A year after the worst day of her life, Debra Miller received a voicemail she couldn’t quite make out. In a thick accent, a man said something about research and left a phone number. She called but couldn’t get through. “I didn’t know what country code to put in,” she said.
Debra moved on, but the voice kept tumbling through her brain. She was desperate. Her first child, Hawken, had been diagnosed 13 months before with Duchenne muscular dystrophy. In blunt tones she would never forget, a doctor had told her that her 5-year-old boy would slowly lose the ability to walk and die by 18.
When she finally figured out the digits, a Dutch scientist explained he was launching a startup around one of the most counterintuitive ideas in modern genetics: that sometimes you can fix a broken gene by breaking it just a little bit more.
That strategy, known as exon skipping, would taunt Debra for two decades, always promising a therapy just out of reach. It prompted her to raise $1.3 million for the Dutch scientist and helped turn her fledgling advocacy group, CureDuchenne, into a powerhouse. Eventually, the idea spread far beyond the Netherlands and Debra’s home in Newport Beach, Calif., stirring tenuous hope for a life-altering treatment.
Exon-skipping drugs sparked a civil war within the Food and Drug Administration. Under pressure from advocates and companies, a top official overrode reviewers to approve the first of several candidates. One company, Sarepta Therapeutics, has since earned over $5.5 billion from from drugs that may or may not provide much benefit.
Throughout, by the fickle winds of scientific misfortune, mother and son remained waiting — until about two and a half years ago. That’s when Hawken enrolled in a clinical trial for a new exon-skipping drug Debra helped support. The results from him and 38 other patients have since stunned some of the field’s top experts.
Background: Women living in rural agrarian reform communities face intersecting challenges related to social, economic, racial, and gender vulnerabilities, which significantly increase their likelihood of developing physical and mental health problems. Despite the potential of telephone-based interventions to promote mental health, there is a lack of studies assessing their feasibility and effectiveness among underserved populations in Brazil. Objective: This study aimed to assess the feasibility and effectiveness of a telephone-based intervention on mental health outcomes among women living in a rural agrarian reform community in Brazil. Methods: We conducted a descriptive, prospective pilot study with a pretest and posttest design. Data were collected at 3 time points: baseline, 1 week, and 1 month after the intervention. The outcomes assessed included quality of life, social support, self-efficacy, and common mental disorder symptoms. Nonparametric tests were used to analyze the data. The intervention consisted of 3 phone calls supported by a workbook, with content based on cognitive behavioral and psychiatric nursing principles. Results: Of the 31 women enrolled, 23 (74.2%) completed all 3 phone-based sessions. There was a significant reduction in common mental disorder symptoms (Kendall =0.280; =.002), particularly in the somatic domain (=.02). Moreover, participants reported improved perceptions of the physical domain of quality of life (Kendall =0.131; =.049). All women rated the intervention positively, with more than half emphasizing its practical usefulness. Conclusions: The telephone-based intervention was feasible and showed promising results in improving mental health outcomes among women in a rural setting. These findings support integrating low-intensity, remote psychosocial strategies into primary health care, especially those led by nurses, to increase access to mental health promotion for vulnerable populations.
