Published in BMC Health Services Research , the review followed PRISMA 2020 guidelines and was registered with PROSPERO under the identifier CRD42024572294. The authors described inclusive implementation, tailored delivery of e-health applications, and support in using e-health as elements that could help address the digital divide between people with low and high socio-economic positions. For the systematic search, researchers used five databases: PsycINFO, Web of Science, Embase, PubMed and Sociological Abstracts.

Search terms were built around three core concepts: socio-economic position, e-health and delivery strategy. The search produced 5,127 papers after duplicates were removed, from which the researchers included 35 papers describing 33 studies. To compare the varied approaches, the review classified delivery strategies into five components: place, point of contact, promotion, facilitation and incentive.

The analysis showed that almost all identified strategies combined multiple components. Place and point-of-contact strategies were most frequently applied and often used together. These findings concern the delivery and reach of digital health rather than the clinical effectiveness of the e-health applications themselves.

The review therefore does not establish that any application improved health outcomes, nor does it demonstrate that one delivery strategy consistently outperformed another. The authors described the findings as insights into potentially effective approaches rather than conclusive proof of a preferred method. Reported design underpinnings were limited.

Stakeholder involvement was identified as an applied principle for implementation-strategy design, together with the identification and operationalization of specific delivery strategies. However, the studies rarely explained whether these strategies were based on established theory or accumulated experience. Overall, the review identifies a gap in evidence about effective e-health delivery for people with a low socio-economic position.

Its findings show that digital health delivery commonly depends on combinations of place, contact, promotion, facilitation and incentives, with place and point of contact appearing most often. The limited and dispersed evidence indicates potential approaches, but definitive conclusions require stronger evaluations of delivery strategies in real-world settings.

Subject of Research: Delivery strategies and reported reach of digital health for people with a low socio-economic position

Article Title: A systematic literature review to identify delivery strategies of digital health for low SEP populations

Article References: Bartha Standaar, L. M., van der Vaart, R., Leenaars, K., Friele, R. D., & van Tuyl, L. H. D. (2026). A systematic literature review to identify delivery strategies of digital health for low SEP populations. BMC Health Services Research. https://doi.org/10.1186/s12913-026-15442-w

Image Credits: AI Generated

DOI: 10.1186/s12913-026-15442-w

Keywords: digital health, e-health, health inequalities, socio-economic position, systematic review, digital divide, implementation, health services, stakeholder involvement, literature review, systematic, literature

The review, led by Fang Li and Xiaoyu Liu of the Sichuan Cancer Hospital and Institute in Chengdu, China, together with colleagues across Sichuan and the China Medical University School of Nursing in Shenyang, took an unusually rigorous approach for a scoping review. Rather than simply cataloguing studies, the team mapped the entire landscape of digital symptom management interventions for patients in the peri-chemoradiotherapy period, meaning before, during and shortly after concurrent chemoradiation, and then interrogated that landscape through two established theoretical lenses: the United Kingdom Medical Research Council framework for complex interventions and the Capability-Opportunity-Motivation-Behaviour model of behaviour change.

The Medical Research Council framework matters because digital symptom management is not a simple drug or device. It is what researchers call a complex intervention, a bundle of interacting components, including symptom assessment tools, educational content, tailored feedback, remote monitoring and communication channels with clinicians, delivered through technology to patients whose physical condition and motivation fluctuate week by week. The framework forces developers to think systematically about how an intervention is built, how its mechanisms are supposed to work, how it will be implemented in real clinics and how it will be evaluated. The COM-B model complements this by asking whether patients truly have the capability, opportunity and motivation to use these tools consistently, which is precisely where many digital health projects quietly fail.

To assemble the evidence base, the researchers followed the Joanna Briggs Institute methodology and the PRISMA extension for scoping reviews, searching six major electronic databases, PubMed, Scopus, Embase, the Cochrane Library, Web of Science and CINAHL, along with grey literature sources, from inception to January 2026. The search captured 32 studies, a mix dominated by development and validation work rather than definitive trials. Most of the research came from high-income countries, a pattern the authors highlight as a signal of how unevenly this technology is being studied and deployed across the world, leaving patients in low- and middle-income settings largely outside the evidence base.

One of the review’s most useful contributions is its taxonomy of delivery. The digital interventions identified fell into seven distinct modalities, with dedicated programmes and websites and mobile applications emerging as the most common vehicles. The application scenarios the authors mapped are intuitive once spelled out: patients use these platforms to report symptoms in real time, receive education about what to expect from radiation to the head and neck, practise swallowing or mouth-care exercises, track nutritional intake, and in some cases trigger alerts to clinical teams when scores deteriorate. In principle, this closes a notorious gap in oncology care, where symptoms between hospital visits can escalate unnoticed until they become severe enough to require unplanned admissions or treatment interruptions.

The technical logic behind such tools is worth unpacking. Symptom tracking apps typically rely on validated patient-reported outcome measures, short questionnaires that patients complete on a phone or computer, converting subjective experience into quantifiable data streams. When those streams are monitored, clinicians can intervene earlier, adjusting pain medication, prescribing treatments for oral mucositis or arranging dietetic support before a patient becomes malnourished. Educational modules can be timed to the radiation schedule, delivering information about skin care or swallowing exercises precisely when they become relevant. Some platforms incorporate automated feedback, reminders and tailored coaching, which is where the COM-B model becomes directly operational: a reminder addresses motivation, a simplified interface addresses capability, and integration with hospital systems addresses opportunity.

Yet the review’s central finding is a sobering one, and it is captured in a phrase the authors use explicitly: the engagement-impact gap. Digital interventions in this field are frequently hindered by poorly sustained user engagement and insufficient clinical application. Patients download an app, use it enthusiastically for a week or two, and then drift away, often exactly when treatment side effects peak and energy for self-management collapses. Clinics pilot a platform and then struggle to embed it into routine workflows, funding structures and electronic health records. The result is a graveyard of well-intentioned prototypes that showed early promise but never achieved the sustained, integrated use required to change outcomes at scale.

By applying the Medical Research Council framework and the COM-B model to the collected evidence, the authors identified core implementation barriers and generated tentative, theory-informed proposals for optimising future interventions. These propositions, the team is careful to stress, are hypothesis-generating rather than proven; they lack direct validation from primary research. Still, the framework-based analysis gives the field something it has often lacked, a structured way to diagnose why a given intervention failed. Was the content too demanding for a fatigued patient with mouth pain, a capability problem? Did the app assume smartphone access or digital literacy the patient did not have, an opportunity problem? Did the feedback feel impersonal, eroding motivation? Each diagnosis points to a different fix.

What can clinicians and patients conclude today? The review’s verdict is that preliminary evidence indicates digital modalities may improve symptom control, but that most of the 32 included studies were limited by small sample sizes and non-controlled designs. In other words, the field is rich in prototypes and poor in proof. Without randomised, adequately powered, well-controlled trials, it remains impossible to say with confidence that any particular digital intervention improves swallowing outcomes, reduces hospital admissions or extends treatment completion rates compared with usual care. The authors are explicit that firm conclusions about effectiveness must wait for such trials, and their framework-based roadmap is intended to help designers build interventions robust enough to survive that testing.

The significance of this work extends beyond head and neck cancer. The peri-chemoradiotherapy period is arguably the hardest possible test case for digital symptom management, because it combines an intense, time-limited symptom trajectory with patients who are often exhausted, dehydrated and in pain. If engagement strategies can be engineered to work here, grounded in behaviour-change theory and developed through the staged, iterative process the Medical Research Council framework prescribes, the same principles should transfer to other cancer populations and chronic illnesses. For now, the message of the review is one of cautious optimism: the technology exists, the early signals are promising, and the next decade of work must shift from building apps to proving, with rigorous trials, that they genuinely lighten the burden borne by patients in the treatment chair.

