Delays in accessing ambulatory care create significant operational strain for health systems and lead to adverse outcomes and diminished experiences for patients. While manual waitlists are resource-intensive and difficult to scale, automated waitlists leverage technology to monitor schedules continuously and send automated notifications to patients when earlier slots open up.

In the fourth and final blog entry of our Healthy Aging® Month series, Eliza Wong, PhD from the Jockey Club School of Public Health and Primary Care at The Chinese University of Hong Kong shares insights from her team’s latest research published in JMIR Aging.

In the third post for our Healthy Aging® Month video series, Hsiu-Ping Yueh, PhD fromNational Taiwan University gives an overview of her team’s research published in JMIR Aging. Joining her in the video are two Kebbi social robots, her "co-stars" in studying how technology can tackle social isolation in older adults.

In the second installment of our Healthy Aging® Month series, Baldwin Pok Man Kwan, MPH; a physiotherapist and project officer at Silverchain, shares insights from his team's recent research published in JMIR Aging.

This September for Healthy Aging® Month, JMIR Aging is bringing peer-reviewed science off the page and into daily life. We are spotlighting four articles and their lead authors in a dedicated video series that illustrates the direct connection between academic research and real-world impact. Each feature demonstrates how evidence-based digital health solutions are moving beyond theoretical design to actively empower older adults, caregivers, and technology developers, showing how digital tools can foster independence, purpose, and vitality in everyday living. In our first video feature for the series, Anna Whittaker, PhD from the University of Stirling shares key insights from her research published in JMIR Aging.

Ask any peer reviewer or editor what's changed most about their workload in the last two years, and generative artificial intelligence (AI) is likely to come up quickly in conversation. Peer reviewers might identify AI tools as something they've started using for extra review help, or they’re thinking about whether to do this and how to do it ethically. Editors will say they’re seeing more and more formulaic-appearing peer reviews that look suspiciously like they are AI-generated. Authors also are noticing this in peer reviews too. 

Generative artificial intelligence (AI) tools can genuinely help researchers organize, conduct, and write up their research work faster than ever before. However, a lack of transparency on how these tools were applied and their outputs verified can influence scientific work and its trustworthiness. 

Why does anyone agree to review a manuscript at all? It's unpaid, mostly invisible work, on top of a reviewer's own research and clinical or academic responsibilities. Yet the entire scholarly publishing system depends on people saying yes. I work on peer review operations and editorial policy at JMIR Publications, and this is the question I keep coming back to.

Peer review is the heartbeat of scholarly publishing. It requires countless unseen hours of critical reading, deep intellectual effort, and constructive feedback to ensure published research is rigorous, accurate, and trustworthy. Yet, despite being essential to scientific progress, the dedicated effort behind peer review often goes unacknowledged.

Sustaining peer review capacity amid rising submission volumes requires balancing three core elements: speed, quality, and expanding the reviewer workforce. By treating reviewer feedback as a collaborative editing process rather than a barrier, incorporating vetted early-career researchers into the reviewer pool, and utilizing transparent review models, the scientific community can drastically reduce publication bottlenecks without compromising integrity.