<img src="https://jmir-production.s3.us-east-2.amazonaws.com/thumbs/29b6717618113fd23ab574e12f131acb" />
<strong>Background:</strong> Parent management training (PMT) is an evidence-based intervention for children with disruptive behavior problems. However, access to care is often limited by cost, availability of clinicians, scheduling, and transportation barriers. Integrating artificial intelligence (AI) into group PMT may improve accessibility, personalization, and adherence, while preserving therapeutic quality. <strong>Objective:</strong> This study explored caregivers’ experiences with a hybrid PMT program that combined live therapist-led group sessions with asynchronous support from Pat, an AI conversational agent designed to augment the therapists’ support to caregivers. <strong>Methods:</strong> A total of 88 caregivers of children aged 3-14 years (mean 7.98, SD 2.45 years) from Argentina and Paraguay participated in eight weekly online group sessions led by human therapists and supplemented by Pat. Caregivers were asked to provide the net promoter score (NPS) and their perceived contribution of their experience in remote group sessions and with Pat. Caregiver perspectives were analyzed using thematic analysis by multiple coders with consensus and interrater reliability assessment. <strong>Results:</strong> The average NPS was 76.92, indicating excellent satisfaction. Regarding participants’ perceptions of the overall program, the most frequent theme was useful strategies (73/202, 36.1%), reflecting the value placed on clear, structured, and practical tools to address everyday parenting challenges. Most comments about Pat were positive (156/164, 95.1%), particularly highlighting its 24/7 accessibility and constant availability (69/164, 42.1%). Recommendations for improvement mainly focused on enhancing the user experience and incorporating additional functionalities. Regarding perceived contribution to progress, caregivers attributed, on average, 61% to Pat and 46% to the group sessions. <strong>Conclusions:</strong> Combining therapist-led PMT group sessions with AI support appears feasible, acceptable, and valued by caregivers and may expand reach without sacrificing quality. Overall, caregivers valued the useful strategies and the professional and peer support and reported positive changes. Regarding Pat, the most valued aspect is the constant and immediate support. This model integrates human expertise with the accessibility and continuity provided by AI, reducing barriers, such as time, cost, and clinician availability.
<strong>Background:</strong> China faces a high prevalence of mental disorders but low treatment uptake, a gap driven by limited awareness and unevenly distributed mental health resources. While online psychotherapy has the potential to expand access, patient willingness remains insufficiently explored. <strong>Objective:</strong> This study aimed to investigate the willingness of Chinese patients with mental disorders to engage in online psychotherapy and to identify associated factors. <strong>Methods:</strong> A multicenter, cross-sectional survey was conducted using a structured questionnaire to assess the attitudes and willingness of patients with mental disorders in China to engage in online psychotherapy. Willingness to engage in online psychotherapy was assessed using a 0 to 100 rating scale, with higher scores indicating greater willingness. Univariate analysis, correlation analysis, and multivariate linear regression analyses were used to identify factors influencing willingness. <strong>Results:</strong> Among 361 eligible participants, the mean willingness score for online psychotherapy was 70 (SD 28.56). In total, 86.4% (n=312) of participants preferred short-term therapy (1 to 10 sessions), while 92.5% (n=334) expected the cost per session to remain less than CNY ¥400 (US $55.50). Participants most preferred therapist-guided online individual therapy (n=142, 39.3%). Convenience (124/361, 34.3%) and perceived anonymity (“no one will know about the illness”; 119/361, 33.0%) were the 2 most commonly reported perceived benefits of online psychotherapy. The leading barrier was concerns about data security and privacy (108/303, 35.6%), followed by difficulty in establishing therapeutic rapport (60/303, 19.8%). The regression analysis revealed that age, self-stigma, satisfaction with current psychiatric medications, and satisfaction with previous online psychotherapy significantly influenced patients’ willingness to seek online psychotherapy. <strong>Conclusions:</strong> This multicenter study reveals a high level of willingness to engage in online psychotherapy among Chinese patients, with self-stigma as a key barrier. These findings support the development of tailored services, stigma reduction interventions, and infrastructure investment to enhance mental health care delivery.
As cancer care becomes data-driven, artificial intelligence (AI) will play an increasingly central role across the treatment continuum, from biomarker identification and drug development to clinical trial recruitment and diagnostics. In this corner of healthcare, the ability of AI to interpret and annotate tumor sample slides that have been digitized is taking center stage. While the promise is great, and AI interpretation is already influencing some clinical care, it has not yet reached critical mass.
“There’s something like a billion slides created every year for diagnostic purposes, and today most of those, about 85%, are still read by a pathologist with a microscope on physical glass slides,” said David West, CEO and co-founder of digital pathology company Proscia. In practice, that means pathologists manually examine slides, identify cancer, grade tumors, and dictate reports in a traditional approach to diagnosing cancer that has seen little change in decades.