Subject of Research: Digital health interventions for symptom management in head and neck cancer patients undergoing chemoradiotherapy

Article Title: Digital symptom management interventions for patients with head and neck cancer during peri-chemoradiotherapy: a scoping review using the UK Medical Research Council Framework

Article References: Li, F., Liu, X., Yin, L., Yang, Q., Zhang, H., Jiang, Q., Zhang, S., Wang, A., & Lyu, J. (2026). Digital symptom management interventions for patients with head and neck cancer during peri-chemoradiotherapy: a scoping review using the UK Medical Research Council Framework. BMC Health Services Research. https://doi.org/10.1186/s12913-026-15667-9

Image Credits: AI Generated

DOI: 10.1186/s12913-026-15667-9

Keywords: head and neck cancer, digital health interventions, symptom management, chemoradiotherapy, scoping review, Medical Research Council framework, COM-B model, patient-reported outcomes, mobile health, engagement-impact gap, oncology nursing, e-health

The research, published in Discover Artificial Intelligence, comes from Bo Xie and Ruoke Zhang, who analyzed their own experience running the Nursing AI Studio, a year-round innovation incubator created to help frontline health professionals turn real-world problems into governable AI tools. The team conducted a retrospective comparative case study of four AI-powered web applications developed through the studio between spring 2025 and spring 2026: a clinical evaluation tool for nursing education, a tailored support tool for dementia caregivers, a patient simulation tool for nursing students, and a statistical assistance tool for large-scale biomedical and social-behavioral datasets. The first three served internal university users and were built with the help of a single graduate research assistant in computer science; the fourth was an NIH-funded project involving researchers at two universities.

The authors defined deployment burden as the organizational, technical, and coordination work required to move a locally developed functional AI-powered web application into legitimate institutional use. They operationalized it across five dimensions: temporal burden measured in business days, pathway-discovery burden reflected in gaps and uncertainty about the next responsible office, dependency burden from steps that could not proceed until prior approvals were complete, enterprise integration burden covering authentication and security review, and collaboration-context burden arising from external collaborators or changing enterprise requirements. Data came from structured deployment logs and dated operational records including email confirmations, approval notifications, and internal deployment notes.

The results reveal a striking pattern. Across the three internal tools, early infrastructure steps were consistently fast and stable: obtaining cloud storage access took one business day, subdomain setup took three, and enterprise AI licenses and Information Security Office review were completed in one to two business days. Enterprise integration, by contrast, was the longest and most variable stage: six business days for the first tool, seventeen for the second, twenty-five for the third, and more than three months for the cross-institutional project. These numbers suggest that early provisioning was not the primary bottleneck. Instead, the pathway slowed most when the application needed deeper enterprise handling around authentication, institutional access, or both.

Perhaps the most revealing finding concerns what the authors call pathway-discovery burden. For the first tool, approximately two weeks separated AI-license approval on October 17, 2025, from initiation of the cloud storage request on October 31, 2025, and another roughly two weeks separated cloud storage setup from initiation of the subdomain request on November 17, 2025. These lapses were not periods of team inaction but time spent trying to discover what the next institutional step was, whom to contact, and whether any prerequisite existed. Later experience suggested that acquiring an enterprise AI license did not actually need to precede cloud storage setup, meaning at least part of the delay reflected an initially misread sequence rather than an unavoidable dependency.

Some burdens proved reusable. Once an enterprise AI license and a workable internal path were established, later internal tools did not require a new AI license request. Cloud storage access and subdomain setup became standardized enough to function as reusable assets. However, repetition did not eliminate all obstacles. The third tool showed that prior experience still did not prevent substantial delays when downstream credential and integration issues arose. The first tool’s integration was also expedited through a deliberate administrative intervention linked to an existing memorandum of understanding with the Chief Information Officer’s office, meaning it should not be treated as a neutral baseline.

The fourth tool introduced a qualitatively different problem. Although it remained a health-related research tool, this NIH-funded project involved collaborators from two universities, so the challenge was no longer only how to move through one institution’s internal pathway. It became a cross-institutional access problem involving AI and computing resources, control of deployment infrastructure, and uncertainty about how external personnel could participate in building a tool that would ultimately live under the lead institution’s domain. The case suggests that a pathway workable for internal personnel can become fragile when collaboration crosses institutional boundaries.

The pathway also proved dynamic. During the period in which the manuscript was being prepared, an additional Information Security Office compliance requirement emerged for this workstream, underscoring that the deployment pathway continues to change even after teams believe they have learned it. Deployment burden is therefore not only a steep first-team learning curve but also an ongoing maintenance burden of keeping up with a moving enterprise and compliance environment. The authors argue that deployment knowledge cannot be treated as a one-time checklist that each team learns and keeps forever.

The practical implication is not that institutions should lower standards or bypass governance. Rather, they should stop forcing each AI builder team to rediscover the same pathway independently. The authors propose adapting a studio-like model across campus for clinically relevant AI projects in the health and social sciences: a small, shared service could help teams scope projects early, identify needed collaborators and expertise, maintain reusable templates and accounts, interpret sequencing, and escalate complex integration issues. They also recommend a staged pathway that pre-provisions what can be reused, allows controlled web deployment and pilot use when appropriate, and escalates to deeper enterprise integrations only when their value justifies the added burden.

For prospective researchers and frontline health professionals, the lesson is that internally developed tools are not equivalent to creating a custom GPT or configuring a commercial chatbot for personal use. Meaningful institutional deployment requires early decisions about collaborators, access, hosting, authentication, review, maintenance, and escalation points. The study has limitations: the evidence base was brief and retrospective, the sample was small, the cases arose in one institutional environment, and the authors were participant-observers. The tools were clinically relevant but not bedside clinical decision-support systems, so the pathway may differ for higher-risk applications. Still, the findings offer a practice-informed map that similar academic health settings can examine in their own infrastructure, making visible a hidden form of implementation waste that is easy to miss if one looks only at formal review times.

Subject of Research: Deployment pathways and organizational burden for internally developed AI tools in clinical education and health research

Article Title: Analyzing and redesigning deployment pathways for internally developed artificial intelligence tools in clinical education and health research

Article References: Xie, B., & Zhang, R. (2026). Analyzing and redesigning deployment pathways for internally developed artificial intelligence tools in clinical education and health research. Discover Artificial Intelligence, 6(1), Article 1240. https://doi.org/10.1007/s44163-026-02220-0

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02220-0

Keywords: artificial intelligence, health informatics, clinical education, enterprise integration, deployment burden, Nursing AI Studio, information security review, cross-institutional collaboration, health AI governance, implementation science, academic health research, e-health

The scale of the problem is staggering. According to the Global Burden of Disease Study, the prevalence of overweight among children and adolescents doubled between 1990 and 2021, while obesity prevalence tripled over the same period. Forecasts indicate that global obesity prevalence in young people will continue rising through 2025 across all world regions. In Germany, national surveillance data show that overweight and obesity prevalence has remained relatively stable in recent decades, but the COVID-19 pandemic triggered a marked increase in severe obesity, particularly among older children and adolescents. This is alarming because pediatric obesity carries elevated risks for type 2 diabetes, hypertension, asthma, joint pain, impaired mobility, low self-esteem, depressive symptoms, and reduced quality of life. Given that childhood obesity frequently persists into adulthood, early intervention is essential.

