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

The research team interviewed eleven care management staff from four geographically diverse health centers and five subject matter experts in health information technology, all drawn from OCHIN, a nonprofit that hosts a shared instance of the Epic EHR for more than 2,000 community health center clinics across 37 states, the largest such network in the country. In 2022, OCHIN activated Epic’s care management module, called Compass Rose, which is designed to support care coordination activities including the tracking of contextual drivers such as food, housing, and transportation insecurity. Yet at the time of the study, fewer than 10 percent of OCHIN member clinics had ever used the module to make and track referrals for these needs, a striking adoption gap that motivated the investigation.

Methodologically, the study was a formative evaluation informed by human-centered design principles, conducted between September 2023 and July 2024. The researchers used semi-structured interviews paired with a virtual “guided tour” technique, in which participants shared their screens and demonstrated how they actually navigated the EHR to document screening, make referrals, and follow up, using a dummy patient record. Transcripts were analyzed with a rapid qualitative analytic approach organized around the Integrated Technology Implementation Model, and the findings were distilled into a care manager journey map that traces each step of addressing contextual drivers, the tasks involved, and the barriers that surface at each one. Preliminary findings were shared back with participants to verify accuracy.

Step one of the journey, screening, is the system’s relative bright spot. Health centers screen for contextual needs at check-in, rooming, or care management enrollment, and results are captured in EHR flowsheets that support longitudinal tracking. Some centers have configured alerts that prompt any staff member opening a chart to screen, and shortcuts allow quick insertion of relevant text into notes. Once documented, social risk data appear as color-coded icons in the EHR’s patient summary and within the care management module itself, giving care managers a consolidated view. One care manager described the appeal plainly: everything from screenings to disease-specific questions is “all right there,” eliminating the need to hunt across the chart for information relevant to care planning.

Step two, making referrals, is where the machinery begins to grind. For internal referrals to social workers or community health workers, care managers can use EHR messaging and referral ordering. For external referrals to community-based organizations such as food pantries or transportation services, some centers use community resource referral systems like Findhelp or Unite Us, platforms increasingly integrated with EHRs to enable closed-loop communication with registered organizations. But adoption of these platforms is hampered by two major challenges the participants identified: the effort and cost required for both clinics and community organizations to establish the referral model, and the fact that many community-based organizations lack the technological infrastructure to engage in the required data exchange at all. Relationships, participants stressed, matter as much as software; one expert recounted how a center’s referrals went unaccepted because the organizations it wanted to reach had never been onboarded to the platform.

Documentation of referrals proved equally problematic. Most EHR tools as configured at the participating centers offer no discrete field for referral documentation outside a specific referral work queue, and that work queue demands extensive manual entry and is not linked to care management care plans. Within the care management module, care plans include only pre-set, general referral options that must be customized by hand, a task one care manager called a “huge slowdown” that is “very, very time-consuming.” Worse, care plans built inside the module do not propagate to other views of the chart, forcing care managers to re-enter the same information elsewhere so that other care team members can see it. As one staff member explained, the care plan exists only in the module and “doesn’t cross over into any other part” of the record, so visibility elsewhere requires duplicative documentation. The practical result is that much referral information ends up in free-text notes, where it cannot be systematically tracked.

Step three, tracking whether patients actually connected with services, exposes the deepest gaps. Community resource referral systems could in principle close the loop by letting community providers report service receipt back into the EHR, but their limited adoption leaves most centers without that capability. Only referrals routed through the standard EHR referral work queue can be followed longitudinally, and even then, the lack of integration with care management care plans means outcome dispositions must be re-entered in multiple locations. Referrals documented in care plans through free text or shortcuts were not reliably trackable at all. One staff member admitted that after years of doing this work, the center had no formal referral pathway to community partners and could barely “fathom how that would work” given the scale of capturing and closing referrals. Notably, the care management module contains a “tasking” dashboard feature that could support referral tracking, but care managers reported they rarely use it for this purpose simply because they never received adequate training on how.

The study’s authors argue that these barriers can be sorted into three categories: technical, organizational, and those requiring implementation support rather than tool redesign. On the technical side, structured data fields for referrals, care plans customized to each center’s specific programs and reimbursement requirements, and alignment with emerging documentation standards such as those promoted by the Gravity Project could make referral data visible, actionable, and reportable. On the organizational side, the findings highlight a structural tension: health centers are investing heavily in EHR tools and workflows, but the community organizations receiving referrals are often under-resourced to participate in electronic exchange. Without investment in the infrastructure and readiness of those partners, even well-designed platforms will be underused, a problem likely to be most acute in communities with the fewest available services. The authors suggest implementation strategies that include assessing community organization readiness, mapping feasible referral pathways, and building backup workflows when electronic exchange is not possible.

Implementation support emerges as the study’s central prescription. Participants described their training on the care management module as something they “learned as we went,” with comments like “there wasn’t much training available” and wishes for “a much better training program.” Experts confirmed that hands-on, step-by-step guidance with demonstration and practice is the single biggest lever for improving uptake, and that support must be ongoing, since staff reported questions persisting even one or two years after adopting the tools. Leadership backing, workflow redesign coaching, and site-specific configuration assessments before training begins round out the recommended support package. These findings will now feed directly into a cluster-randomized trial, registered on ClinicalTrials.gov, that will test implementation strategies aimed at helping care management teams adopt EHR-based tools for social care coordination, an effort the researchers describe as the first of its kind. The study’s limitations are acknowledged: a small sample, a single EHR system, and the absence of community organization perspectives, though the authors note that Epic’s dominance in health centers and the breadth of roles interviewed strengthen the findings’ relevance.

The broader significance is hard to overstate. Federal and state programs, from Medicare’s Chronic Care Management codes to California’s CalAIM Enhanced Care Management, increasingly require the screening of social needs and linkage to services, and value-based payment models tie reimbursement to that documentation. If the electronic backbone meant to support this work remains siloed and untracked, centers risk both missed interventions for vulnerable patients and unfulfillable reporting obligations. This evaluation offers a precise map of where the technology works, where it breaks, and what a coordinated fix would require: better configuration, shared visibility across teams, genuine two-way infrastructure with community partners, and sustained, hands-on support for the people asked to use it.

Subject of Research: Adoption of electronic health record-based care management technologies for addressing social needs in community health centers

Article Title: Community Health Center Adoption of Enabling Technologies to Address Contextual Drivers of Health for Care-Managed Patients: Formative Evaluation

Article References: Owens-Jasey, C., Gunn, R., Cook, N., Fein, H. L., Pisciotta, M., Fee, C., Larson, Z., Templeton, A., & Gold, R. (2026). Community Health Center Adoption of Enabling Technologies to Address Contextual Drivers of Health for Care-Managed Patients: Formative Evaluation. Journal of General Internal Medicine. https://doi.org/10.1007/s11606-026-10735-6

Image Credits: AI Generated

DOI: 10.1007/s11606-026-10735-6

Keywords: community health centers, electronic health records, care management, social needs, care coordination, health information technology, referrals, implementation science, contextual drivers, social determinants of health, qualitative research, health informatics