The problem SANITA targets is subtle but consequential. When patients manage their own medical records, they become the custodians of data they may not have the technical expertise to police. Consent decisions, access logs, and usage histories can drift into inconsistency, and those inconsistencies translate directly into degraded healthcare quality when clinicians rely on incomplete or unverifiable records. The authors observe that existing healthcare blockchain solutions typically bolt on provenance mechanisms as an afterthought, lacking comprehensive multi-level access controls, post-access validation, and robust privacy safeguards backed by verifiable and accountable consent management. In practice, that means a record might be shared with a specialist, copied into an analytics pipeline, or aggregated by a research consortium without any tamper-evident trail that the patient—or an auditor—can later inspect.

Provenance, in the data-management sense, is the documented lineage of information: where it originated, how it was transformed, and who touched it along the way. In clinical settings, provenance is not a luxury. Clinical data registries depend on context to be meaningful, and systematic reviews of health information systems have repeatedly flagged provenance management as a weak link. The SANITA team’s contribution is to treat provenance as a first-class citizen of the blockchain itself rather than an external log. The system records not just the fact that data was accessed, but the full hierarchical chain of usage—preserving the lineage of medical records as they move across interoperating institutions and applications, so that a laboratory result derived from a wearable reading, for example, carries its ancestry with it.

At the technical heart of SANITA sits a smart contract, self-executing code deployed on the blockchain that automates the provenance workflow. When a request to use medical data arrives, the contract evaluates the request against the access-control policy, records the consent decision, and then—crucially—continues to monitor the interaction after access has been granted. This post-access provenance is the feature the authors single out as their answer to a gap in comparable systems: most blockchain health platforms verify identity before unlocking a record and then go quiet. SANITA’s contract instead captures what happens to the data after the door opens, and it does so, according to the team’s experiments, in comparable time to systems that offer far weaker guarantees. The smart contract architecture also supports upgradeability patterns, an engineering consideration the authors examined carefully, since immutable contracts that cannot evolve become liabilities in a regulatory landscape that shifts as fast as healthcare technology.

Interoperability is the second pillar. Healthcare data rarely lives in one place; it flows between hospitals, pharmacies, insurers, wearable vendors, and national e-health infrastructures such as Estonia’s widely cited electronic health record system. A provenance system that only works inside a single blockchain silo would miss most of the story. SANITA is designed to maintain hierarchical provenance across interoperating healthcare blockchains, meaning that usage records remain coherent and verifiable even when data crosses institutional boundaries. The authors argue this is essential for consumer-centric e-healthcare, where the data feeding a clinician’s dashboard may have been generated by a dozen different consumer devices, each with its own trust domain and its own chain of custody.

To find out whether these design ambitions survive contact with reality, the researchers ran a series of experiments benchmarking SANITA against state-of-the-art models in the field. The headline numbers are striking: SANITA delivered 25 percent better throughput while reducing latency by 30 percent relative to the comparison systems. In blockchain terms, throughput measures how many provenance transactions the network can process per unit of time, while latency measures the delay between a data-use event and the moment its provenance record is immutably committed. Both matter enormously in clinical practice. A provenance system that slows record retrieval to a crawl will be abandoned by clinicians; one that lags behind real-time access cannot prevent or even promptly detect misuse. The evaluation used synthetic patient records generated with Synthea, an open-source simulation platform that produces realistic synthetic electronic health records, allowing the team to stress-test the system at scale without exposing any real patient data.

Security analysis formed the other half of the evaluation, and here the authors report that SANITA achieved what they describe as 100 percent attack resistance capability across the threat scenarios they modeled, alongside efficiency suitable for deployment on healthcare blockchains. The formal analysis drew on the Dolev–Yao adversary model, a standard abstraction in which an attacker can intercept, replay, and forge messages but cannot break the underlying cryptography itself. Within that model, the team assessed how the system withstands eavesdropping, tampering, and impersonation attempts against both the provenance records and the consent-management workflow. The multi-level access-control design, combined with verifiable consent records anchored on-chain, is what allows every access decision to be audited after the fact without revealing the contents of the medical data itself.