Mohamed Omar, MD Associate Professor Cedars-Sinai Medical Center
But that foundation is now shifting. Advances in slide scanning, cloud storage, and AI are turning digital pathology images into data that can be analyzed at scale. At Memorial Sloan Kettering Cancer Center, large archives of digitized slides helped launch Paige AI, one of the earliest companies to train deep learning systems on pathology images linked to clinical and genomic outcomes. This yielded the first U.S. Food and Drug Administration (FDA)-approved diagnostic using AI and digital pathology: Paige Prostate Detect. The company, which was acquired last year by AI-enabled precision medicine company Tempus, now combines Paige’s digital pathology-based AI with Tempus’s broad genomic sequencing data platform.
Researchers in the field say the implications of AI in digital pathology extend beyond image analysis. Mohamed Omar, MD, an associate professor of computational biology at Cedars-Sinai Medical Center, Los Angeles, noted that large language models can help clinicians navigate a research landscape that produces “hundreds of papers every single day” to inform ongoing cancer research. Multimodal AI tools promise to unlock even more insights from digital pathology data by combining it with genomic, radiomic, and clinical data to build powerful new models of both common and rare cancers for diagnosis, drug development, and clinical trial enrollment.
Razik Yousfi SVP and GM, Tempus
While adoption is in its early stages, the advent of faster and less expensive scanners is bringing digital pathology within reach of both regional and rural hospitals. Razik Yousfi, senior vice president and general manager of AI products at Tempus, and a co-founder of Paige, predicts that within the next 10 years, the majority of pathology workflows will be digital. The ultimate goal of the application of AI here is not to replace human pathologists, but to empower them with a capable assistant while spreading adoption beyond major medical centers.
Building the foundations
As the field of applying AI to digital pathology progresses, it needs to build the groundwork for a wider range of potential applications that could address rare cancers and other areas without an abundance of data. One such project is called Atlas, a collaboration between researchers in Korea, Germany, and the United States to build a foundation model trained using 1.2 million histopathology whole-slide images from 490,000 cases sourced from the Mayo Clinic and Charité – Universitätsmedizin Berlin.
Foundation models like Atlas allow large-scale pre-training of data to develop numerical representations called embeddings that capture both the structural and contextual features of slides in the dataset. Atlas incorporates a diversity of diseases, staining types, and scanners, and uses multiple image magnifications during training. This broad approach confers power and utility. It allows the digitized representations of the histology to be adapted, queried, or fine-tuned to very specific downstream tasks using much less data than would be needed to build a one-off model.
As such, a foundation model provides a reusable digitized computational backbone that can be tapped across a wide range of uses, like tumor classification, detection of morphologic structures, biomarker quantification, and outcome prediction. In short, foundational models make the process of querying digital pathology images more efficient compared with past approaches.
Andrew P. Norgan, MD, PhD CMO, Mayo Clinic
“In the case of pathology, the successful AI models developed using ‘conventional’ neural network approaches before the advent of FMs (foundation models) typically required huge amounts of training data to achieve high performance and generalizability—the ability to work across datasets distinct from the training data,” said lead Atlas researcher Andrew P. Norgan, MD, PhD, CMO of Mayo Clinic Digital Pathology and assistant professor of laboratory medicine and pathology. “We think of FMs as [an] enabler that allows model development in pathology … to move from artisanal or craft processes to more scalable and reproducible processes that should allow for the rapid development of high-quality models to address problems in pathology.”
At Paige AI, the company’s early work resulted in the first FDA-approved AI diagnostic, Paige Prostate Detect. Its algorithm was built using a technique called multiple instance learning instead of traditional supervised neural network techniques that require detailed human annotation of slides, a time-consuming and expensive method that could expose the learning to human error. The difference between the two methods is that traditional neural networks expose AI to a slide with cancer and tell it that there is cancer present. In multiple instance learning, the model is shown unannotated slides and is tasked with finding the cancer.
Even this approach, however, required a very large dataset. It became apparent to company leaders that the heavy lifting required to get Paige Prostate Detect to work wasn’t scalable.
“We had kind of cracked this recipe,” said Yousfi. “We know how to use a lot of GPU (graphics processing unit) compute, and if we get a ton of data and a lot of compute, we can build anything. But GPU infrastructure is very expensive, and it takes a lot of time to train a very large system.”
Perhaps the most important factor moving Paige away from this model is that it will not work when there is only a small amount of data available. This blocks the ability to train AI to recognize rare cancers for which sample counts are low. The company needed a different approach.
