Journal of Medical Internet Research

In the 21st century, health care has been going through a paradigm shift called digital health. Due to major advances and breakthroughs in information technologies, most recently artificial intelligence, the patriarchy of the doctor-patient relationship has started evolving toward an equal-level partnership with initial signs of patient autonomy. Being an underused resource for centuries, patients have started to contribute to their care with information, data, insights, preferences, and knowledge. It is important to recognize that at its core, digital health represents a cultural transformation, where patient empowerment has likely played the most significant role in driving these changes. This viewpoint paper traces the remarkable journey of patient empowerment from its nascent stages to its current prominence in shaping health care’s future. Spanning over two and a half decades, we explore pivotal moments and technological advancements that have revolutionized the patient’s role in health care. We dive into a few historical milestones, mainly in the United States, that have challenged and redefined societal norms around agency, drawing parallels between patient empowerment and broader social movements, such as the women’s suffrage and civil rights movements. Through these lenses, we argue that patient empowerment is not solely a function of knowledge or technology but requires a fundamental shift in societal attitudes, policies, health care culture, and practices. As we look to the future, we posit that the continued empowerment of patients will play a pivotal role in the development of more equitable, effective, and personalized health care systems. This paper calls for an ongoing commitment to fostering environments that support patient agency, access to resources, and the realization of patient potential in navigating and contributing to their health outcomes with an emphasis on the emerging significance of patient design.

Large language models (LLMs) are transforming how data is used, including within the health care sector. However, frameworks including the Unified Theory of Acceptance and Use of Technology highlight the importance of understanding the factors that influence technology use for successful implementation.

Currently, there is a paucity of literature addressing personalized risk stratification using multimodal data in patients with symptomatic aortic stenosis and heart failure with preserved ejection fraction (HFpEF) following transcatheter aortic valve replacement (TAVR).

The global surge in mental health challenges has placed unprecedented strain on health care systems, highlighting the need for scalable interventions to promote mental health self-care. Chatbots have emerged as promising tools by providing accessible, evidence-based support. While chatbots have shown promise in delivering mental health interventions, most studies have only focused on clinical populations and symptom reduction, leaving a critical gap in understanding their preventive potential for self-care and mental health literacy in the general population.

Patient-centered clinical decision support (PC CDS) exists on a continuum that reflects the degree to which its knowledge base, data, delivery, and use focus on patient needs and experiences. A new focus on value-based, whole-person care has resulted in broader development of PC CDS technologies, yet there is limited information on how to measure their performance and effectiveness. To address these gaps, there is a need for more measurement guidance to assess PC CDS interventions.

Perinatal depression and anxiety significantly impact maternal and infant health, potentially leading to severe outcomes like preterm birth and suicide. Aboriginal women, despite their resilience, face elevated risks due to the long-term effects of colonization and cultural disruption. The Baby Coming You Ready (BCYR) model of care, centered on a digitized, holistic, strengths-based assessment, was co-designed to address these challenges. The successful BCYR pilot demonstrated its ability to replace traditional risk-based screens. However, some health professionals still overrely on psychological risk scores, often overlooking the contextual circumstances of Aboriginal mothers, their cultural strengths, and mitigating protective factors. This highlights the need for new tools to improve clinical decision-making.

Despite the growing use of mobile health apps in managing chronic heart disease, their integration into routine care remains challenging due to dynamic, context-specific barriers.

The COVID-19 pandemic has been accompanied by an “infodemic,” where the rapid spread of misinformation has exacerbated public health challenges. Traditional fact-checking methods, though effective, are time-consuming and resource-intensive, limiting their ability to combat misinformation at scale. Large language models (LLMs) such as GPT-4 offer a more scalable solution, but their susceptibility to generating hallucinations—plausible yet incorrect information—compromises their reliability.

Emergency department (ED) admissions are one of the most critical decisions made in health care, with 40% of ED visits resulting in inpatient hospitalization for Medicare patients. A main challenge with the ED admissions process is the inability to move patients from the ED to an inpatient unit quickly. Identifying hospital discharge volume in advance may be valuable in helping hospitals determine capacity management mechanisms to reduce ED boarding, such as transferring low-complexity patients to neighboring hospitals. Although previous research has studied the prediction of discharges in the context of inpatient care, most of the work is on long-term predictions (ie, discharges within the next 24 to 48 hours) in single-site health care systems. In this study, we approach the problem of inpatient discharge prediction from a system-wide lens and evaluate the potential interactions between the two facilities in our partner multisite system to predict short-term discharge volume.

