Journal of Medical Internet Research

Computerized critical care information systems (CCIS) can have a range of positive to negative impacts on clinical care in ICUs and the job satisfaction of ICU staff. Key factors influencing these effects include the usability of the IT system and the level of training provided. Resistance to using the system may arise from users due to increased control imposed by the system and from insufficient participation in its development and configuration. The usability of CCIS, along with other important barriers such as co-determination, has not been thoroughly examined.

Emergency toxicology is a complex field requiring rapid and precise decision-making to manage acute poisonings effectively. Toxic exposures are often unpredictable, and the constraints of time and resources often challenge conventional diagnostic and treatment approaches. Artificial intelligence (AI) has emerged as a valuable tool in emergency medicine, offering the potential to enhance diagnostic accuracy, predict clinical outcomes and improve clinical decision support systems. Despite the increasing focus of AI in medicine, its applications in emergency toxicology are still under-explored. This viewpoint aims to provide perspectives on AI applications in emergency toxicology by highlighting key advancements, challenges, as well as future directions. While AI has demonstrated significant potential in improving toxicological predictions through various applications, challenges such as data quality, regulatory concerns, and implementation barriers are still hurdles to its use. Further research, regulatory frameworks and integration strategies are needed to ensure effective and ethical implementation in clinical practice.

A digital pain manikin is a measurement tool that presents a diagram of the human body where people mark the location of their pain to produce a pain drawing. Digital pain manikins facilitate collection of more detailed spatial pain data compared to questionnaire-based methods and are an increasingly common method for self-reporting and communicating pain. An overview of how digital pain drawings, collected through digital pain manikins, are analyzed and summarized is currently missing.

Social media has become a vital platform for hospitals to engage with the public, disseminate health knowledge, and build trust. While promotional strategies have shown potential to enhance social media influence, the mechanisms through which these strategies impact hospital influence on social media remain unclear. Furthermore, the effectiveness of these strategies may vary across hospitals of different types and regions, necessitating a deeper understanding of their contextual applicability.

The All of Us Research Program (AoURP) is a prominent precision medicine research initiative committed to diverse participation. The program harnesses digital outreach as a key strategy for recruiting and retaining underrepresented populations, using language that sometimes invokes notions of solidarity. This targeted recruitment of underrepresented groups and potential use of solidaristic language raise concerns about how participation will manifest tangible benefits for these populations and whether institutions assume responsibility for addressing past and present research harms.

Social media platforms offer valuable insights into the patient’s experience, revealing organic conversations that reflect their immediate concerns and needs. Through active listening to lived experiences, we can identify unmet needs and discover real-world challenges patients and caregivers face.

Online surveys have become a key tool of modern health research, offering a fast, cost-effective, and convenient means of data collection. It enables researchers to access diverse populations, such as those underrepresented in traditional studies, and facilitates the collection of stigmatized or sensitive behaviours through greater anonymity. However, the ease of participation also introduces significant challenges, particularly around data integrity and rigour. As fraudulent responses – whether from bots, repeat responders, or individuals misrepresenting themselves – become more sophisticated and pervasive, ensuring the rigour of online surveys has never been more crucial. This article provides a comprehensive synthesis of practical strategies that help to increase the rigour of online surveys through the detection and removal of fraudulent data. Drawing on recent literature and case studies, we outline several options that address the full research cycle from pre-data collection strategies to post-data collection validation. We emphasize the integration of automated screening techniques (e.g. CAPTCHAs, honeypot questions) and attention checks (e.g. trap questions) for purposeful survey design. Robust recruitment procedures (e.g. concealed eligibility criteria, two-stage screening) and a proper incentive or compensation structure can also help to deter fraudulent participation. We examine the merits and limitations of different sampling methodologies, including river sampling, online panels, and crowdsourcing platforms, offering guidance on how to select samples based on specific research objectives. Post-data collection, we discuss meta-data based techniques to detect fraudulent data (e.g. duplicate email or IP addresses, response time analysis), alongside methods to better screen for low quality responses (e.g. inconsistent response patterns, improbable qualitative responses). The escalating sophistication of fraud tactics, particularly with the growth of Artificial Intelligence, demands that researchers continuously adapt and stay vigilant. We propose the use of dynamic protocols, combining multiple strategies into a multi-pronged approach that can better filter for fraudulent data and evolve depending on the type of responses received across the data-collection process. However, there is still significant room for strategies to develop, and it should be a key focus for upcoming research. As online surveys become increasingly integral to health research, investing in robust strategies to screen for fraudulent data and increasing the rigour of studies is key to upholding scientific integrity.

Multimodal data integration systematically combines complementary biological and clinical data sources such as genomics, medical imaging, electronic health records, and wearable device outputs. This approach provides a multidimensional perspective of patient health that enhances the diagnosis, treatment, and management of various medical conditions. This viewpoint presents an analysis of multimodal integration in healthcare spanning clinical applications, current challenges, and future directions. We focus primarily on its applications across different disease domains, particularly in oncology and ophthalmology. Other diseases are briefly discussed due to the few available literature. In oncology, the integration of multimodal data enables more precise tumor characterization and personalized treatment plans. Multimodal fusion demonstrates accurate prediction of anti-HER2 therapy response (AUC 0.914). In ophthalmology, multimodal integration through the combination of genetic and imaging data facilitates the early diagnosis of retinal diseases. However, substantial challenges remain regarding data standardization, model deployment and model interpretability. Future developments will likely focus on multimodal integration, including its expanded disease applications such as neurological and otolaryngological diseases, and the trend towards large-scale multimodal models, which enhance accuracy. Overall, the innovative potential of multimodal integration is expected to further revolutionize the healthcare industry, providing more comprehensive and personalized solutions for disease management.

Mental health (MH) issues are the leading cause of mortality for young people, highlighting the importance of timely, high-quality, and affordable care. However, recent trends show a deceleration in the growth of youth mental health (YMH) services capacity in Australia. Meanwhile, digital interventions hold significant potential to sustain and enhance YMH outcomes.

China’s 3-child policy has increased the demand for scientific and personalized pregnancy health management. The convenience of mobile health has promoted the use of pregnancy management apps among pregnant women. User satisfaction has a significant impact on continued use intention. Systematically evaluating user satisfaction with pregnancy management apps is of great significance in promoting the digital transformation of maternal-infant health care.

e-Cigarette use is a growing public health concern, with e-cigarettes being marketed by social media influencers on Instagram. Influencers promote e-cigarettes using misleading relative harm claims, portraying them as safer than regular cigarettes while overstating benefits and selectively omitting information on the harms. To counter this, the US Federal Drug Administration requires influencers to include a nicotine warning label in their sponsored posts, similar to the ones used on e-cigarette packages. However, research on their effectiveness remains limited, leaving questions about when, how, and for whom these warnings work.

The dementia landscape has evolved, with earlier diagnoses, improved prevention understanding (eg, modifiable factors), and new treatments. Emerging digital technologies (eg, wearables, smart home systems, and mobile apps) offer self‑management opportunities; yet, gaps persist regarding integration into the care needs and preferences of people with dementia. Broader gaps remain concerning intervention design; adaptation; and implementation, including effectiveness, study quality, and accessibility.