“We had this idea [for] a new system that was basically trained on all of the images we had access to, independent of the organ and indication and tissue and task,” Yousfi said. “Back then, we didn’t know what that thing was called. But ultimately, that became what everyone is calling today a foundation model.”
Originally trained on 200,000 slides, Paige’s new model now includes 3.5 million images and roughly two billion parameters, making it the backbone for other downstream applications the company builds today. This ability to use foundation models as the AI and data encyclopedia for smaller applications will ultimately propel the field of digital pathology forward by widening the playing field.
Going multimodal
To address more complex predictive problems, additional data types can be integrated. Clinical, radiologic, or genomic data can be combined with morphologic embeddings or used during training to help the model learn which tissue features carry a signal of disease or identify a biomarker. These approaches aim to support precision oncology by making morphologic data computable and aligning slide-derived features with other cancer-focused datasets. “These approaches can surface subtle or ‘latent’ patterns in pathology slides and align them with other data sources,” Norgan said. Pathologist and oncology care teams can then evaluate and interpret the features identified by the models within the clinical and biological context.
“In this way, pathologists and oncology teams use these outputs as decision-support tools, while clinical judgment remains central to diagnostic interpretation and therapeutic decision making,” Norgan added.
Atlas has now been succeeded by Atlas2, which was trained on 5.5 million pathology images and is now a two billion-parameter model, making it one of the largest pathology foundation models to date. The team has explored distilling methods to create smaller, more efficient, and targeted versions of the model that retain performance, with an eye toward finding a balance between scale and deployability.
Proscia is embarking on a different multimodal approach that combines vision models with language models, with the intent of creating methods to query the morphology of digitized slides. Their efforts in vision-language models (VLMs) combine textual data with visual data and allow the model to describe the morphology of a slide, answer questions about what it contains, find images in a database based on a text query, and even follow multimodal instructions such as “circle the tumor area on this image.”
In short, a VLM can be engaged in the same way you can engage a human. “I could go ask a pathologist to point out all the areas of tumor-infiltrating lymphocytes,” West said. “Now, because language-vision models are encoding language and images in the same space, they can do that, too. You can ask the model to describe what is happening in an image, and it will tell you exactly what it sees.”
At Cedars-Sinai, Omar’s work with large language models takes a less direct route of leveraging queries to gather information from research studies or even images. “Basically, you could go to the tool, ask questions, and the tool will provide you with pieces of code,” he explained. “These pieces of code are what you use on the slide to get more information.”
Atlas provides a similar function at the Mayo Clinic, Norgan noted. Because the model-generated embeddings in the digitized slide also encode semantic information, the Atlas team is now building a slide search function, which would allow researchers or clinicians to identify and access slides, or regions of slides, with related features.
Democratizing care
Although it will take time to disseminate the tools needed for AI-enabled digitized models of cancer care to smaller health systems, the future is now at Moffitt Cancer Center, where the research hospital is engaged in a top-to-bottom digitization of its system.
Marilyn Bui, MD, PhD Senior Member Moffitt Cancer Center
According to Marilyn Bui, MD, PhD, senior member of the departments of pathology and machine learning, the comprehensive cancer center plans for full digital adoption across clinical and research labs by 2027. Last August, it entered a multi-year collaboration with integrated AI and digital pathology company PathAI to deploy its cloud-based digital pathology image management system for both research and clinical applications.
Within the pathology department, the transition will mean that all glass slides will be scanned and reviewed digitally, providing the basis for applying AI computational tools to assist pathologists. Bui said that the cancer center is accelerating its move toward clinical AI adoption: “Just today I received an email asking which AI algorithms we plan to incorporate for clinical utility—prostate cancer, breast cancer, general tumor detection,” she said. “For us, it’s no longer just research.”
Moffitt is taking a hybrid approach to algorithm development and deployment within the system. Some AI tools will come from commercial vendors and will be validated internally, while others will be developed by investigators through the center’s translational pathology work. Taking this approach will allow it to apply AI to both common cancers and the rare tumor types Moffitt frequently encounters.