Perhaps the most forward-looking aspect of the work is its embrace of post-quantum cryptography. Quantum computers of sufficient scale, should they arrive, would shred the elliptic-curve cryptography protecting most of today’s blockchains, exposing archived medical records to retroactive decryption—a scenario security researchers call harvest now, decrypt later. SANITA sidesteps this threat by building on CRYSTALS-Kyber and CRYSTALS-Dilithium, the lattice-based key-encapsulation and digital-signature schemes selected by standardization bodies as the leading post-quantum primitives. Both schemes derive their hardness from problems on mathematical lattices, structures whose worst-case difficulty is believed to reduce to their average-case difficulty, giving cryptographers unusually strong confidence in their resilience. The authors note that these schemes resist quantum attacks while maintaining efficient performance, meaning the quantum-proofing does not come at the price of the throughput gains that make SANITA practical.

The significance of the work extends beyond one protocol. Healthcare blockchain deployments are proliferating, from pharmaceutical supply networks like MediLedger to patient-centric storage frameworks built on the Interplanetary File System, and regulatory pressure around data breaches continues to intensify under regimes such as HIPAA. Yet surveys of blockchain applications in electronic health records consistently identify consent management and provenance as the weakest architectural layers. By demonstrating that post-access provenance, hierarchical interoperability, and post-quantum security can coexist with better performance than existing alternatives, SANITA offers a template for what the next generation of patient-controlled health platforms should look like. The authors, who declare no competing interests and conducted the research without specific grant funding, position the system as a step toward secure, transparent, and scalable healthcare data management built around the consumer rather than the institution.

Challenges remain before systems like SANITA reach production. Real-world deployment would need to confront the governance of cross-chain interoperability, the onboarding of patients who lack technical proficiency, and the operational costs of running lattice-based cryptography on resource-constrained wearable devices. The authors themselves frame consumer-managed provenance as a challenge of accuracy, security, and privacy that no single mechanism fully solves. Still, the study’s experimental results suggest that the trade-off long assumed between strong provenance guarantees and acceptable performance may be false. If healthcare blockchains are to earn the trust that patient-centered medicine demands, the ledger will need to remember not just who owns a record, but every hand it has passed through—and SANITA’s smart contracts are designed to make sure it never forgets.

Subject of Research: A decentralized smart-contract-based data usage provenance system with post-quantum cryptography for patient-controlled healthcare blockchains.

Article Title: SANITA: decentralized data usage provenance system for healthcare blockchains

Article References: Kumar, G., Saha, R., Conti, M., Markendey, V., & Thomas, R. (2026). SANITA: decentralized data usage provenance system for healthcare blockchains. Cluster Computing, 29(14), Article 809. https://doi.org/10.1007/s10586-026-06586-9

Image Credits: AI Generated

DOI: 10.1007/s10586-026-06586-9

Keywords: blockchain, healthcare, data provenance, smart contracts, post-quantum cryptography, CRYSTALS-Kyber, CRYSTALS-Dilithium, e-health, patient data privacy, interoperability, electronic health records, consent management

The answer, the review finds, is both promising and sobering. The researchers identified three core dimensions along which LLM-driven health systems personalize their behavior, eight distinct technical architectures that developers use to adapt these models to individual users, and four categories of implementation challenges that consistently undermine effectiveness. That structure matters because personalization in health is not a single feature you can bolt onto a chatbot. It is a design problem that spans what the system knows about you, how it reasons about that knowledge, and how it changes its outputs over time as your needs evolve.

To understand why this review lands at such a pivotal moment, consider what makes large language models different from the health apps that came before them. Earlier generations of digital health tools relied on rigid rule engines and decision trees: if a user logged three workouts in a week, the app sent a congratulatory notification; if a diabetic patient’s glucose reading crossed a threshold, an alert fired. These systems were transparent but brittle. They could not hold a conversation, could not interpret the messy, ambiguous way people actually describe their lives, and could not adjust their tone or content to a user’s literacy level, culture, or emotional state. LLMs, by contrast, are trained on vast corpora of text and can generate fluent, contextually sensitive responses to essentially any prompt, which makes them natural candidates for conversational health coaching, mental wellness support, medication guidance, and lifestyle intervention.

But fluency is not the same as personalization. A model that answers every question beautifully but identically for a teenager in Nairobi, a retiree in Halifax, and a shift nurse in Mumbai is not adaptive; it is merely articulate. The Dalhousie team’s synthesis of the literature shows that researchers have attacked this problem from multiple technical directions. Among the eight adaptation architectures catalogued in the review are approaches such as fine-tuning, where a base model is retrained on domain-specific health data to sharpen its medical reasoning; retrieval-augmented generation, where the model consults external knowledge sources or a user’s personal records before responding; prompt engineering and in-context learning, where user profiles, preferences, and history are packed into the prompt itself so the model conditions its answers on them; and agent-based designs, where multiple specialized model components collaborate, with one handling medical accuracy, another managing conversational tone, and a third tracking long-term user goals.

