Journal of Medical Internet Research

The global burden of obesity continues to rise, highlighting the need for patient-centered approaches to weight management. Shared decision-making is particularly important in the selection of antiobesity medications (AOMs), as treatment options differ in mechanism, effectiveness, side effects, routes of administration, and cost. Despite this preference-sensitive context, only a few patient decision aids (PDAs) have been culturally and clinically adapted for use in Asian populations.

Serious mental illness (SMI) is difficult to treat for various reasons, such as rapid changes in symptoms, comorbid health conditions, long gaps between provider visits, and additional societal barriers experienced by this population. Wearable mobile-sensing devices can be used to passively collect valuable patient-generated health data, such as daily step count, heart rate variability, sleep information, and other health-related behaviors, which could inform and improve treatment for individuals with SMI. Wearable health devices have become more economically accessible, providing promise for the possibility of their implementation in health care. However, more information regarding how individuals with SMI perceive and interact with these devices is needed.

Large language models (LLMs), such as ChatGPT (OpenAI), are rapidly evolving, and their applications in health care are increasing. There is a growing demand for automation of routine tasks and a drive to use LLMs or similar to support research.

Telemedicine use expanded rapidly during the COVID-19 pandemic. The Hong Kong Hospital Authority (HA) launched both tele-designated clinics (Tele-DCs) and face-to-face physical designated clinics (PDCs) to manage mild cases. However, the comparative effectiveness of these models remains unclear.

Extracting medical knowledge for secondary purposes, such as diagnostic support, continues to pose a substantial challenge. Conventional named entity recognition has focused on short terms (eg, genes, diseases, and chemicals), whereas extraction and assessment of longer, complex expressions remain underexplored. Clinically vital concepts, such as diseases, pathologies, symptoms, and findings, often appear as long phrases, and accurate extraction is crucial for applications such as constructing causal knowledge from case reports. Consequently, a framework addressing both short terms and clinically meaningful long phrases—termed extended Clinical Concept Recognition (E-CCR)—is essential.

Machine learning (ML) and deep learning (DL) show promise for fall risk prediction, but prior reviews focused mainly on real-time fall detection, in-hospital falls, or conventional statistical models. The performance of ML-DL–based models for predicting future falls in community-dwelling older adults remains unclear.

Previous research has demonstrated that the use of continuous glucose monitoring (CGM) can improve glycemic control in people with type 2 diabetes when used regularly alongside a digital diabetes self-management education and support (DSMES) program. However, to date, there is limited evidence showing the benefits of a digitally delivered DSMES program combined with real-time CGM for adults with type 2 diabetes.

Patient experience surveys are essential tools for assessing health care quality, yet the potential influence of survey mode on patient experience scores remains understudied. This study investigates the mode effects between mobile web and telephone surveys within South Korea’s Patient Experience Assessment.

A growing segment of the population requires ongoing care and support for managing their chronic diseases. Digital platforms for self-management are rapidly emerging to meet this need, but patients’ experiences with these platforms vary significantly. This may be due to the complexity and flexibility of digital platforms, where the wide array of available features can generate unexpected impacts.

Patients’ access to their electronic health record (EHR) supports their participation and satisfaction with care. Despite the benefits, some patients have been upset after reading their EHR. Additionally, health care professionals are concerned that patients, particularly those with mental health conditions, may be offended, and they have expressed a need for further guidelines on how to write EHRs. Experiences among various patient groups are essential to support the relationship between patients and professionals. However, prior studies have often focused on single patient groups or specific clinical contexts, leaving a limited understanding of differences across multiple patient groups.

Digital health literacy (DHL) has the potential to improve health among older adults by enhancing access to health-related information and health care services.

Artificial intelligence (AI)–enabled systems must simultaneously improve the Quintuple Aim and digital health maturity, including equitable access to and quality and interoperability of data, tools, agents, and services. This requires a comprehensive sociotechnical and global approach to cocreation, management, and governance for individuals and organizations in the ecosystem.