While the digital initiative will be transformational, Bui emphasized that the goal is not to replace pathologists but to enhance their capabilities. She prefers to refer to AI as augmented intelligence to reflect this. “Artificial intelligence suggests a robot replacing us,” she said. “But what we mean is augmented intelligence—tools that assist and enhance our ability to make clinical decisions.”
Further, Moffitt intends to integrate digitized slide data with genomic, proteomic, and clinical outcome data to build a multimodal data environment that could advance precision oncology. “Digital pathology and AI will allow us to extract far more information from tissue samples,” Bui said, “making our diagnoses more actionable for the clinical team and ultimately improving patient care.”
The promise of AI in oncology isn’t just better algorithms, it’s broader access. The maturation of computational pathology and its dissemination from large cancer centers like Moffitt to regional and rural health systems has the potential to provide levels of care typically only available at large research hospitals in community settings as well.
“It’s about democratizing access to care,” said Omar. “For a person in Maine or Wisconsin or another place to have access to the same high-quality care that you would get from a larger academic medical center in LA or New York, slides have to be digitized.”
Over the next 10 years, there could be a compelling business case for hospitals to embrace digital pathology. As the cost of scanners comes down and a broad range of diagnostic tools becomes available, digitizing routine H&E slides could become common.
While genetic cancer testing can cost hundreds of dollars, Omar pointed out that pathology slides “cost $5 [and] they are available universally, in all patients with cancer.” As AI models increasingly identify genomic-level insights directly from those inexpensive images, it represents a “huge win for accessibility, making AI work for patients who cannot afford genetic tests,” Omar said. If there is broad adoption of digital pathology “it is very easy to roll out any kind of AI models and computational tools across the board, across situations and locations that don’t have access to care.”
“At the end of the day, all slides will be digitized,” he concluded. “It’s just a matter of time.”
Chris Anderson, a Maine native, has been a B2B editor for more than 25 years. He was the founding editor of Security Systems News and Drug Discovery News, and led the print launch and expanded coverage as editor in chief of Clinical OMICs, now named Inside Precision Medicine.
Background: Digital mental health technologies (DMHTs) are playing an increasing role in mental health services. The quality of evidence for DMHTs is variable, and there are concerns that evidence is not sufficient to support decision-making. Objective: This study used a cross-sectional analysis of evidence supporting DMHTs included in National Institute for Health and Care Excellence (NICE) evaluations to examine the strength of evidence available for decision-making. Methods: We identified all NICE evaluations relating to DMHTs by reviewing details of published NICE evaluations on the NICE website. From each of these evaluations, we identified included DMHTs and reviewed committee documentation to identify studies that provided supporting evidence for each of these technologies. We extracted information on a series of items relating to study quality and summarized the characteristics of evidence both at the level of individual studies and across the package of evidence from multiple studies supporting DMHTs. We also identified key evidence gaps in available evidence. Results: We included nine NICE evaluations relating to anxiety, depression, psychosis, insomnia, attention deficit hyperactivity disorder (ADHD), and tic disorders. These evaluations included 30 DMHTs and referenced 78 supporting studies. We identified common evidence gaps relating to effectiveness compared to relevant comparators, use of appropriate outcomes, including health-related quality of life, cost of delivery, and impact on resource use, and reporting of adverse events. Conclusions: Our study highlights that some DMHTs have been supported by high-quality studies and that evidence to support DMHTs is likely to be developed across a series of studies. However, there are often key evidence gaps that need to be addressed to provide a stronger case for adoption. Developers should ensure that they consider these gaps while planning evidence generation, and where possible, address them earlier in the product lifecycle.
<img src="https://jmir-production.s3.us-east-2.amazonaws.com/thumbs/825f13db8cbad54213afa5c433d7adde" />
Researchers at the Institute for Biomedical Sciences at Georgia State University have developed a vaccine platform designed to provide protection against a range of influenza virus infections by targeting conserved viral structures and inducing immunity at mucosal surfaces. The study, published in ACS Nano, details how the development of the novel vaccine uses cell-derived extracellular vesicles (EVs) engineered to display multiple influenza hemagglutinins (HAs) in an inverted configuration, which allows the immune system to recognize conserved regions shared across viral strains. In mouse models, the vaccine elicited cross-reactive antibodies, cellular immune responses, and mucosal immunity, providing protection against heterosubtypic H5N1 and H7N9 influenza virus challenges following intranasal administration.