Existing treatments have shown only modest success. Multicomponent family-focused interventions combining nutritional guidance, physical activity promotion, and behavioral strategies remain the best-established approach, but systematic reviews demonstrate only short-term improvements in body mass index (BMI) z-scores, with limited long-term evidence. Evidence suggests that high-intensity treatments requiring at least 26 contact hours are most effective, yet such intensity is rarely feasible in routine care. Meanwhile, newer pharmacological options such as glucagon-like peptide-1 analogs liraglutide and semaglutide have shown promise in children and adolescents, but evidence from adults indicates that stopping these medications often leads to rapid weight regain. This underscores the need for sustainable lifestyle-based strategies embedded in everyday life. Pediatric practices are the professional environment parents most prefer for guidance on their child’s weight, and e-health tools such as mobile applications and web-based platforms may further facilitate the transfer of clinical advice into daily routines.

STARKIDS was designed as a step-wise, tailored, e-health supported multicomponent family-focused intervention program, the first of its kind to be implemented in pediatric primary care in Germany and evaluated against optimized treatment-as-usual in a cluster-randomized trial. The researchers enrolled children and adolescents aged 3 to 17 years with overweight (BMI above the 90th percentile) or obesity (BMI above the 97th percentile) from pediatric and family practices across the state of Baden-Wuerttemberg. A cluster randomization was chosen to prevent contamination of interventions between practices. Of 89 randomized practices, 71 enrolled families in the study. Between April 2022 and July 2023, 611 families were enrolled, 559 underwent baseline assessments and started the intervention or control condition, and 421 and 422 families were assessed for primary endpoints after 12 and 18 months respectively. Overall dropout was 25 percent, a relatively low attrition rate that compares favorably with rates reported elsewhere in family-based pediatric obesity interventions, which have reached up to 83 percent.

The intervention itself was a carefully structured hybrid program. Families in the STARKIDS group attended individual 90-minute face-to-face sessions every three months over one year, each conducted by pre-trained medical assistants who were part of the families’ usual care teams. The sessions followed the principles of motivational interviewing, a person-centered counseling style designed to reduce ambivalence and strengthen intrinsic motivation for change. Each session covered one of five core modules in sequence: STARKIDS Start, Eating and Drinking, Activity and Media, Family Life, and STARKIDS Keep on, with an optional STARKIDS Joker module addressing barriers and motivational difficulties. Content was individually tailored based on pre-session assessments and family-specific needs, addressing dietary energy density, physical activity enjoyment, media consumption, sleep habits, stress, weight stigmatization, and self-worth. Between sessions, families had continuous access to an accompanying e-health online platform offering educative texts, short films, serious games, reflection tools, and printable tip sheets aligned with the goals agreed upon in each session. Families who consented also received biweekly text message reminders to monitor their individually selected goals and the child’s weight. In the control group, families received a single 30-minute structured counseling session covering the same core topics but limited to guideline-based recommendations without behavioral tools or follow-up.

The primary outcome was the proportion of children achieving a reduction of at least 0.2 BMI z-score, a threshold considered to represent a clinically meaningful difference. After 12 months, 33 percent of participants in the intervention group achieved this criterion compared with 22 percent in the control group, a statistically significant difference with a risk difference of 0.11 (95 percent CI 0.03 to 0.20, p = 0.0256). The analysis of BMI z-score development over time as a continuous outcome showed a greater decrease in the intervention group between baseline and 12 months, with mean BMI z-scores falling from 2.41 to 2.24 in the STARKIDS group versus 2.37 to 2.32 in the control group. However, after 18 months, the advantage disappeared: 30 percent of the intervention group and 28 percent of the control group achieved the criterion, with no statistically significant difference between groups. The researchers attribute this to the fact that the period between 12 and 18 months functioned as a follow-up phase during which the STARKIDS group had access only to the online platform, suggesting that the e-health component alone may not be sufficient to maintain achieved weight reductions without continued personal interaction with the practice team.

For the second primary outcome, health-related quality of life measured with the KINDL questionnaire, no significant differences between groups were detected in either parental reports or adolescent self-reports. Quality of life scores in both groups were lower than in a German reference population, possibly reflecting lingering pandemic effects and the known burden of pediatric obesity on psychological well-being. The parent-reported within-group change in the intervention group corresponded to only 0.14 standard deviations of the baseline score, well below the 0.5 standard deviation threshold for clinical relevance. The researchers note that how the intervention could better address quality of life warrants further investigation.

The baseline characteristics of participants revealed the substantial burden these children already carried. Mean BMI percentile at baseline was 98.5, with 36 percent of participants in the severe obesity range, 47 percent in the obesity range, and only 17 percent in the overweight range. About one third of the sample reported snoring, concentration problems, headaches, knock-knees, or foot deformities. Notably, 17 percent of those who underwent sonographic examination showed indications of metabolic dysfunction-associated steatotic liver disease. The accompanying legal guardians were predominantly mothers with elevated mean BMI falling in the overweight range, consistent with the well-established role of parental overweight as a risk factor for childhood obesity.

Feasibility and satisfaction data were encouraging. Attendance rates at face-to-face sessions ranged from 69 to 92 percent, and engagement with educative texts, tip sheets, and videos on the online platform was good, although mindfulness audios were the least used resource. Overall satisfaction with the program was high among both parents and adolescents, and pediatricians and medical assistants reported high motivation and enjoyment conducting the program with moderate to high feasibility. However, uptake of step 2 of the program, which involved additional targeted counseling sessions delivered by public health services for families whose children had not achieved sufficient BMI z-score reduction, was poor, with only 15 percent of eligible families participating. A qualitative process evaluation suggested that families did not want to burden public health services already strained by the pandemic, did not perceive them as the right setting for further counseling, and saw no additional value in further sessions.

Based on these findings, the researchers propose a revised three-step model: the one-time structured counseling session becomes the new step 1 provided for all eligible patients, only families with insufficient progress after this counseling receive the STARKIDS face-to-face sessions and online platform as an intensification, and the original step 2 is adapted to take place within the pediatric practice with gradually fading contact hours rather than at local health departments. The study’s strengths include its step-by-step approach with prolonged intervention for non-responders, suitability for routine primary care across diverse urban and rural practices, and alignment with current German guideline recommendations. A separately reported health economic evaluation demonstrated cost-effectiveness for the subgroup with obesity, and the German Federal Joint Committee has officially recommended transfer of the counseling step into routine care for this subgroup, supporting the program’s potential for nationwide scaling. The researchers conclude that digitally enhanced and tailored treatment programs for childhood overweight and obesity can be successfully implemented in pediatric primary care settings, achieving moderate and meaningful effects with a low-threshold intervention in routine outpatient care. Future research should address the maintenance of treatment success through gradually reducing contact times instead of abruptly ending interventions, and potentially extend the approach through artificial intelligence systems allowing for further individualization.

