Journal of Medical Internet Research
The leading peer-reviewed journal for digital medicine and health and health care in the internet age.
Editor-in-Chief:
Gunther Eysenbach, MD, MPH, FACMI, Founding Editor and Publisher; Adjunct Professor, School of Health Information Science, University of Victoria, Canada
Impact Factor 5.8 CiteScore 14.4
Recent Articles

Vaping rates in Canada are continuing to increase. In 2019, 4.7% of Canadians used an electronic cigarette (e-cigarette) in the past 30 days, which rose to 5.8% in 2022. In the same year, young adults aged 20-24 years demonstrated the highest use among Canadians, at 19.7%. Given this, existing interventions are not resulting in the desired outcomes, and smartphone apps have the potential to address this gap. Although limited, current evidence highlights that apps can be an effective cessation support; however, a gap persists in understanding the user experience of vaping cessation apps.

Real-world data (RWD) from sources like administrative claims, electronic health records, and cancer registries offer insights into patient populations beyond the tightly regulated environment of randomized controlled trials. To leverage this and to advance cancer research, 6 university hospitals in Bavaria have established a joint research IT infrastructure.

Sub-Saharan Africa (SSA) accounts for up to 67% of the global HIV burden yet grapples with health system challenges like distant health facilities, low doctor-to-patient ratio, and poor or non-functioning post-hospital follow-up mechanisms. The rising phone ownership and internet penetration in SSA (46% and 67%, respectively) offer an opportunity to leverage technology to address these gaps and drive toward achieving the UNAIDS (Joint United Nations Programme on HIV and AIDS) 95-95-95 targets.

Following the proposal by Tsafnat et al (2024) to converge on three open health data standards, this viewpoint offers a critical reflection on their proposed alignment of openEHR, Fast Health Interoperability Resources (FHIR), and Observational Medical Outcomes Partnership (OMOP) as default data standards for clinical care and administration, data exchange, and longitudinal analysis, respectively. We argue that open standards are a necessary but not sufficient condition to achieve health data interoperability. The ecosystem of open-source software needs to be considered when choosing an appropriate standard for a given context. We discuss two specific contexts, namely standardization of (1) health data for federated learning, and (2) health data sharing in low- and middle-income countries. Specific design principles, practical considerations, and implementation choices for these two contexts are described, based on ongoing work in both areas. In the case of federated learning, we observe convergence toward OMOP and FHIR, where the two standards can effectively be used side-by-side given the availability of mediators between the two. In the case of health information exchanges in low and middle-income countries, we see a strong convergence toward FHIR as the primary standard. We propose practical guidelines for context-specific adaptation of open health data standards.

Concerned significant others (CSOs) play a significant role in supporting individuals with substance use disorders. There is a lack of tailored support services for these CSOs, despite their substantial contributions to the well-being of their loved ones (LOs). The emergence of helplines as a potential avenue for CSO support is outlined, culminating in the focus on the Partnership to End Addiction’s helpline service, an innovative public health intervention aimed at aiding CSOs concerned about an LO’s substance use.

Prevention of drug-induced QT prolongation (diLQTS) has been the focus of many system-wide clinical decision support (CDS) tools, which can be directly embedded within the framework of the electronic health record system and triggered to alert in high-risk patients when a known QT-prolonging medication is ordered. Justification for these CDS systems typically lies in the ability to accurately predict which patients are at high risk; however, it is not always evident that identification of risk alone is sufficient for appropriate CDS implementation.

Digital health has become integral to public health care, advancing how services are accessed, delivered, and managed. Health organizations increasingly assess their digital health maturity to leverage these innovations fully. However, existing digital health maturity models (DHMMs) primarily focus on technology and infrastructure, often neglecting critical communication components.

Crisis hotlines serve as a crucial avenue for the early identification of suicide risk, which is of paramount importance for suicide prevention and intervention. However, assessing the risk of callers in the crisis hotline context is constrained by factors such as lack of nonverbal communication cues, anonymity, time limits, and single-occasion intervention. Therefore, it is necessary to develop approaches, including acoustic features, for identifying the suicide risk among hotline callers early and quickly. Given the complicated features of sound, adopting artificial intelligence models to analyze callers’ acoustic features is promising.

Certain populations are underrepresented in clinical trials, limiting the generalizability of new treatments and their efficacy and uptake in these populations. It is essential to identify and understand effective strategies for enrolling young adults in clinical trials, as they represent a vital and key demographic for future clinical trial participation.


Depression affects more than 350 million people globally. Traditional diagnostic methods have limitations. Analyzing textual data from social media provides new insights into predicting depression using machine learning. However, there is a lack of comprehensive reviews in this area, which necessitates further research.
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