trials), or the QUORUM statement (for systematic reviews), which are designed to ensure the quality of reports in the medical literature, a checklist of recommendations for authors is being presented by the Journal of Medical Internet Research (JMIR) in an effort to ensure complete descriptions of Web-based surveys. Papers on Web-based surveys reported according to the CHERRIES statement will give readers a better understanding of the sample (self-)selection and its possible differences from a "representative" sample. It is hoped that author adherence to the checklist will increase the usefulness of such reports.

BACKGROUND: Many promising technological innovations in health and social care are characterized by nonadoption or abandonment by individuals or by failed attempts to scale up locally, spread distantly, or sustain the innovation long term at the organization or system level. OBJECTIVE: Our objective was to produce an evidence-based, theory-informed, and pragmatic framework to help predict and evaluate the success of a technology-supported health or social care program. METHODS: The study had 2 parallel components: (1) secondary research (hermeneutic systematic review) to identify key domains, and (2) empirical case studies of technology implementation to explore, test, and refine these domains. We studied 6 technology-supported programs-video outpatient consultations, global positioning system tracking for cognitive impairment, pendant alarm services, remote biomarker monitoring for heart failure, care organizing software, and integrated case management via data sharing-using longitudinal ethnography and action research for up to 3 years across more than 20 organizations. Data were collected at micro level (individual technology users), meso level (organizational processes and systems), and macro level (national policy and wider context). Analysis and synthesis was aided by sociotechnically informed theories of individual, organizational, and system change. The draft framework was shared with colleagues who were introducing or evaluating other technology-supported health or care programs and refined in response to feedback. RESULTS: The literature review identified 28 previous technology implementation frameworks, of which 14 had taken a dynamic systems approach (including 2 integrative reviews of previous work). Our empirical dataset consisted of over 400 hours of ethnographic observation, 165 semistructured interviews, and 200 documents. The final nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework included questions in 7 domains: the condition or illness, the technology, the value proposition, the adopter system (comprising professional staff, patient, and lay caregivers), the organization(s), the wider (institutional and societal) context, and the interaction and mutual adaptation between all these domains over time. Our empirical case studies raised a variety of challenges across all 7 domains, each classified as simple (straightforward, predictable, few components), complicated (multiple interacting components or issues), or complex (dynamic, unpredictable, not easily disaggregated into constituent components). Programs characterized by complicatedness proved difficult but not impossible to implement. Those characterized by complexity in multiple NASSS domains rarely, if ever, became mainstreamed. The framework showed promise when applied (both prospectively and retrospectively) to other programs. CONCLUSIONS: Subject to further empirical testing, NASSS could be applied across a range of technological innovations in health and social care. It has several potential uses: (1) to inform the design of a new technology; (2) to identify technological solutions that (perhaps despite policy or industry enthusiasm) have a limited chance of achieving large-scale, sustained adoption; (3) to plan the implementation, scale-up, or rollout of a technology program; and (4) to explain and learn from program failures.

BACKGROUND: Student mental health in higher education has been an increasing concern. The COVID-19 pandemic situation has brought this vulnerable population into renewed focus. OBJECTIVE: Our study aims to conduct a timely assessment of the effects of the COVID-19 pandemic on the mental health of college students. METHODS: We conducted interview surveys with 195 students at a large public university in the United States to understand the effects of the pandemic on their mental health and well-being. The data were analyzed through quantitative and qualitative methods. RESULTS: Of the 195 students, 138 (71%) indicated increased stress and anxiety due to the COVID-19 outbreak. Multiple stressors were identified that contributed to the increased levels of stress, anxiety, and depressive thoughts among students. These included fear and worry about their own health and of their loved ones (177/195, 91% reported negative impacts of the pandemic), difficulty in concentrating (173/195, 89%), disruptions to sleeping patterns (168/195, 86%), decreased social interactions due to physical distancing (167/195, 86%), and increased concerns on academic performance (159/195, 82%). To cope with stress and anxiety, participants have sought support from others and helped themselves by adopting either negative or positive coping mechanisms. CONCLUSIONS: Due to the long-lasting pandemic situation and onerous measures such as lockdown and stay-at-home orders, the COVID-19 pandemic brings negative impacts on higher education. The findings of our study highlight the urgent need to develop interventions and preventive strategies to address the mental health of college students.