Subject of Research: Pediatric obesity

Article Title: Effects of an e-health enhanced multicomponent outpatient intervention for children and adolescents with overweight or obesity in routine primary care (STARKIDS): a cluster-randomized controlled trial

Article References: Ziser, K., Erschens, R., Zipfel, S., Martus, P., Jacoby, J., Herschbach, A., Wels, A., Mack, I., Weiland, A., Krauß, I., Greule, C., Sudeck, G., Thiel, A., Zurstiege, G., Hoell, A., Salize, H. J., Bethge, W., Ums, M., Sammet, T., … Junne, F. (2026). Effects of an e-health enhanced multicomponent outpatient intervention for children and adolescents with overweight or obesity in routine primary care (STARKIDS): a cluster-randomized controlled trial. The Lancet Regional Health – Europe, 71, Article 101897. https://doi.org/10.1016/j.lanepe.2026.101897

Image Credits: AI Generated

DOI: 10.1016/j.lanepe.2026.101897

Keywords: childhood obesity, e-health, primary care, cluster-randomized trial, STARKIDS, BMI z-score, family intervention, motivational interviewing, pediatric practice, digital health, Effects, enhanced

The research team began by assembling a candidate pool of quality indicators drawn from existing evaluation instruments and the published literature on health information quality. This starting library reflected the accumulated wisdom of information science and health communication research, covering familiar concepts such as accuracy, authority, and objectivity. But rather than imposing expert judgments from the top down, the researchers then designed a questionnaire survey aimed squarely at mobile social media users themselves. Their goal was to capture user perception as the organizing principle of the evaluation framework, on the reasoning that an assessment scheme which ignores the expectations of its end users risks being technically sound but practically irrelevant.

Methodologically, the study combined two analytical strategies. First, respondents were asked to rate the importance of each candidate indicator, allowing the researchers to rank the indicators according to user suggestions. Second, the team applied hierarchical clustering analysis, a statistical technique that groups items based on the similarity of their response patterns, to identify natural dimensions of indicator classification. Hierarchical clustering builds a tree-like structure in which each indicator starts as its own cluster and neighboring clusters are merged step by step according to a distance measure, so the final grouping emerges from the data rather than from preconceived categories. The researchers then refined these statistical clusters with professional practice, producing a framework that is both empirically grounded and operationally usable.

The result is a five-dimensional organization of health information quality indicators for mobile social media. Four of the dimensions concern the health information content itself: relevance, timeliness, credibility, and professional value. Relevance captures whether the information matches a user’s actual health situation and needs. Timeliness reflects how current the content is, a critical attribute in a field where guidance evolves rapidly. Credibility speaks to the trustworthiness of the claims and their sources, while professional value addresses whether the content carries genuine clinical or scientific substance rather than superficial wellness talk. The clustering analysis, supplemented by expert input, showed that these content-oriented properties form coherent, distinguishable axes along which users implicitly evaluate what they read on their phones.

Beyond the content dimensions, the framework incorporates platform-level and service-level indicators that reflect the distinctive affordances of mobile social media, such as navigation, personalized recommendation, and the presence or absence of advertising. This is precisely where conventional evaluation criteria fall short. Traditional health information quality tools were built for websites, printed materials, or institutional sources, and they have no natural way to account for algorithmic feeds, recommendation engines, or the commercial pressures embedded in platform design. By grounding the indicator set in user perception, the study ensures that these platform-specific factors receive the same analytical attention as the classical content attributes, producing an evaluation model that matches the environment in which people actually encounter health information.

One of the most practically valuable findings of the study concerns demographic consistency. Subgroup analysis revealed no statistically significant differences across demographic groups in the perceived importance of several key indicators, including authentication, objectivity, author transparency, reference source, and readability. In other words, users across different ages, backgrounds, and population segments appear to agree that knowing who authored a post, whether the author is credentialed, what evidence is cited, and how easily the text can be understood are all matters of shared concern. This consensus simplifies the task of platform designers and regulators, because it identifies a common baseline of quality expectations that can be applied universally without tailoring to specific demographic segments.

The same consistency extended to another cluster of indicators. Users from different groups showed aligned recognition of the importance of navigation, personalized recommendation, content relevance, the absence of advertising, content focus, and the universality of health programs. These indicators describe the experience of finding and consuming health content: whether users can move through information efficiently, whether recommendations match their needs, whether the content stays on topic, and whether commercial distractions interfere with the health message. The demographic agreement on these points suggests that platforms can pursue improvements in these areas with confidence that the changes will be valued broadly across their user base, rather than serving only a narrow slice of the audience.

Not every indicator, however, enjoyed universal endorsement. The study found significant differences among user groups in the perceived importance of multimedia richness, the degree to which health information is presented through varied formats such as video, audio, images, and interactive elements. This divergence is instructive. It implies that the optimal presentation format for health content is not a one-size-fits-all decision: some users may rely heavily on visual and video formats to understand medical concepts, while others may prefer text-based information or may be indifferent to format altogether. For content creators and health communicators, this finding argues for audience-aware production strategies, in which the choice of media format is calibrated to the target population rather than assumed to be universally beneficial.

The broader implications of the work reach into platform governance, health communication practice, and future research. By identifying a structured set of evaluation indicators and organizing them into five dimensions, the researchers have given platform operators, public health agencies, and information quality auditors a user-validated yardstick for assessing the health content that flows through mobile social media. Moreover, the demonstration that perceptions vary partially across user groups leads the authors to a clear conclusion: future optimization of health information quality can be carried out in a targeted manner for different user groups. Rather than applying uniform quality interventions, platforms and health authorities could tailor their efforts, strengthening, for example, credibility signals for all users while adjusting multimedia presentation strategies for specific segments.

The study, conducted in accordance with the Declaration of Helsinki with ethical approval from the Ethics Committee of the Second Xiangya Hospital of Central South University and informed consent from all participants, was supported by grants from the 2023 Key R&D Projects in Hunan Province and a 2023 Teaching Reform Research Project of Ordinary Higher Education Institutions in Hunan Province. As health misinformation continues to spread at the speed of a share button, frameworks like this one shift the conversation from vague alarm about bad information to concrete, measurable criteria grounded in what users themselves expect. The message for the digital health era is clear: quality evaluation must live where the information lives, on the mobile screens of the people it is meant to protect.

Subject of Research: User-perception-based evaluation indicators for health information quality on mobile social media

Article Title: Research on health information quality evaluation indicators for mobile social media based on user perception

Article References: Luo, A., Xu, L., Xu, Q., Nie, Y., Hu, D., Xin, Z., Dai, P., Zhao, X., Pan, Z., & Dai, P. (2026). Research on health information quality evaluation indicators for mobile social media based on user perception. BMC Public Health. https://doi.org/10.1186/s12889-026-29799-4

Image Credits: AI Generated

DOI: 10.1186/s12889-026-29799-4

Keywords: mobile social media, health information quality, evaluation indicators, user perception, public health, health informatics, hierarchical clustering, health communication, e-health, misinformation, personalized recommendation, BMC Public Health

Healthcare-associated infections, commonly abbreviated as HAIs, include some of the most feared complications of modern medicine. Central line-associated bloodstream infections, catheter-associated urinary tract infections, ventilator-associated pneumonia, surgical site infections, and infections caused by multidrug-resistant organisms such as Clostridioides difficile all impose heavy burdens on patients and health systems alike. They prolong hospital stays, drive up costs, fuel antimicrobial resistance, and in severe cases contribute to death. Prevention guidelines for each of these conditions exist and are well supported by clinical evidence, yet adherence in real-world hospital settings remains inconsistent, and the gap between what is recommended and what is practiced has proven remarkably durable.

To understand why, the research team systematically searched three major bibliographic databases, PubMed/MEDLINE, Scopus, and Web of Science, for studies published between January 2010 and December 2025. The search focused on studies that evaluated digital interventions, organizational interventions, or combinations of the two in adult inpatient hospital settings. From the results, the investigators included 47 studies in their analysis. Because the included studies varied widely in design, setting, and outcome measurement, the team performed a structured narrative synthesis rather than a formal meta-analysis, a methodological choice that reflects the genuine heterogeneity of implementation research but also limits the precision of any conclusions that can be drawn.