Each architecture carries distinct trade-offs that the review helps clarify for the field. Fine-tuning can produce deep domain competence but requires curated datasets, computational resources, and careful validation to avoid degrading a model’s general capabilities or introducing subtle biases. Retrieval-augmented approaches keep personal data outside the model weights, which eases privacy concerns and allows information to be updated in real time, but they depend on the quality and currency of the underlying data stores. Prompt-based personalization is cheap and flexible, yet it consumes context window space and can be fragile when a user’s profile grows complex. Agentic pipelines offer modularity and auditability but add engineering complexity and latency. The review’s contribution is to map these options against the health domains where they have been deployed, giving designers a conceptual framework for choosing the right tool for the right intervention.

The health domains covered in the synthesized literature are strikingly broad. LLM-driven systems have been explored for mental health support, where conversational agents can offer around-the-clock availability and nonjudgmental interaction that some users find easier than talking to a human; for chronic disease management, where models can interpret self-reported symptoms and medication logs; for physical activity and nutrition coaching, where adaptive feedback loops respond to a user’s progress; and for health information access, where models translate clinical jargon into plain language tailored to a reader’s background. Across these domains, the review highlights evidence that LLM-driven applications can be effective, while emphasizing that their success hinges on how well they meet individual needs and preferences rather than on raw model capability alone.

Here is where the review delivers its most uncomfortable finding: inclusivity and contextual sensitivity remain underexplored across the entire field. Most systems studied to date are built and evaluated with narrow populations in mind, often English-speaking, digitally literate, and from high-income settings. A personalization engine that models a user’s preferences but ignores their cultural context, language, disability status, or socioeconomic reality risks producing interventions that work beautifully in a pilot study and fail, or even cause harm, in the real world. The authors frame this as a central gap for the human-computer interaction community, arguing that future systems must be designed to be adaptive, personalized, inclusive, effective, and responsive to evolving individual characteristics and needs, a five-part standard that raises the bar well beyond current practice.

The four categories of implementation challenges identified in the review compound that concern. Technical hurdles include the tendency of LLMs to generate plausible but incorrect health information, a failure mode that is dangerous when the subject is medication dosing or symptom triage. Data challenges revolve around the scarcity of high-quality, representative datasets for training and evaluation, particularly for underrepresented groups. Ethical and privacy challenges loom largest for deployment: health conversations are among the most sensitive data a person can share, and questions about consent, data retention, and secondary use remain unsettled in most jurisdictions. Finally, evaluation challenges persist because there is no consensus on how to measure personalization quality itself; a system can score well on generic benchmarks while failing catastrophically at adapting to any single real user.

The ethical dimension receives particular attention in the review, which situates technical choices inside questions of fairness, autonomy, and global impact. If LLM health assistants become a mainstream interface to care, the populations excluded from their training data could face systematically worse automated advice, deepening existing health disparities rather than closing them. Conversely, the authors point to genuine opportunities for global reach: conversational agents that speak low-resource languages, that operate on inexpensive hardware, and that deliver evidence-informed guidance to communities with limited access to clinicians could extend the frontier of wellness support to billions of people. Whether that promise materializes depends on the design decisions being made in laboratories right now, which is precisely why a structured map of the field arrives at such a useful moment.

The Dalhousie team’s conceptual framework, built from the three personalization dimensions, eight adaptation architectures, and four challenge categories, is offered as a shared vocabulary for the next generation of research. Rather than each lab reinventing its own taxonomy of what personalization means, future studies can locate their contributions within a common structure, compare results across domains, and identify which combinations of architecture and dimension remain untested. The review’s dataset, including all coding records from the 149 articles, is available in supplementary material, and the article is open access, lowering the barrier for researchers in resource-constrained settings to build on it. The work was published on 30 September 2026 in Artificial Intelligence Review, with no conflicts of interest declared by the authors.

What emerges from this synthesis is a field in transition. The first wave of LLM health applications proved that the technology could hold convincing, helpful conversations about well-being. The second wave, which this review both documents and aims to accelerate, must prove something harder: that these systems can know who they are talking to, respect who that person is, and change what they say accordingly, safely, and fairly. The 149 studies analyzed here suggest the ingredients are in place, from mature adaptation architectures to growing awareness of ethical pitfalls. What remains is the disciplined, inclusive engineering work of turning a chatty generalist into a genuinely personal health companion, one carefully validated system at a time.