BACKGROUND: Electronic health resources are helpful only when people are able to use them, yet there remain few tools available to assess consumers' capacity for engaging in eHealth. Over 40% of US and Canadian adults have low basic literacy levels, suggesting that eHealth resources are likely to be inaccessible to large segments of the population. Using information technology for health requires eHealth literacy-the ability to read, use computers, search for information, understand health information, and put it into context. The eHealth Literacy Scale (eHEALS) was designed (1) to assess consumers' perceived skills at using information technology for health and (2) to aid in determining the fit between eHealth programs and consumers. OBJECTIVES: The eHEALS is an 8-item measure of eHealth literacy developed to measure consumers' combined knowledge, comfort, and perceived skills at finding, evaluating, and applying electronic health information to health problems. The objective of the study was to psychometrically evaluate the properties of the eHEALS within a population context. A youth population was chosen as the focus for the initial development primarily because they have high levels of eHealth use and familiarity with information technology tools. METHODS: Data were collected at baseline, post-intervention, and 3- and 6-month follow-up using control group data as part of a single session, randomized intervention trial evaluating Web-based eHealth programs. Scale reliability was tested using item analysis for internal consistency (coefficient alpha) and test-retest reliability estimates. Principal components factor analysis was used to determine the theoretical fit of the measures with the data. RESULTS: A total of 664 participants (370 boys; 294 girls) aged 13 to 21 (mean = 14.95; SD = 1.24) completed the eHEALS at four time points over 6 months. Item analysis was performed on the 8-item scale at baseline, producing a tight fitting scale with alpha = .88. Item-scale correlations ranged from r = .51 to .76. Test-retest reliability showed modest stability over time from baseline to 6-month follow-up (r = .68 to .40). Principal components analysis produced a single factor solution (56% of variance). Factor loadings ranged from .60 to .84 among the 8 items. CONCLUSIONS: The eHEALS reliably and consistently captures the eHealth literacy concept in repeated administrations, showing promise as tool for assessing consumer comfort and skill in using information technology for health. Within a clinical environment, the eHEALS has the potential to serve as a means of identifying those who may or may not benefit from referrals to an eHealth intervention or resource. Further research needs to examine the applicability of the eHEALS to other populations and settings while exploring the relationship between eHealth literacy and health care outcomes.

Electronic health tools provide little value if the intended users lack the skills to effectively engage them. With nearly half the adult population in the United States and Canada having literacy levels below what is needed to fully engage in an information-rich society, the implications for using information technology to promote health and aid in health care, or for eHealth, are considerable. Engaging with eHealth requires a skill set, or literacy, of its own. The concept of eHealth literacy is introduced and defined as the ability to seek, find, understand, and appraise health information from electronic sources and apply the knowledge gained to addressing or solving a health problem. In this paper, a model of eHealth literacy is introduced, comprised of multiple literacy types, including an outline of a set of fundamental skills consumers require to derive direct benefits from eHealth. A profile of each literacy type with examples of the problems patient-clients might present is provided along with a resource list to aid health practitioners in supporting literacy improvement with their patient-clients across each domain. Facets of the model are illustrated through a set of clinical cases to demonstrate how health practitioners can address eHealth literacy issues in clinical or public health practice. Potential future applications of the model are discussed.

BACKGROUND: The Internet is increasingly used as a medium for the delivery of interventions designed to promote health behavior change. However, reviews of these interventions to date have not systematically identified intervention characteristics and linked these to effectiveness. OBJECTIVES: The present review sought to capitalize on recently published coding frames for assessing use of theory and behavior change techniques to investigate which characteristics of Internet-based interventions best promote health behavior change. In addition, we wanted to develop a novel coding scheme for assessing mode of delivery in Internet-based interventions and also to link different modes to effect sizes. METHODS: We conducted a computerized search of the databases indexed by ISI Web of Knowledge (including BIOSIS Previews and Medline) between 2000 and 2008. Studies were included if (1) the primary components of the intervention were delivered via the Internet, (2) participants were randomly assigned to conditions, and (3) a measure of behavior related to health was taken after the intervention. RESULTS: We found 85 studies that satisfied the inclusion criteria, providing a total sample size of 43,236 participants. On average, interventions had a statistically small but significant effect on health-related behavior (d(+) = 0.16, 95% CI 0.09 to 0.23). More extensive use of theory was associated with increases in effect size (P = .049), and, in particular, interventions based on the theory of planned behavior tended to have substantial effects on behavior (d(+) = 0.36, 95% CI 0.15 to 0.56). Interventions that incorporated more behavior change techniques also tended to have larger effects compared to interventions that incorporated fewer techniques (P < .001). Finally, the effectiveness of Internet-based interventions was enhanced by the use of additional methods of communicating with participants, especially the use of short message service (SMS), or text, messages. CONCLUSIONS: The review provides a framework for the development of a science of Internet-based interventions, and our findings provide a rationale for investing in more intensive theory-based interventions that incorporate multiple behavior change techniques and modes of delivery.