“The influenza virus is smart. They have evolved to evade the immune system by hiding their critical conserved structures, rendering these elements poorly immunogenic,” said senior author Bao-Zhong Wang, PhD, a professor at the Institute for Biomedical Sciences at Georgia State.
The vaccine’s design centers on two key features: the use of extracellular vesicles (EVs) as a delivery platform and the inversion of HA proteins on their surface. EVs are natural nanoparticles involved in cell-to-cell communication and have been researched extensively for therapeutic delivery due to their biocompatibility. In this study, they were engineered to display multiple HA subtypes simultaneously. The HAs were presented in an upside-down orientation, which partially shields the highly variable head domain while exposing the conserved stalk domain. This structural arrangement directs the immune response toward regions less prone to mutation, enabling broader protection across influenza strains.
“These (vaccine responses) highlight that the inverted HA is a smarter strategy for inducing protective immunity to the conserved HA stalk. Meanwhile, cell-origin EVs are a biocompatible platform for mucosal vaccine delivery. Using EVs simultaneously displaying multiple inverted HAs is a powerful approach for developing universal influenza vaccines,” Wang added.
To test their vaccine design the researchers immunized mice intranasally with the EV-based vaccine, allowing researchers to assess mucosal immunity in addition to systemic responses. The data showed that the vaccine induced cross-reactive antibodies targeting HA stalks, virus-specific T cell responses, and a balanced Th1/Th2 immune profile. Importantly, the vaccinated mice were fully protected against lethal infections from reassortant H5N1 and H7N9 viruses.
The new vaccine designed was based on previous research into EV-based vaccine delivery and different strategies to target the HA stalk. Earlier studies had shown that EVs could serve as adjuvants and antigen carriers for intranasal vaccines. In addition, other research seeking to develop a universal influenza vaccine has focused on the conserved HA stalk domain, which evolves more slowly than the immunodominant head. As the researchers noted, “the conserved HA stalk domain has emerged as a promising candidate for a universal influenza vaccine due to its low evolutionary rate and greater tolerance to mutations.” However, previous approaches had used isolated HA stalk constructs, but faced shortcoming related to structural stability and immunogenicity.
By preserving the full HA ectodomain while inverting its orientation, the new design addressed these limitations. “Our findings suggest that utilizing the entire HA ectodomain as an immunogen, while hiding the HA head and increasing exposure of the HA stalk, is an effective strategy to induce robust immune responses targeting conserved HA epitopes,” the researchers wrote, noting that this approach allows the immune system to access structurally intact conserved regions.
If this vaccine design can be shown effective in humans, it could provide a vaccine with broader and longer-lasting protection against influenza, and reduce the need for frequent reformulations. The use of intranasal delivery could also change how vaccines are administered by targeting immune responses at the site of viral entry. “Mucosal vaccination effectively induces local immune responses, protecting against respiratory virus infections at the site of invasion,” the team noted.
Next steps for the team include continued characterization of the immune responses induced by the vaccine, to include the specificity and neutralizing capacity of antibodies, as well as evaluation of anti-EV immunity with repeated dosing. More animal studies, and eventually clinical trials, will be needed to assess safety, scalability, and efficacy in humans.