The synthesis revealed a clear pattern in how the three categories of intervention behaved. Digital interventions primarily supported surveillance and feedback processes. In practice, this means electronic systems that automatically identify infection events, track catheter days, flag deviations from hand hygiene protocols, or deliver timely performance data back to clinicians and managers. Such systems can dramatically expand the reach of infection prevention teams, which are often too small to manually monitor every unit, and can provide feedback loops that keep infection control visible in daily clinical work rather than confined to quarterly reports.

Organizational strategies, by contrast, commonly supported adherence and standardization. These include measures such as care bundles, checklist programs, dedicated infection prevention staffing models, leadership accountability structures, and standardized protocols that specify exactly which practices should occur and when. The logic here is different from the digital approach: rather than generating better information, organizational strategies reshape the clinical environment itself, making the correct behavior the default and reducing the reliance on individual memory or motivation. The review found that these strategies were frequently associated with improved process measures, such as higher compliance with hand hygiene, appropriate device maintenance, and evidence-based insertion practices.

The most encouraging results, however, emerged from hybrid approaches that combined digital and organizational elements. Studies implementing such integrated strategies frequently reported improvements in both process outcomes and infection-related outcomes. This finding aligns with a growing body of implementation science showing that technical tools and organizational change are mutually reinforcing. An electronic surveillance system, for instance, generates data that only becomes valuable when organizational structures exist to act on it, such as unit-level huddles, accountable leadership, and quality improvement cycles. Conversely, organizational programs benefit from the objective, continuous measurement that digital tools provide, allowing teams to detect slippage early and target resources where they are needed most.

The review is careful to note an important limitation: direct category-level comparisons between digital, organizational, and hybrid strategies were absent from the included literature. The reported effects varied according to hospital setting, organizational capacity, and implementation context, meaning that a strategy producing impressive reductions in one intensive care unit may underperform in a resource-constrained ward. This variability is not a weakness of the interventions themselves but a reflection of the environments in which they operate. The authors identify three factors that were commonly associated with successful implementation: leadership support, workflow integration, and organizational readiness. Hospitals whose leaders visibly prioritized infection prevention, whose digital tools were embedded into existing clinical workflows rather than bolted on, and whose staff were prepared and trained for change consistently achieved better results.

These findings carry significant practical implications for hospital administrators and infection prevention teams, particularly as hospitals everywhere face pressure to digitize. Electronic health records, automated surveillance algorithms, and dashboard-based performance monitoring are being deployed at pace, but the review suggests that treating these technologies as standalone solutions is likely to disappoint. Digital technologies, the authors conclude, may better support sustained HAI prevention when embedded within organizational workflows and quality improvement structures. In other words, the software is only half of the intervention; the other half is the management system that turns data into action and sustains new behaviors over time.

For the scientific community, the review also exposes gaps in the evidence base. The methodological heterogeneity that prevented meta-analysis, the absence of direct comparisons between intervention types, and the context-dependent nature of the results all point to the need for better-designed implementation studies. Future research, the findings imply, should evaluate digital and organizational strategies within shared frameworks, report implementation processes transparently, and measure both process and infection outcomes over sufficiently long periods to assess sustainability. Without such studies, hospitals will continue to make consequential investment decisions based on incomplete and fragmented evidence.

The stakes of getting this right could hardly be higher. As antimicrobial resistance narrows the treatment options available for hospital-acquired infections, prevention becomes the most reliable line of defense, and the systematic review by Kayral and colleagues offers a sober but actionable roadmap. The message to health systems is clear: infection prevention succeeds not through gadgets or protocols alone, but through the deliberate fusion of digital capability and organizational commitment, supported by leaders who treat patient safety as an operational priority rather than a compliance exercise. For hospitals willing to make that investment, the evidence suggests, the payoff is a measurably safer environment for the patients who depend on them.

Subject of Research: Implementation of digital and organizational strategies for preventing healthcare-associated infections in adult hospitals

Article Title: Implementation of digital and organizational strategies for the prevention of healthcare-associated infections in adult hospitals: a systematic review

Article References: Kayral, İ. H., Dülger, D., & Taşır, S. (2026). Implementation of digital and organizational strategies for the prevention of healthcare-associated infections in adult hospitals: a systematic review. BMC Health Services Research. https://doi.org/10.1186/s12913-026-15827-x

Image Credits: AI Generated

DOI: 10.1186/s12913-026-15827-x

Keywords: healthcare-associated infections, infection prevention and control, digital health, hospital management, systematic review, patient safety, surveillance, quality improvement, organizational readiness, health services research, e-health, antimicrobial resistance

The study, conducted by a team led by Mingxue Ma, Yuanheng Li, and Yazhou Wang of Harbin Medical University together with collaborators at I.M. Sechenov First Moscow State Medical University, was published in BMC Public Health. It surveyed 3,408 healthcare workers across 35 tertiary public hospitals in nine cities of Heilongjiang Province between March and May 2025. Participants were asked whether they had experienced digital, physical, or psychological violence during the preceding twelve months, and the researchers used logistic regression models to identify the factors associated with digital violence and to probe its links with cognitive, behavioral, and occupational outcomes.

The headline figure is stark: 22.4 percent of respondents reported experiencing digital violence within the previous year. Among those exposed, the abuse was not always confined to the digital realm. Only 41.6 percent of victims experienced digital violence alone, while 58.4 percent also reported physical and/or psychological workplace violence during the same period. That overlap is scientifically important. It indicates that digital violence frequently shares risk environments with traditional workplace violence, likely arising from the same underlying conflicts between patients, families, and the healthcare system, yet it also occurs on its own in a meaningful minority of cases, meaning it cannot simply be treated as a shadow of in-person aggression.

The concept of digital violence in healthcare reflects a structural shift in how medicine is delivered and discussed. Digitalization has extended patient–healthcare worker interactions far beyond the consultation room, into online appointment platforms, review sites, social media, and messaging applications. Every public-facing digital touchpoint creates a surface on which dissatisfaction can be expressed anonymously, at scale, and with a permanence that a corridor argument never has. A defamatory post about a physician can circulate indefinitely, shaping the perceptions of thousands of prospective patients and remaining attached to the clinician’s name in search results. The study’s authors argue that this transformation has created genuine opportunities for violence in digital environments, and that evidence on the phenomenon and its relationship to conventional workplace violence has, until now, remained limited.

The demographic and occupational patterns the researchers uncovered are among the most thought-provoking elements of the work. Higher odds of digital violence were observed among male healthcare workers, those employed in general hospitals, those holding administrative positions, and those working in low-resource cities. By contrast, holding an associate senior or senior professional title was associated with lower odds of exposure. Each of these associations invites interpretation. The male excess runs counter to some patterns seen in in-person workplace violence research and may reflect differences in public visibility or in the roles men occupy within hospitals. The elevated risk for administrative staff suggests that the front line of digital conflict is not exclusively clinical; managers who handle complaints, scheduling, and billing disputes may absorb a disproportionate share of online hostility. The urban gradient points toward structural inequities, with healthcare workers in cities with fewer resources facing conditions that generate more friction with patients and their families.

The protective association with senior professional titles is equally revealing. Clinicians who have reached associate senior or senior rank may carry greater institutional authority, enjoy stronger reputational buffers online, or interact with patients through channels less exposed to public commentary. Alternatively, senior staff may have had more time to develop strategies for managing difficult encounters before they escalate into public attacks. Whatever the mechanism, the finding implies that early-career and mid-career healthcare workers, who lack both the title and the accumulated social capital, may be the most digitally vulnerable members of the workforce.