BACKGROUND: There is currently a lack of information about the uses, benefits, and limitations of social media for health communication among the general public, patients, and health professionals from primary research. OBJECTIVE: To review the current published literature to identify the uses, benefits, and limitations of social media for health communication among the general public, patients, and health professionals, and identify current gaps in the literature to provide recommendations for future health communication research. METHODS: This paper is a review using a systematic approach. A systematic search of the literature was conducted using nine electronic databases and manual searches to locate peer-reviewed studies published between January 2002 and February 2012. RESULTS: The search identified 98 original research studies that included the uses, benefits, and/or limitations of social media for health communication among the general public, patients, and health professionals. The methodological quality of the studies assessed using the Downs and Black instrument was low; this was mainly due to the fact that the vast majority of the studies in this review included limited methodologies and was mainly exploratory and descriptive in nature. Seven main uses of social media for health communication were identified, including focusing on increasing interactions with others, and facilitating, sharing, and obtaining health messages. The six key overarching benefits were identified as (1) increased interactions with others, (2) more available, shared, and tailored information, (3) increased accessibility and widening access to health information, (4) peer/social/emotional support, (5) public health surveillance, and (6) potential to influence health policy. Twelve limitations were identified, primarily consisting of quality concerns and lack of reliability, confidentiality, and privacy. CONCLUSIONS: Social media brings a new dimension to health care as it offers a medium to be used by the public, patients, and health professionals to communicate about health issues with the possibility of potentially improving health outcomes. Social media is a powerful tool, which offers collaboration between users and is a social interaction mechanism for a range of individuals. Although there are several benefits to the use of social media for health communication, the information exchanged needs to be monitored for quality and reliability, and the users' confidentiality and privacy need to be maintained. Eight gaps in the literature and key recommendations for future health communication research were provided. Examples of these recommendations include the need to determine the relative effectiveness of different types of social media for health communication using randomized control trials and to explore potential mechanisms for monitoring and enhancing the quality and reliability of health communication using social media. Further robust and comprehensive evaluation and review, using a range of methodologies, are required to establish whether social media improves health communication practice both in the short and long terms.

In an ongoing effort of this Journal to develop and further the theories, models, and best practices around eHealth research, this paper argues for the need for a "science of attrition", that is, a need to develop models for discontinuation of eHealth applications and the related phenomenon of participants dropping out of eHealth trials. What I call "law of attrition" here is the observation that in any eHealth trial a substantial proportion of users drop out before completion or stop using the application. This feature of eHealth trials is a distinct characteristic compared to, for example, drug trials. The traditional clinical trial and evidence-based medicine paradigm stipulates that high dropout rates make trials less believable. Consequently eHealth researchers tend to gloss over high dropout rates, or not to publish their study results at all, as they see their studies as failures. However, for many eHealth trials, in particular those conducted on the Internet and in particular with self-help applications, high dropout rates may be a natural and typical feature. Usage metrics and determinants of attrition should be highlighted, measured, analyzed, and discussed. This also includes analyzing and reporting the characteristics of the subpopulation for which the application eventually "works", ie, those who stay in the trial and use it. For the question of what works and what does not, such attrition measures are as important to report as pure efficacy measures from intention-to-treat (ITT) analyses. In cases of high dropout rates efficacy measures underestimate the impact of an application on a population which continues to use it. Methods of analyzing attrition curves can be drawn from survival analysis methods, eg, the Kaplan-Meier analysis and proportional hazards regression analysis (Cox model). Measures to be reported include the relative risk of dropping out or of stopping the use of an application, as well as a "usage half-life", and prediction models reporting demographic usage discontinuation in a population. Differential dropout or usage rates between two interventions could be a standard metric for the "usability efficacy" of a system. A "run-in and withdrawal" trial design is suggested as a methodological innovation for Internet-based trials with a high number of initial dropouts/nonusers and a stable group of hardcore users.