Historically, recruiting research participants through social media facilitated access to people who use opioids, capturing a range of drug use behaviors. The current rapidly changing online landscape, however, casts doubt on social media’s continued usefulness for study recruitment. In this viewpoint paper, we assessed social media recruitment for people who use opioids and described challenges and potential solutions for effective recruitment. As part of a study on barriers to harm reduction health services, we recruited people who use opioids in New York City to complete a REDCap (Research Electronic Data Capture; Vanderbilt University) internet-based survey using Meta (Facebook and Instagram), X (formerly known as Twitter), Reddit, and Discord. Eligible participants must have reported using opioids (heroin, prescription opioids, or fentanyl) for nonprescription purposes in the past 90 days and live or work in New York City. Data collection took place from August 2023 to November 2023. Including study purpose, compensation, and inclusion criteria caused Meta’s social media platforms and X to flag our ads as “discriminatory” and “spreading false information.” Listing incentives increased bot traffic across all platforms despite bot prevention activities (eg, reCAPTCHA and counting items in an image). We instituted a rigorous post hoc data cleaning protocol (eg, investigating duplicate IP addresses, participants reporting use of a fictitious drug, invalid ZIP codes, and improbable drug use behaviors) to identify bot submissions and repeat participants. Participants received a US $20 gift card if still deemed eligible after post hoc data inspection. There were 2560 submissions, 93.2% (n=2387) of which were determined to be from bots or malicious responders. Of these, 23.9% (n=571) showed evidence of a duplicate IP or email address, 45.9% (n=1095) reported consuming a fictitious drug, 15.8% (n=378) provided an invalid ZIP code, and 9.4% (n=225) reported improbable drug use behaviors. The majority of responses deemed legitimate (n=173) were collected from Meta (n=79, 45.7%) and Reddit (n=48, 27.8%). X’s ads were the most expensive (US $1.96/click) and yielded the fewest participants (3 completed surveys). Social media recruitment of hidden populations is challenging but not impossible. Rigorous data collection protocols and post hoc data inspection are necessary to ensure the validity of findings. These methods may counter previous best practices for researching stigmatized behaviors.

Canada is a progressive nation that endeavors to provide comprehensive, universal, and portable health care to all its citizens. This is a challenge for a country with a population of 40 million living within a land expanse of 10 million km2 and where 18% live in rural or highly remote locations. The combined population of Yukon, Northwest Territories, and Nunavut is only 128,959 (0.32% of the population), living within 3.92 million km2, and many of these citizens live in isolated communities with unique health needs and social issues. The current solution to providing health care in the most remote locations has been to transport the patient to the health care provider or vice versa, which incurs considerable financial strain on our health care system and personal stress to the patient and provider. The recent global deployment of low Earth orbit communication satellites (LEO-ComSats) will change the practice and availability of online medicine everywhere, especially in northern Canada. The deployment of LEO-ComSats could result in disruptive but positive changes in medical care for underserved communities in remote geographic locations across Canada. LEO-ComSats can be used to demonstrate online medical encounters between a patient and a doctor in Canada, separated by thousands of kilometers. Most certainly, the academic medical centers in lower Canada could perform online telementored medical care to our northern communities like the remote care provided to many Canadians during the COVID-19 pandemic. An online health care model requires effective design, testing, and validation of the policies, standards, requirements, procedures, and protocols. Although the COVID-19 pandemic was the initial prime mover across all of Canada in the use of online medical encounters and creating rapidly devised reimbursement models, it was nonetheless created reactively, using real-time managerial fiat and poorly defined procedures based on minimal pedagogical experience, which made it “difficult to prove it was universally safe.” It is essential to proactively derive the medical policies, standards, and procedures for telementored medicine and “prove it is safe” before LEO-ComSat technology is ubiquitously deployed in northern Canada. This viewpoint was written by subject matter experts who have researched online and internet-based medicine for many years, sometimes 3 decades. In many cases, a literature review was not necessary since they already had the articles in the bibliography or knowledge in their possession. In many cases, internet search engines (ie, Google or PubMed) and Canadian government documents were used to provide corroborating evidence.

Ecological momentary assessment (EMA) is pivotal in longitudinal health research in youth, but potential bias associated with nonparticipation, omitted reports, or dropout threatens its clinical validity. Previous meta-analytic evidence is inconsistent regarding specific determinants of missing data.