Beyond mapping who gets targeted, the study examined what digital violence does to the people on the receiving end, and here the results take on a psychological texture. Digital violence was associated with the overall distributions of worry, monitoring of online evaluations, and reduced online expression. In practical terms, healthcare workers who had been attacked online were more likely to be anxious about their digital presence, to habitually check what patients and the public were saying about them on review platforms and social media, and to withdraw from online spaces altogether, self-censoring their professional and personal expression. These are classic signatures of a chilling effect. The digital commons that hospitals increasingly rely upon for outreach, education, and reputation management becomes a source of vigilance and fear rather than an instrument of practice.

Perhaps the most sobering finding concerns the profession’s future. Digital violence was associated with altered attitudes toward whether respondents would want their children to enter the same profession. When abuse follows clinicians home through their phones and lingers in public search results, it does not merely wound the individual; it transmits a message to the next generation about the costs of the career. Notably, however, the study found no significant association between digital violence and two other outcomes: willingness to choose the profession again and turnover intention. This dissociation is analytically interesting. It suggests that online abuse erodes the affective and reputational dimensions of occupational identity, the pride and the willingness to recommend the career to one’s own children, without necessarily pushing people out of their jobs in the short term. The damage, in other words, may be cumulative and intergenerational rather than immediately visible in staffing statistics.

Methodologically, the study has both strengths and constraints that readers should keep in view. Its scale is a genuine asset: 3,408 respondents across 35 tertiary hospitals in nine cities gives the prevalence estimates considerable weight within the population sampled. The use of logistic regression allowed the team to adjust for multiple factors simultaneously and to separate the correlates of digital violence from those of physical and psychological aggression, confirming that both traditional forms were independently and significantly associated with the digital form. At the same time, the cross-sectional design captures a single moment in time, so the associations reported cannot establish causal direction. It remains possible, for instance, that healthcare workers who are already worried or withdrawn experience or recall digital abuse differently, rather than the abuse producing the worry. The sample was also restricted to tertiary public hospitals in a single Chinese province, which limits generalization to primary care settings, private institutions, and other health systems. The authors themselves frame digital violence as a partially distinct yet interconnected form of occupational violence, a formulation that carefully preserves both the overlap with and the independence from traditional workplace aggression.

The policy implications are straightforward and, arguably, overdue. The authors conclude that digital violence is common among healthcare workers and that the findings support incorporating it into workplace violence surveillance and prevention frameworks in healthcare settings. Most hospital violence-prevention programs were designed around physical security: guards, panic buttons, restricted access, and de-escalation training for front-line staff. None of that machinery detects a defamatory thread on a review platform or a coordinated harassment campaign on social media. Building digital violence into surveillance would mean routinely asking about online abuse in occupational health assessments, tracking it alongside physical and psychological incidents, and developing institutional responses, from platform reporting mechanisms to legal support for targeted staff. The study was supported by the National Natural Science Foundation of China, and its publication in an open-access journal means that hospital administrators, occupational health researchers, and policymakers worldwide can examine the full evidence base. As healthcare systems everywhere deepen their digital transformation, the experience of Heilongjiang’s healthcare workers offers a warning worth heeding: the waiting room is no longer the only place where violence finds them, and any serious effort to protect the health workforce must now extend into the digital spaces where their professional lives increasingly unfold.

Subject of Research: Prevalence and associated factors of digital violence against healthcare workers in Chinese tertiary public hospitals

Article Title: Digital violence against healthcare workers in Heilongjiang Province of China: prevalence and associated factors

Article References: Ma, M., Li, Y., Wang, Y., Li, J., Zhong, L., Wei, K., Li, J., Ye, J., Zheng, M., Reshetnikov, V., Sokolov, N., Bobkov, A., Shan, L., & Jiao, M. (2026). Digital violence against healthcare workers in Heilongjiang Province of China: prevalence and associated factors. BMC Public Health. https://doi.org/10.1186/s12889-026-29524-1

Image Credits: AI Generated

DOI: 10.1186/s12889-026-29524-1

Keywords: digital violence, healthcare workers, workplace violence, online harassment, occupational health, cyberpsychology, China, public health, tertiary hospitals, cross-sectional survey, e-health, Harbin Medical University

The study, published in BMC Health Services Research, is described by its authors as the first Australian evaluation of electronic medical record-linked PROMs integrated into a nurse-led breast cancer survivorship clinic. It was a prospective pilot conducted between September 2022 and March 2024, enrolling women with breast cancer six to twelve months after they had completed their active treatment. The central question was practical rather than theoretical: would patients actually use a digital companion in the months after treatment, would they complete the questionnaires it delivered, and could their answers give breast care nurses a real-time window into symptoms and unmet needs before the consultation began?

The technology itself was a collaborative design effort rather than an off-the-shelf product. The hospital’s electronic medical record team worked alongside breast care nurses, surgeons and consumer representatives to build a platform that integrates and distributes electronic PROMs with automated reminders, gives clinicians real-time access to patient responses, and triages patients according to symptom severity ahead of clinic visits. That triage function is the technically significant piece. Instead of a nurse discovering a distressing symptom mid-consultation, the platform surfaces it in advance, allowing the clinical team to prioritise patients whose reported scores suggest they need more urgent or more intensive attention.

Patients invited to the nurse-led clinic were asked to complete three validated instruments through Care Companion four weeks before their appointment. Each measures a different dimension of recovery. The SCST, a screening tool for cancer-related concerns, achieved a completion rate of 98 percent, suggesting that patients found the most clinically focused questionnaire worth their time. The FACT-B + 4, which assesses quality of life across physical, emotional, social and functional domains with additional breast-cancer-specific items, was completed by 52 percent of participants. The Menopause Rating Scale, or MRS, relevant because many breast cancer survivors experience treatment-induced menopausal symptoms, reached 58 percent completion.

Those divergent completion rates are among the most instructive findings in the study. A near-universal response to the screening tool paired with roughly half of patients completing the longer quality-of-life instrument tells implementation researchers something important about digital burden: questionnaire length, relevance and perceived value all shape whether patients engage. The authors are candid that engagement was incomplete. Of the 133 patients invited to the nurse-led clinic, only 47, or 35.3 percent, completed the pre-clinic PROMs and attended. That uptake figure is the study’s sobering counterweight to its enthusiasm, and the team explicitly states that further refinement is required to improve patient engagement and fully realise the potential of digital survivorship care models.

Where patients did engage, however, their response was strikingly positive. In a satisfaction survey administered to a subset comprising ten percent of participants, 100 percent reported satisfaction with the program and said they would recommend the Care Companion breast survivorship program to others. Among respondents, 89 percent felt able to complete the tasks, felt actively involved in their care, found the platform easy to use, and were comfortable using the devices required. For a hospital-built tool rather than a polished commercial app, those usability numbers carry real weight, particularly given that the cohort spanned a wide age range of women who did not grow up with smartphones.

The clinical logic behind the platform rests on a well-documented gap in oncology care. Survivorship is the phase in which many of the long-term and late effects of treatment emerge, yet it is also the phase with the least structured contact between patients and their treating teams. Breast care nurses sit at the centre of the Australian response to that gap, coordinating follow-up, managing symptoms and providing psychosocial support, often with limited visibility into how patients are coping between appointments. PROMs are designed to close that visibility gap by systematically capturing what patients experience, rather than relying on symptoms to be volunteered opportunistically during a consultation. Evidence from oncology more broadly has repeatedly shown that routine symptom monitoring can improve outcomes, but translating that evidence into routine practice has proven difficult, which is precisely why an EMR-embedded approach is significant.