Everybody talks about e-health these days, but few people have come up with clear definition of this comparatively term. Barely in use before 1999, this term now seems to serve as general buzzword, used to characterize not only medicine, but also virtually everything related to computers and medicine. The term was apparently first used by industry leaders and marketing people rather than academics. They created and used this term in line with other e-words such as e-commerce, e-business, e-solutions, and so on, in an attempt to convey the promises, principles, excitement (and hype) around e-commerce (electronic commerce) to the health arena, and to give an account of the possibilities the Internet is opening up to the area of health Intel, for example, referred to e-health as a concerted effort undertaken by leaders in health care and hi-tech industries to fully harness the benefits available through convergence of the Internet and health care. Because the Internet created opportunities and challenges to the traditional health care information technology industry, the use of term to address these issues seemed appropriate. These new challenges for the health care information technology industry were mainly (1) the capability of consumers to interact with their systems online (B2C = business to consumer); (2) improved possibilities for institutionto-institution transmissions of data (B2B = business to business); (3) possibilities for peerto-peer communication of consumers (C2C = consumer to consumer).

BACKGROUND: Web-based and mobile health interventions (also called "Internet interventions" or "ehealth/mhealth interventions") are tools or treatments, typically behaviorally based, that are operationalized and transformed for delivery via the Internet or mobile platforms. These include electronic tools for patients, informal caregivers, healthy consumers, and health care providers. The "Consolidated Standards of Reporting Trials" (CONSORT) was developed to improve the suboptimal reporting of randomized controlled trials (RCTs). While broadly the CONSORT statement can be applied to provide guidance on how ehealth and mhealth trials should be reported, RCTs of web-based interventions pose very specific issues and challenges, in particular related to reporting sufficient details of the intervention to allow replication and theory-building. OBJECTIVE: To develop a checklist, dubbed CONSORT-EHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile HEalth Applications and onLine TeleHealth), as an extension of the CONSORT statement that provides guidance for authors of ehealth and mhealth interventions. METHODS: A literature review was conducted, followed by a survey among ehealth experts and a workshop. RESULTS: An instrument and checklist was constructed as an extension of the CONSORT statement. The instrument has been adopted by the Journal of Medical Internet Research (JMIR) and authors of ehealth RCTs are required to submit an electronic checklist explaining how they addressed each subitem. CONCLUSIONS: CONSORT-EHEALTH has the potential to improve reporting and provides a basis for evaluating the validity and applicability of ehealth trials. Subitems describing how the intervention should be reported can also be used for non-RCT evaluation reports. As part of the development process, an evaluation component is essential, therefore feedback from authors will be solicited, and a before-after study will evaluate whether reporting has been improved.

Abstract In recent years, remote exercise services (“telerehabilitation”) have been increasingly explored and implemented, including in supportive cancer care, on the promise of greater convenience, access independent of geographic location, reduced infection risk, and cost savings. Ethical commentary on telehealth in general is substantial, but sustained discussion of questions specific to telerehabilitation remains comparatively sparse. As contributors to PREFERBALE-II, a European Union–funded project evaluating live-remote exercise for people recovering from cancer treatment, we sought practical guidance on how to deliver such services responsibly, and found little to work with. This prompted this viewpoint, which sets out our own position as researchers and clinicians engaged in this work. We draw on a purposive sample of professional guidance documents, which were assembled through our professional networks and a supplementary web search, and on a narrative reading of qualitative studies chosen to represent accounts of benefits as well as burdens. We read these documents through an analytical lens of responsible innovation. We make 3 arguments. First, the ethical content of current telerehabilitation guidance is largely confined to the products and processes of innovation, covering safety, privacy, consent, and compliance, while the question of purpose, that is, whether, for whom, and why remote rehabilitation should be offered at all, is left largely unasked. Anticipation appears only implicitly, and reflexivity, inclusion, and responsiveness barely at all. Responsibility is repeatedly delegated to individual professionals without commensurate structural support. Second, the situated experience of users and professionals forms part of the ethics of telerehabilitation and is not merely data to be assessed. Reported benefits and burdens depend on social context, including age, digital literacy, connectivity, caregiver availability, language, and sensory or cognitive impairment, so an equity claim made for the modality in general may not hold for the individual patient. Third, the ethics of telerehabilitation consequently has to be institutionalized across 3 overlapping domains: the interpersonal and experiential quality of remote care, the legal and institutional conditions of delivery, and the positioning of telerehabilitation within health systems. We address these arguments primarily to rehabilitation professionals and the associations and standard-setting bodies that issue guidance for them, and secondarily to digital health and ethical, legal, and social implications researchers, policymakers, and health-system decision-makers who commission or implement such services. We offer an agenda for a broader ethics conversation across professional societies, curricula, and affected publics, together with observations from a consortium put forward as hypotheses for study.