Subject of Research: Systematic review of large language model-driven personalized and adaptive systems for health and wellness

Article Title: LLM-driven personalized and adaptive systems for health and wellness: a systematic review

Article References: Kimeu, J. M., Chan, G., Orji, R., & Oyebode, O. (2026). LLM-driven personalized and adaptive systems for health and wellness: a systematic review. Artificial Intelligence Review. https://doi.org/10.1007/s10462-026-11703-6

Image Credits: AI Generated

DOI: 10.1007/s10462-026-11703-6

Keywords: large language models, personalization, adaptive systems, digital health, wellness, human-computer interaction, systematic review, health informatics, artificial intelligence, machine learning, e-health, conceptual framework

The scientific rationale behind the intervention rests on a growing body of evidence linking social participation to health in later life. Studies have shown that staying socially engaged protects against the onset or progression of chronic conditions, slows declines in the ability to perform everyday activities, and reduces the risk of physical frailty. The World Health Organization emphasizes social participation and the ability to create and maintain relationships as central pillars of healthy aging. Yet despite this evidence, social participation is rarely the primary focus of interventions targeting older adults, and even less attention has been paid to the concrete strategies older people use in their daily lives to stay engaged with their communities.

Social participation is a complex concept. According to the widely cited definition by Levasseur and colleagues, it describes a person’s involvement in activities that provide interactions with others in community life and in important shared spaces, shaped by the societal context and by what the individual finds meaningful. Participation exists on a spectrum of engagement, from simply interacting with others, to doing activities together, to contributing to other people and to society at large. Life events such as the loss of friends or family members, or the need to relocate, can threaten these connections and become risk factors for loneliness. Environmental barriers, including a lack of accessible social meeting places, further complicate the picture. Research also shows that maintaining social participation in old age is an active, ongoing process that requires commitment, initiative, and the development of personal strategies to adapt to new circumstances.

It is precisely these strategies that the Health Web intervention targets. A key psychological ingredient is self-efficacy, the belief in one’s own ability to plan and carry out the actions needed to achieve specific goals. A systematic meta-synthesis of qualitative studies previously conducted by the research group revealed that older adults use a variety of inward-looking and outward-looking strategies to build routines within daily activities that support their social participation. That systematic review was integrated into the theoretical framework of the new intervention, alongside occupational therapy theory. The ValMO model describes how different values attached to daily activities generate meaning in everyday life, while the transactional model of occupation focuses on the situations in which activities take place, treating digital tools and digital environments as part of the whole that shapes opportunities for participation.

Because Health Web 1.0 is designed as a group intervention, its developers also built the programme on established theories of group dynamics. Cole’s seven steps for group intervention guide the occupational therapist on how to structure each group meeting, which is considered important both for the group process and for the outcomes achieved. The purpose of the group format is to help participants gain new perspectives on their own situation by reflecting and sharing experiences with one another. In addition, the programme borrows pedagogical principles from the flipped classroom. Participants prepare for each group meeting by watching short recorded educational videos and completing reflection assignments on their own, which frees the occupational therapist to spend the live sessions supporting participants in becoming aware of, reflecting on, and developing their own strategies for social participation, rather than simply delivering information.

The structure of the intervention is carefully specified. It begins with two introductory sessions, followed by five themed sessions spread over seven weeks, and concludes with a follow-up session one month after the last themed meeting. Each themed session consists of a short educational video of five to ten minutes, a reflection assignment, and a group meeting of approximately ninety minutes moderated by an occupational therapist. Before the programme starts, participants receive a binder containing an intervention guide that presents the theme sessions and reflection assignments, along with study-specific registration forms. The occupational therapists delivering the intervention complete a dedicated education programme beforehand, including recorded videos on the theoretical foundation and an example of how a themed session can be conducted, and they receive ongoing support from the research group throughout the delivery period.

The feasibility study itself follows the Medical Research Council framework for developing and evaluating complex interventions and uses a pre-test and post-test design without a control group. Qualitative and quantitative data will be collected concurrently in a convergent mixed-methods design. After each themed session, older adults will complete registration forms rating, on five-point Likert scales, their agreement with statements about the knowledge gained, the value of the reflection assignments, the value of and engagement in group discussions, and their use of the digital platform. The occupational therapists will complete parallel forms rating their compliance with the intervention guide, their confidence as group leaders, and the management of the digital platform. Semi-structured interviews with both groups, conducted before, during, and after the programme, will probe expectations, experiences, perceived benefits, harms, and unintended consequences.