Embedding the platform in the electronic medical record, rather than running it as a standalone app, solves several chronic problems of digital health pilots at once. Patient responses flow into the same record clinicians already use, so there is no separate portal to check and no duplicate data entry. Automated reminders can be triggered from scheduling data without manual chasing. And because the platform was built by the hospital’s own EMR department in collaboration with the nurses who would use it, the workflow reflects the realities of a survivorship clinic rather than the assumptions of an external software vendor. The trade-off is that such bespoke systems require institutional investment and maintenance, and their portability to other hospitals with different record systems is not guaranteed.

The study’s limitations are those of a pilot. It was conducted at a single centre, the number of patients who completed both the PROMs and the clinic visit was modest, and the satisfaction survey sampled only a small subset of participants, which makes the 100 percent satisfaction figure encouraging but statistically fragile. The authors do not claim that the platform improved clinical outcomes; the secondary aim of the study was simply to describe patient-reported outcomes, not to test whether digital monitoring changed them. What the study does establish is feasibility and acceptability in an Australian public hospital context, filling a documented gap in the local literature on digitally enabled survivorship models.

The broader significance lies in what comes next. If digital companions like Care Companion can raise their engagement rates, perhaps through shorter instruments, better-timed reminders or targeted support for patients less comfortable with technology, they could transform nurse-led clinics from episodic check-ins into continuously informed encounters. The Royal Melbourne team has demonstrated that the plumbing works: patients will answer questionnaires on a hospital-built platform, nurses can read the answers before the patient arrives, and the patients who do engage overwhelmingly approve of the experience. The unfinished work, as the authors acknowledge, is persuading the majority who did not engage to join them, and proving that this richer stream of patient-reported data ultimately translates into better survivorship care.

Subject of Research: Digital patient-reported outcome measures in nurse-led breast cancer survivorship care

Article Title: Introduction of a digital care companion to facilitate patient reported outcome measures and breast care nurse-led survivorship

Article References: Mao, C., Shanahan, K., Keen, R., Louie, J., Vu, M., Fazio, T., Boulton, A., Alvandi, A. O., & Skandarajah, A. (2026). Introduction of a digital care companion to facilitate patient reported outcome measures and breast care nurse-led survivorship. BMC Health Services Research. https://doi.org/10.1186/s12913-026-15796-1

Image Credits: AI Generated

DOI: 10.1186/s12913-026-15796-1

Keywords: breast cancer, survivorship care, patient-reported outcome measures, digital health, nurse-led clinic, electronic medical record, breast care nurses, e-health, health services research, patient engagement, quality of life, Australia

The research team, led by Mythily Subramaniam and Rachel Hsiao Shen Tan of the Institute of Mental Health in Singapore, set out to answer three linked questions: how mental health professionals view the growing availability of online mental health information, how those professionals manage encounters with patients who arrive informed by the internet, and whether professionals and young patients differ in their beliefs about what such information actually does to treatment. Participants completed a questionnaire adapted from a previously developed instrument assessing beliefs about online mental health information and its perceived impact on treatment-related outcomes. The professional group was deliberately broad, encompassing psychiatrists, psychologists, counsellors, social workers and case managers, reflecting the multidisciplinary reality of modern mental health care. Statistical comparisons between the two groups were made using multinomial logistic regression, adjusted for age and gender, a technique that allows researchers to estimate the odds of holding a particular belief while controlling for demographic differences that might otherwise confound the results.

The headline numbers reveal a profession that is, on the surface, broadly comfortable with the digital information era. A clear majority of the professionals surveyed, 94 out of 129, or 72.9 percent, viewed the increasing availability of online mental health information positively. Most also considered dedicated mental health websites to be reasonably accurate sources of information. That relative confidence, however, did not extend across the digital ecosystem. The professionals were considerably more sceptical of social media platforms as sources of mental health content, a distinction that matters enormously for young patients, whose information-seeking habits are heavily concentrated on exactly those platforms. The finding hints at a layered landscape in which clinicians draw sharp boundaries between curated health websites and the algorithmic feeds of social networks, boundaries that their young patients may not perceive or respect in the same way.

Yet even among professionals who expressed positive views, a curious behavioural gap appeared. Despite their general approval of online mental health information, more than half of the professionals surveyed, 71 out of 129, or 55.0 percent, reported that they rarely or never referred patients to online resources. This mismatch between attitude and practice is one of the most intriguing aspects of the study. It suggests that positive beliefs about online information do not necessarily translate into active integration of such resources into clinical care. Whether this reflects concerns about quality control, uncertainty about which resources to recommend, time pressures within consultations, or simply the absence of established referral habits, the result is that a large share of clinicians are leaving the digital information channel unmanaged, effectively ceding it to search engines and social media algorithms.

When the researchers compared the two groups directly, the perception gap became stark. Professionals were significantly less likely than patients to believe that online mental health information encourages adherence to treatment advice, with an odds ratio of 0.39 and a 95 percent confidence interval of 0.17 to 0.92. In plain terms, after adjusting for age and gender, the clinicians were less than half as likely as their young patients to see online information as a tool that helps people stick to their treatment plans. Conversely, professionals were more likely to believe that online information promotes unnecessary visits to mental health professionals, with an odds ratio of 2.68 and a confidence interval of 1.29 to 5.59. The two groups, in other words, look at the same phenomenon and see opposite consequences: where patients may see empowerment and engagement, many clinicians see wasted appointments and undermined advice.

The divergence deepened when the survey probed specific treatment-related domains. Across every domain measured, professionals were significantly more likely than patients to perceive that online mental health information had either hurt outcomes or provided no benefit, rather than improved them. The effect was particularly pronounced in two areas. For the domain of understanding mental health conditions or treatments, professionals were far more likely to report harm or no benefit, with an odds ratio of 7.12 and a confidence interval of 2.73 to 18.57. For communication with mental health professionals, the odds ratio was 6.18, with a confidence interval of 2.70 to 14.13. These are large effects by epidemiological standards, and they indicate that the perception gap is not a marginal statistical artefact but a substantive disagreement about the everyday consequences of patients searching for information online.

The implications of these numbers extend into the consulting room. Shared decision-making, the model of care in which patients and clinicians jointly weigh options and arrive at treatment choices together, depends on a common factual baseline and mutual trust. If young patients believe that reading about their condition online helps them understand their illness and communicate better with their clinicians, while their clinicians believe the opposite, every consultation begins with an unspoken disagreement about the value of the patient’s own research. An internet-informed patient who quotes a website may be experienced by a clinician as challenging or misinformed, while the same patient may experience the clinician’s dismissal of that source as dismissive of their autonomy. The study’s authors suggest that such dynamics could strain the therapeutic relationship precisely at the point where collaboration matters most.

The research also arrives amid a broader scientific debate about the quality of mental health content online. Clinicians’ scepticism toward social media platforms echoes published concerns that short-form video content and influencer-driven discussions can oversimplify, sensationalise or outright misrepresent psychiatric conditions, from attention deficit hyperactivity disorder to dissociative identity disorder. At the same time, the profession’s own ambivalence, evident in the finding that most clinicians view online information positively yet rarely refer patients to it, suggests that the problem is not simply bad content but a missing bridge between the clinical and digital worlds. Without curated, clinician-endorsed online resources being actively offered during consultations, young patients are left to navigate the information ecosystem alone, and the algorithms that shape their feeds are optimised for engagement rather than clinical accuracy.