Background: The universal adoption of mobile technologies by households has created an opportunity to provide families with young children with access to high-quality oral health information at convenient times and locations. Using community agencies (eg, Head Start and public health programs) that offer parenting education is an effective approach to reaching families in low-income households. Objective: This study aimed to explore the extent to which a coordinated, in-person oral health prevention intervention, together with an accompanying smartphone app, BeReadyToSmile, is feasible to implement among caregivers of young children. Methods: The BeReadyToSmile program targeted parents of children aged 0 to 6 years attending parenting education classes. This study was designed as a single-group pre-post feasibility study that included quantitative surveys and open-ended feedback. A total of 30 parents attended an in-person session on child oral health and were invited to use the BeReadyToSmile smartphone app. Preintervention and postintervention surveys were administered to assess pediatric oral health knowledge, attitudes toward child toothbrushing, brushing intention, brushing efficacy, program satisfaction, and ease of use. Results: Significant effects were observed on parent-reported pediatric oral health knowledge, attitudes toward brushing, brushing intention, and toothbrushing efficacy. Out of the 30 parents invited to use the BeReadyToSmile app, 1 (3%) completed no sessions and 20 (67%) completed all sessions. Participants rated the app highly on measures of satisfaction and use. We found significant increases in pediatric oral health knowledge (.004), child brushing attitudes and intention (=.01), and parental efficacy regarding child toothbrushing (=.03). Conclusions: Caregivers reported positive experiences with the implementation of BeReadyToSmile, indicating the overall feasibility of delivering oral health prevention to households with young children both in person and through a facilitated smartphone app. Further studies should include a larger and more diverse sample, randomized comparison conditions, and a longer follow-up period to assess outcomes. Trial Registration: ClinicalTrials.gov NCT03637309;
<img src="https://jmir-production.s3.us-east-2.amazonaws.com/thumbs/337d501308d195f5f436e32d7939b2c5" />
Background: Health care professionals’ perceptions of telemedicine, its usability, and the presence of organizational barriers are important determinants of the successful implementation of digital solutions in health care. In Kazakhstan, the use of international assessment instruments requires contextual adaptation. The Telehealth Usability Questionnaire-Model for Assessment of Telemedicine-Kazakhstan version (TUQ-MAST-KZ) questionnaire was previously developed and psychometrically validated by integrating elements of the TUQ and MAST frameworks to assess perceptions of telemedicine within the national context. Objective: The aim of this study was to conduct the first pilot application of the TUQ-MAST-KZ questionnaire with physicians in Kazakhstan and perform an initial assessment of the organizational, technical, and educational aspects of telemedicine implementation. Methods: This cross-sectional study involved an anonymous online survey using the TUQ-MAST-KZ questionnaire, which covers perceptions of telemedicine, formats of use, platform usability, communication-related aspects, telemonitoring, organizational conditions, and implementation barriers. Responses from 156 physicians were analyzed. Stratified nonparametric comparisons were performed by sex, age group, work experience (years), and workplace, adjusted for multiple comparisons. Results: The most used telemedicine formats were telephone consultations (78/156, 50%), video consultations (69/156, 44.2%), chats and messaging applications (57/156, 36.5%), and mobile apps (48/156, 30.8%). The Kazakhstan National Telemedicine Network was used by 14.7% (23/156). Wearable devices were used by 5.8% (9/156). Telemedicine technologies incorporating artificial intelligence elements were used regularly by 13.5% (21/156) and occasionally by 32.1% (50/156) and not used by 50.6% (79/156). Positive ratings were as follows: 48.7% (76/156) regarding the simplicity and intuitiveness of telemedicine platforms; 56.4% (88/156) regarding the timeliness of patient condition monitoring; 51.9% (81/156) regarding the effectiveness of telemedicine for the management of patients with chronic diseases. The potential usefulness of telemonitoring for earlier detection of deterioration of a patient’s condition was rated as fairly or very high by 48.7% (76/156); 41% (64/156) rated it as moderate. Only 35.9% (56/156) positively rated the connection’s reliability and stability. Regarding the accuracy of wearable device data transmission, 57.1% (89/156) responded neutrally, potentially indicating ambiguity in perception, limited personal experience, or difficulty evaluating this aspect. Readiness to recommend telemonitoring at the national level was more often rated as moderate, high, or very high (78/156, 50%; 42/156, 26.9%; 14/156, 9%, respectively). Conclusions: This pilot application of the TUQ-MAST-KZ questionnaire showed a generally moderately positive perception of telemedicine by physicians, who recognized its potential clinical and organizational value. However, we identified substantial technical and institutional barriers, including connection instability, concerns about the accuracy of data transmission, insufficient process formalization, and a need for additional training. These preliminary findings should be interpreted in light of the pilot study design; however, they may serve to inform future larger-scale research and the development of organizational measures related to physician training, protocol standardization, and infrastructure support for telemedicine implementation.
<img src="https://jmir-production.s3.us-east-2.amazonaws.com/thumbs/804a11a35fc36f67f33a929fd1435eba" />