Potential outcomes will be measured with standardized self-assessment tools administered before the intervention, immediately after, and three months later. The Occupational Balance Questionnaire, an eleven-item instrument with good confirmed validity, captures satisfaction with and variation in daily activities, with higher scores indicating better occupational balance, a quality previously linked to health and quality of life in older adults. The General Self-Efficacy scale measures self-perceived ability to cope with life’s demands across ten items. A newly developed twenty-four-item measure of strategies for social participation, based directly on the research group’s meta-synthesis, will capture how frequently participants use specific strategies in daily life. Baseline sociodemographic data will be collected, and perceived loneliness will be assessed with the twenty-item UCLA Loneliness Scale, which covers emotional, social, and existential dimensions of loneliness.

The study will enroll approximately twelve older adults and two occupational therapists, a sample size considered appropriate for an early-stage feasibility study where the goal is to gather detailed information rather than statistical power. Participants must be sixty-five or older, living in ordinary housing, experiencing a changed life situation that affects their possibilities for social participation, and have access to a computer or tablet along with experience using the internet and email. Recruitment began in autumn 2024 and was completed in spring 2025, drawing participants through occupational therapists, advertisements, and local retirement organizations, with screening conducted during home visits. The study has been approved by the Swedish Ethical Review Authority and will be conducted in accordance with the Helsinki declaration and the GDPR.

The researchers acknowledge that several uncertainties remain before Health Web can move to larger-scale testing. Clinical uncertainties concern whether a web-based format suits older adults who may be hindered in using technology, and both digital delivery and flipped-classroom pedagogy represent new ways of working for many occupational therapists. Methodological uncertainties around the research design and the chosen outcome measures also need to be resolved. If the feasibility evaluation succeeds, however, the long-term potential is considerable. The intervention could help occupational therapists adopt a more proactive role in supporting meaningful everyday lives in old age, while the group format and digital delivery promise more efficient use of scarce professional resources and greater accessibility to services, particularly for older adults in sparsely populated regions of northern Sweden and beyond.

Subject of Research: A web-based occupational therapy group intervention to support social participation and healthy aging in older adults

Article Title: Health Web 1.0, a web-based occupational therapy group intervention for older adults: A study protocol for a feasibility study

Article References: Health Web 1.0, a web-based occupational therapy group intervention for older adults: A study protocol for a feasibility study. (n.d.). https://doi.org/10.1080/11038128.2025.2590725

Image Credits: AI Generated

DOI: 10.1080/11038128.2025.2590725

Keywords: occupational therapy, older adults, social participation, digital health, healthy aging, feasibility study, e-health, loneliness, self-efficacy, group intervention, flipped classroom, primary healthcare

The study was carried out by a team of researchers from Shahjalal University of Science and Technology in Sylhet, led by Amit Bhowmick, with contributions from Md Mohi Uddin Rajib, Md. Habibur Rahman and Shimul Roy. Using a mixed-methods research design, the team combined a quantitative survey of 384 respondents, selected through simple random sampling, with qualitative face-to-face interviews involving 16 service recipients and 10 service providers. This dual approach allowed the researchers to measure not only the statistical patterns of awareness and usage but also the lived experiences and frustrations of people working within and relying upon the Upazila Health Complexes, the secondary-level facilities that form the backbone of rural healthcare delivery in Bangladesh.

The headline numbers tell a story of paradox. On the one hand, connectivity is no longer the obstacle it once was: 81 percent of respondents reported having internet access at home, a figure that reflects the extraordinary expansion of mobile broadband across South Asia over the past decade. On the other hand, 68 percent of respondents had never used an e-health service of any kind. Awareness of what e-health services even exist scored lowest of all measured dimensions, with a mean score of just 2.53, while readiness to use such services, at 4.10, and perceived benefits, at 4.25, were both rated highly. Perceived barriers also scored high, at 4.21, suggesting that people recognize both the promise of digital health and the substantial obstacles standing in its way.