Abstract Background Smart care robots are increasingly viewed as a potential way to address care burden and support care quality in long-term care facilities. However, their integration into routine care practice remains limited, and the conditions required for their effective implementation are not yet well understood from the perspective of frontline nursing assistants. Objective This study aimed to clarify nursing assistants’ core functional expectations of smart care robots in long-term care facilities and to explore the key conditions for their integration into practice. Methods A qualitative descriptive study was conducted. Using purposive sampling, 31 frontline nursing assistants were recruited from 5 long-term care facilities in Chengdu, Sichuan Province, China, with recruitment assistance from facility administrators and eligibility screening by the research team. Semistructured face-to-face interviews were conducted between September and November 2025. Data were analyzed using framework analysis. Results Framework analysis produced 4 analytical domains: real-world care needs, limited facilitating conditions, major anticipated barriers, and preparatory pathways for implementation. Participants expected robots to support safety monitoring and risk warning, standardized checking and management, repetitive physical tasks, and basic interaction. Although some openness to technology and organizational support existed, these facilitating conditions were insufficient for routine implementation. Key barriers included unclear human-robot collaboration models, inadequate organizational capacity, potential new care risks, and weak external support. Participants emphasized organizational assessment, contextual adaptation, pilot testing, and structured training as essential preparatory steps. Emotional support functions were also valued for alleviating caregivers’ emotional burden in high-intensity care work. Conclusions Openness to technology, supportive organizational climates, and positive policy and social signals provided an initial foundation for considering robot integration. However, uncertainties remained around human-robot collaboration, risk management, organizational capacity, and concrete external support. Organizational assessment, contextual adaptation, real-world pilot testing, and practical training warrant further evaluation before wider implementation.

Abstract Background The rise of online mental health communities has reshaped the ways individuals seek and receive mental health support. Although online mental health communities (OMHCs) offer a valuable space for peer support, including through experience-sharing, they also present challenges, including moderation practices to cultivate supportive interactions and secure member safety. No comprehensive framework of moderation practices has been established. Developing such an understanding is important, as moderators play a central role in facilitating a sense of community and determining whether OMHCs function as safe and supportive spaces. Objective This study aimed to develop typologies of moderator practices used to support safety and facilitate a sense of community within OMHCs for people with mental health problems, and the content of OMHC community guidelines. Methods A systematized review of empirical research studies focusing on moderation practices and community guidelines in OMHCs was conducted using MEDLINE, Web of Science, Association for Computing Machinery Digital Library, supplemented with a purposive search to identify gray literature on community guidelines for OMHC moderation. Inclusion criteria: online, text-based component, asynchronous or synchronous moderation, mental health–related focus, and human moderation. Exclusion criteria: crisis-focused, audience-based, nonmoderated or bot-moderated, profession not peer support, non-English, and academic review or thesis. The search period was 2014 to 2024. A bespoke data extraction sheet was piloted, then populated, capturing document, OMHC, member, and moderator characteristics. Thematic analysis was used to synthesize the findings relating to each objective. Results The systematized search identified 11,347 studies and other documents, from which 80 studies were included, comprising 48 empirical papers and 32 gray literature documents. These related to 52 different OMHCs. Four categories of moderator practices were identified: (1) premoderation and postmoderation processes, (2) reactive moderation processes, (3) building the community, and (4) monitoring members. Premoderation and postmoderation processes were used within 41 (79%) of the 52 OMHCs. Three categories of community guideline content were identified: (1) content of post, (2) member conduct, and (3) moderation. The category, content of post, was the most widely used component of community guidelines, used in 35 (67%) of the 52 OMHCs. Conclusions The two typologies provide a foundational framework that can be adapted and applied to community guidelines and moderation strategies across a range of contexts. When operationalized, they could provide benefit to people with mental health problems by using them to guide the development of moderator training, digital tools, and governance policies that align with the specific characteristics and objectives of individual OMHCs. Effective translation and application of these typologies require careful, nuanced consideration of each community’s goals, membership, and cultural context to ensure their relevance, effectiveness, and impact.