The study’s design merits attention when weighing its conclusions. As a cross-sectional survey conducted at a single tertiary psychiatric institution in Singapore, it captures beliefs at one point in time among patients well enough to attend specialist care, and its findings may not generalise to other health systems, cultures or patient populations. The survey instrument measured perceptions rather than objective outcomes, so the results speak to what professionals and patients believe about online information, not to what online information actually does. That distinction is important: a perception gap is itself a clinically meaningful finding, because beliefs shape behaviour in consultations, but it does not settle the question of whether online mental health information genuinely improves or harms treatment outcomes. Longitudinal and interventional research would be needed to establish causal effects.

What the study does establish, clearly and quantitatively, is that the two groups at the heart of mental health care are not aligned in their views of the digital information revolution. The authors conclude that training aimed at improving communication with internet-informed patients, and at enhancing professionals’ understanding of how young people seek information online, may help bridge the perception gap and support more collaborative care. Such training would address both sides of the divide: equipping clinicians to engage constructively with what patients bring from the internet, and giving young patients guidance on evaluating sources and integrating what they read with professional advice. In an era when the first response to psychological distress is often a search bar rather than a clinic door, the finding that doctors and patients inhabit different informational realities is a call to close that gap deliberately, before it widens into a fracture in the therapeutic alliance itself.

Subject of Research: Perceptions of online mental health information among mental health professionals and young psychiatric patients

Article Title: The perception gap in online mental health information: a survey comparing the views of mental health professionals and young patients

Article References: Subramaniam, M., Tan, R. H. S., Gunasekaran, S., Tan, Y. B., Tan, C. M., Tan, Y. W. B., Tan, B. C. W., Poh, B., Chandwani, N., Tang, C., Fung, D. S. S., & Chong, S. A. (2026). The perception gap in online mental health information: a survey comparing the views of mental health professionals and young patients. BMC Psychiatry. https://doi.org/10.1186/s12888-026-08730-6

Image Credits: AI Generated

DOI: 10.1186/s12888-026-08730-6

Keywords: online mental health information, mental health professionals, young patients, psychiatry, shared decision making, therapeutic relationship, social media, health communication, e-health, survey research, Singapore, patient-clinician perceptions

The research team conducted the review according to the Joanna Briggs Institute methodology for scoping reviews and reported it following the PRISMA-ScR guidelines, a rigorous framework designed to make evidence syntheses transparent and reproducible. They searched the Web of Science and Ovid databases, including Ovid MEDLINE and APA PsycInfo, for literature published between January 2010 and February 2025. The initial search retrieved 1,173 articles, which the team screened down to 39 studies that underwent full data analysis through narrative synthesis. That funnel shape — from nearly twelve hundred records to fewer than forty included studies — reflects both the growing but still fragmented nature of this research field and the strict inclusion criteria the reviewers applied.

The scope of the review is deliberately broad. Rather than focusing on a single disease, the authors set out to synthesize the needs and experiences that parents of children with chronic conditions express across different diagnosis categories. This cross-diagnostic approach is what gives the findings their punch: recurring themes emerged regardless of whether the child had autism, diabetes, asthma, congenital heart disease, or obesity. In other words, the review suggests that the digital needs of caregiving parents are not primarily disease-specific but reflect the shared architecture of chronic pediatric care itself — information, coordination, communication, and emotional support.

Technically, the e-Health landscape covered by the included studies spans several distinct modalities. Mobile applications delivered on smartphones, web-based platforms accessible through browsers, and video conferencing tools connecting families with clinicians all featured in the literature. Parents’ experiences with these technologies were generally positive, particularly where the tools improved day-to-day monitoring of the child’s condition, strengthened parental empowerment, and expanded access to care. For families living far from specialist centers, or juggling work and caregiving, the ability to transmit data, receive feedback, and consult professionals remotely can transform the logistics of managing a chronic illness.

Yet the review is equally clear about what parents want these tools to be. Across conditions, parents consistently emphasized the need for accessible and user-friendly interfaces — a deceptively simple requirement that many digital health products fail to meet. They also wanted reliable and comprehensible health information, meaning content that is trustworthy, up to date, and written in language a stressed parent can absorb at midnight. A third cluster of needs centered on care coordination and communication: tools that help families organize appointments, track medications, share information with multiple providers, and communicate efficiently with healthcare professionals. In fragmented health systems where a chronically ill child may see pediatricians, subspecialists, therapists, and school nurses, the parents often become the de facto coordinators, and they are asking technology to share that load.

Beyond logistics, the review highlights a more human dimension. Parents valued social support and psychological resources delivered through e-Health applications — features that connect them with other families facing similar challenges or provide coping strategies for the emotional strain of long-term caregiving. Chronic pediatric illness is not only a medical problem; it reshapes family life, finances, and mental health. The finding that parents actively seek psychological and peer support from digital tools underscores that well-designed e-Health is not merely a data pipe between home and hospital but a potential lifeline against isolation.

At the same time, the enthusiasm is tempered by persistent concerns. Data privacy remained a recurring worry among parents, an issue that has only grown in salience as health applications collect increasingly granular information about children. Parents also reported practical barriers: technical difficulties that stall adoption, limited customization that prevents tools from fitting their child’s specific situation, and a fear that increased digitalization could erode the personal contact with providers that many families treasure. That last concern is particularly important for developers and policymakers, because it reframes e-Health not as a replacement for clinical relationships but as an adjunct whose value depends on preserving human connection.

One of the most consequential findings of the review concerns the trajectory of care. The authors found that evidence on parental needs along the care pathway was limited, with early phases after diagnosis and long-term management phases better represented in the literature than transitional periods — for example, the handover from pediatric to adult healthcare services that adolescents with chronic conditions must eventually navigate. Needs and experiences were frequently repeated across different modalities and conditions, which the authors interpret as pointing to common priorities for digital health design. But the dynamic evolution of those needs over time remains under-studied, leaving a gap for future research on how a parent’s requirements change as a child grows, gains autonomy, and eventually takes over their own disease management.

The implications for the digital health industry are straightforward. If effective tools for this population should integrate accessible design, reliable information, coordination features, social support, and robust data protection — as the review concludes — then product development in pediatric e-Health should start from these cross-cutting parental priorities rather than from diagnosis-specific feature lists. The authors argue that parents of children with chronic conditions articulate broad, cross-cutting needs that go beyond diagnosis-specific management, a conclusion that could inform everything from user interface decisions to regulatory frameworks for pediatric health data.

For the growing population of families affected by childhood chronic illness, the message of this review is one of cautious optimism. The technology to support them largely exists, and where it has been deployed well — in mobile apps, web platforms, and video consultations — parents report genuine improvements in monitoring, empowerment, and access to care. What remains is the harder work of alignment: designing systems that are simple enough for an exhausted parent at the end of a long day, transparent enough to earn their trust with their child’s data, and flexible enough to evolve with a family’s journey from diagnosis through transition to adult care. The 39 studies synthesized here provide the clearest map yet of what that alignment must achieve.

Subject of Research: Parental needs and experiences with e-Health applications in chronic pediatric care

Article Title: Parental needs and experiences with e-Health applications in chronic pediatric care – a scoping review

Article References: Grüter, H., Loeffler, A., Stähler, L., Gönen, I., Prüfe, J., De Bock, F., & Pischke, C. R. (2026). Parental needs and experiences with e-Health applications in chronic pediatric care – a scoping review. BMC Pediatrics, 26(1), Article 910. https://doi.org/10.1186/s12887-026-07737-y

Image Credits: AI Generated

DOI: 10.1186/s12887-026-07737-y

Keywords: e-Health, pediatrics, chronic conditions, scoping review, parental needs, digital health, care coordination, data privacy, mobile health apps, telemedicine, health informatics, caregiver support