Journal of Medical Internet Research

High-quality online health information (OHI) can reduce unnecessary visits to health professionals and improve health. One of the ways that people use OHI is to support others with health conditions through proxy OHI seeking. Members of a person’s social circle may help them overcome information-seeking barriers and illness challenges. There are several models on proxy information seeking. Yet, we know little about the use and outcomes of OHI on behalf of someone else.

Triage errors are a major concern in health care due to resulting harmful delays in treatments or inappropriate allocation of resources. With the increasing popularity of digital symptom checkers in pre–primary care settings, and amid claims that artificial intelligence outperforms doctors, the accuracy of triage by digital symptom checkers is ever more scrutinized. This paper examines the context and challenges of triage in primary care, pre–primary care, and emergency care, as well as reviews existing evidence on the prevalence of triage errors in all three settings. Implications for development, research, and practice are highlighted, and recommendations are made on how digital symptom checkers should be best positioned.

The Dutch Institute for Clinical Auditing (DICA) Medicines Program was set up in September 2018 to evaluate expensive medicine use in daily practice in terms of real-world effectiveness using only existing data sources.

Despite the growing number of mobile health (mHealth) interventions targeting childhood obesity, few studies have characterized user typologies derived from individuals’ patterns of interactions with specific app features (digital phenotypes).

Effective interventions aimed at correcting COVID-19 vaccine misinformation, known as fact-checking messages, are needed to combat the mounting antivaccine infodemic and alleviate vaccine hesitancy.

Disease status (eg, cancer stage) has been used in routine clinical practice to determine more accurate treatment plans. Health-related indicators, such as mortality, morbidity, and population group life expectancy, have also been used. However, few studies have specifically focused on the comprehensive and objective measures of individual health status.

Recent work has shown the potential of using audio data (eg, cough, breathing, and voice) in the screening for COVID-19. However, these approaches only focus on one-off detection and detect the infection, given the current audio sample, but do not monitor disease progression in COVID-19. Limited exploration has been put forward to continuously monitor COVID-19 progression, especially recovery, through longitudinal audio data. Tracking disease progression characteristics and patterns of recovery could bring insights and lead to more timely treatment or treatment adjustment, as well as better resource management in health care systems.

Web-based crowdfunding has become a popular method to raise money for medical expenses, and there is growing research interest in this topic. However, crowdfunding data are largely composed of unstructured text, thereby posing many challenges for researchers hoping to answer questions about specific medical conditions. Previous studies have used methods that either failed to address major challenges or were poorly scalable to large sample sizes. To enable further research on this emerging funding mechanism in health care, better methods are needed.

The lack of publicly available and culturally relevant data sets on African American and bilingual/Spanish-speaking Hispanic adults’ disease prevention and health promotion priorities presents a major challenge for researchers and developers who want to create and test personalized tools built on and aligned with those priorities. Personalization depends on prediction and performance data. A recommender system (RecSys) could predict the most culturally and personally relevant preventative health information and serve it to African American and Hispanic users via a novel smartphone app. However, early in a user’s experience, a RecSys can face the “cold start problem” of serving untailored and irrelevant content before it learns user preferences. For underserved African American and Hispanic populations, who are consistently being served health content targeted toward the White majority, the cold start problem can become an example of algorithmic bias. To avoid this, a RecSys needs population-appropriate seed data aligned with the app’s purposes. Crowdsourcing provides a means to generate population-appropriate seed data.

During global health crises such as the COVID-19 pandemic, rapid spread of misinformation on social media has occurred. The misinformation associated with COVID-19 has been analyzed, but little attention has been paid to developing a comprehensive analytical framework to study its spread on social media.

Telehealth for emergency stroke care delivery (telestroke) has had widespread adoption, enabling many hospitals to obtain stroke center certification. Telehealth for pediatric emergency care has been less widely adopted.

The digital health sector has experienced rapid growth over the past decade. However, health care technology stakeholders lack a comprehensive understanding of clinical robustness and claims across the industry.