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BACKGROUND: Patient access to their electronic health records (EHRs) could improve care and reduce errors by enabling patient contribution to documentation. However, interactive functions that support such engagement remain underresearched. OBJECTIVE: The purpose of this study was to analyze patients' use of the free-text comment function in the Swedish national patient-accessible EHR. METHODS: Comments made by patients between 2015 and 2021 for health care professionals to view were retrieved from the patient-accessible EHR administration service in Sweden. The number and length of comments were analyzed using descriptive statistics and chi-square tests, while the content was examined qualitatively using inductive content analysis. In addition, comments were coded for sentiment (positive, neutral, or negative) and note content, and users were classified according to whether they had submitted at least 1 comment referring to mental health care. RESULTS: A total of 1670 comments were left by 659 patients, of which 15 had irrelevant content (15/1670, 0.9%; 95% CI 0.4%-1.4%), leaving 1655 comments for analysis. Most comments (948/1655, 57.3%; 95% CI 54.9%-59.7%) were related to matters other than note content, and nearly all had a neutral (920/1655, 55.6%; 95% CI 53.2%-58.0%) or negative (702/1655, 42.4%; 95% CI 40.0%-44.8%) sentiment. Patients who submitted a comment referencing mental health care tended to leave more comments overall (median 7 vs 2 comments per patient), and their comments were more frequently negative than comments written by other patients (313/495, 63.2% vs 389/1160, 33.5%; P<.001). The most prevalent categories were care engagement, perceived inaccuracies, complaints, and requests. CONCLUSIONS: Patient engagement with clinical notes has the potential to enhance documentation accuracy and strengthen patient-clinician communication. To realize these benefits safely, such features must be deliberately designed and clearly explained to users to avoid patient safety risks and unnecessary burdens on health care professionals.

Abstract Postpartum psychosis is an underrecognized condition that can have tragic consequences without intervention. In this News and Perspectives article, JMIR Correspondent Benedette Cuffari reports on how technology could support earlier identification and treatment.

Abstract Many clinicians and health care administrators are happy to use AI tools, using them to save precious time in their overburdened work days—but how do patients feel? In this News and Perspectives article, JMIR Correspondent Jenna Congdon reports on patient perspectives on AI-drafted communications in health care settings and what this means for maintaining patient trust moving forward.

Abstract Background Engagement with suicide-related content online is often assumed to be harmful, yet many individuals with lived experience describe such content as supportive or even life-preserving. Despite policy and clinical concerns, little is known about how people intentionally or unintentionally encounter this content, how they interpret it, and how these experiences shape perceived harm or benefit. Objective This study aims to explore how intent and context influence the ways in which individuals with lived experience of suicidality encounter, interpret, and respond to suicide-related online content, and to identify factors associated with perceived harm and benefit. Methods An exploratory cross-sectional survey was conducted with a convenience sample of 713 UK-based participants aged 16 to 60 years and older, 95.9% (n=684) of whom reported having lived experience of suicidality. Quantitative data were analyzed using descriptive and inferential statistics, including chi-square tests, to examine associations between engagement patterns, intent, and suicidality. Free-text responses were coded using content analysis to identify categories relating to motivations for engagement, emotional responses, and perceived impact. Results Participants reported a broad range of experiences. While 30.0% (214/713) described suicide-related content as contributing to suicidal thoughts or attempts, others reported relief, validation, or reduced feelings of isolation, with 21.0% (150/713) attributing content to preventing an attempt. Clear differences emerged between participants who actively sought out content (“seekers”) and those who encountered it unintentionally (“stumblers”). Seekers more frequently reported positive or understanding responses, often in the context of unmet offline support needs, whereas stumblers were more likely to report shock or distress. Approximately 39.9% (151/378) of participants described their engagement as neither intentional nor accidental, potentially reflecting a blurred boundary between passive exposure and active seeking, as well as the difficulty individuals face in accurately identifying and articulating their own motivations in relation to suicidality. Conclusions Engagement with suicide-related online content cannot be characterized as uniformly harmful. Outcomes depend on user intent, emotional state, and the context of engagement, and they may change over time. Clinical, regulatory, and policy approaches should move beyond risk-only frameworks to account for the nuanced and sometimes supportive roles such content plays in how individuals make sense of suicidal thoughts, while still protecting those at risk.