This combination of low awareness and high readiness is perhaps the most consequential finding of the entire study. It indicates that the problem is not a lack of demand or an unwillingness among rural Bangladeshis to embrace digital medicine. People are, in principle, ready and willing to consult doctors remotely, access electronic records, and call health hotlines. What they lack is basic knowledge that these services exist and practical pathways to use them. The gap, in other words, is not attitudinal but infrastructural and informational, a distinction with major implications for how policymakers should respond.

The qualitative interviews painted a bleaker picture of conditions inside the health complexes themselves. Participants consistently reported that Upazila Health Complexes are inadequately equipped to meet the healthcare needs of the rural populations they serve. Although initiatives such as online record-keeping and e-health hotlines do exist on paper, their actual utilization remains limited. Respondents described shortages of information and communication technology equipment, an absence of skilled personnel to operate and maintain digital systems, insufficient training for existing staff, and poor physical infrastructure, including unreliable electricity and connectivity at the facility level. These are not exotic technical problems; they are the mundane, grinding deficits that determine whether a national digital health strategy functions or fails.

Weak integration with the existing health system emerged as another critical barrier. E-health services in Bangladesh have often been introduced as standalone projects, bolted onto health facilities without being woven into referral pathways, patient records, or routine clinical workflows. The study found that this fragmentation undermines both effectiveness and sustainability, because a telemedicine consultation that cannot feed its results into a patient’s medical record, or a hotline that cannot escalate a case to a physical clinic, delivers only a fraction of its potential value. The researchers emphasize that digital tools amplify the performance of a health system rather than substituting for it, so gaps in the underlying system are magnified rather than erased by digitization.

From a technical and methodological standpoint, the study is careful about the limits of its own conclusions. The authors frame their results as preliminary, context-specific observations intended to inform hypothesis generation for future intervention research, rather than as definitive evidence of causal effects. They explicitly recommend that policy recommendations emerging from the findings should be tested through experimental designs before implementation. This caution is warranted in a research area where enthusiasm frequently outpaces evidence, and where well-intentioned digital health programs in low- and middle-income countries have repeatedly failed to survive beyond their pilot phases. The study’s mixed-methods design, however, gives it particular strength: the survey quantifies the scale of the awareness and usage gap, while the interviews explain the mechanisms behind it, from broken equipment to undertrained staff to patients who simply do not know the services exist.

The implications for Bangladesh’s health policy are significant. The country has invested in digital health ambitions for years, and the government has positioned information technology as a pillar of national development. Yet this study suggests that investments concentrated on smartphones and connectivity in people’s hands have not been matched by investments in the facilities where formal e-health services are supposed to be delivered. Addressing the mismatch will require, at minimum, procurement of reliable ICT equipment at Upazila Health Complexes, sustained training programs that build digital skills among health workers, infrastructure upgrades to guarantee power and connectivity, and public awareness campaigns that translate high readiness into actual utilization. Above all, the researchers argue, Bangladesh needs a clear and coherent policy framework that defines how e-health integrates with the broader health system, rather than a patchwork of disconnected initiatives.

For the international global-health community, the findings offer a sobering case study with relevance far beyond Bangladesh. Rural health systems across South Asia, Sub-Saharan Africa and beyond face strikingly similar configurations: rising consumer connectivity, under-resourced public facilities, enthusiastic national digital strategies, and weak last-mile implementation. The Bangladesh study suggests that the binding constraint on e-health effectiveness in such settings is rarely the technology itself. It is the institutional capacity, human resources and system integration that determine whether digital tools reach the patients who need them. Until those foundations are strengthened, the researchers conclude, e-health initiatives will continue to demonstrate significant potential on paper while delivering limited impact in the communities they are designed to serve. The next step, they argue, is rigorous experimental research to identify which interventions, deployed under which conditions, can finally close the gap between digital health’s promise and its performance in rural Bangladesh.

Subject of Research: Effectiveness of e-health services at the Upazila Health Complex level in rural Bangladesh

Article Title: Effectiveness of E health services at Upazila health complex level in Bangladesh

Article References: Bhowmick, A., Rajib, M. M. U., Rahman, M. H., & Roy, S. (2026). Effectiveness of E health services at Upazila health complex level in Bangladesh. Discover Social Science and Health. https://doi.org/10.1007/s44155-026-00461-z

Image Credits: AI Generated

DOI: 10.1007/s44155-026-00461-z

Keywords: e-health, Bangladesh, Upazila health complex, digital health, rural healthcare, health information technology, awareness, readiness, perceived benefits, barriers, telemedicine, health policy