Unlabelled: Pediatric health care systems face increasing uncertainty driven by rapid technological change, workforce shifts and shortages, environmental threats, and evolving family and community needs. Pediatrics requires future-focused considerations that go beyond adult systems, including family-centered care, developmental trajectories, and long-term societal impacts on child well-being. While the future cannot be predicted, futures medicine methods offer structured approaches to anticipate change and support proactive, values-driven planning. This Viewpoint describes the application of pediatric futures methods within an academic, independent nonprofit pediatric health system and proposes a practical framework that connects future readiness to strategic decision-making and daily care delivery. Futures medicine methods are not new in pediatric health care but may not be visible from the front line of care delivery, which provides an opportunity for clinical, operational, administrative, and enterprise leaders to implement these methods in their workflows. This paper introduces the discover to deliver pediatric futures framework, a continuum that links long-range futures discovery to near-term strategic planning, operational design, implementation, and outcome demonstration, emphasizing future methods and activities for applicable use.

Background: Conventional triage relies on clinician judgment and limited information available at first contact. AI and machine learning (ML) may improve early risk stratification, whereas tele-triage and patient-facing tools primarily support remote assessment, access, and care routing. Objective: This study aimed to evaluate the diagnostic and prognostic performance of AI- and ML-assisted triage systems and summarize evidence on tele-triage, workflow, implementation, economic, and equity outcomes. Methods: Comparative studies published between January 1, 2010, and December 31, 2025, evaluating AI-, ML-, natural language processing-, telehealth-, or digitally enhanced triage. At first, clinical contacts were included. PubMed/MEDLINE, CINAHL, Web of Science, Scopus, preprint servers, trial registries, and conference proceedings were searched. Screening and data extraction were performed independently in duplicate. Risk of bias was assessed using design-appropriate tools. Clinically comparable area under the receiver operating characteristic curve (AUC) estimates were synthesized using random-effects meta-analysis; heterogeneous diagnostic, operational, implementation, economic, and equity outcomes were summarized narratively. Exploratory scenario modeling projected effects per 10,000 emergency department (ED) visits. Results: ²=94.4%; 95% prediction interval 0.803-0.922). Four studies reported directly extractable within-study comparisons favoring AI-assisted models (AUC differences of 0.097, 0.120, 0.108, and 0.065), although these could not be pooled because paired variance estimates were unavailable. Admission/disposition studies reported AI-model AUCs ranging from 0.80 to 0.93 and were summarized descriptively because extreme heterogeneity precluded meaningful pooling. Two pediatric studies (AUCs of 0.85-0.99) were summarized descriptively. Prediction-model studies were primarily limited by deficiencies in model analysis, calibration reporting, and external validation. Narrative evidence suggested improved front-door operational metrics with tele-triage, including reported reductions of 15%-40% in time to clinical assessment and 2-8 percentage points in left-without-being-seen rates, whereas patient-facing symptom checkers generally favored sensitivity over specificity. Exploratory scenario modeling illustrated that, under specified prevalence and threshold assumptions, AI-assisted triage could be associated with 12-38 fewer missed critical illness cases and 45-120 additional false-positive escalations per 10,000 ED visits. Economic and equity findings were assumption-dependent projections rather than observed clinical, cost-effectiveness, or equity outcomes. Conclusions: AI- and ML-assisted triage models demonstrate promising discrimination for adult ED risk stratification; however, AUC-based discrimination does not establish superiority at clinically relevant decision thresholds, improved patient outcomes, or readiness for clinical implementation. Interpretation is limited by substantial heterogeneity, incomplete threshold-level performance and calibration reporting, limited external validation, and sparse pediatric evidence. Prospective multicenter validation, workflow assessment, subgroup calibration and equity auditing, and formal economic evaluation are required before routine implementation.