Journal of Medical Internet Research

Atrial fibrillation (AF) is a leading chronic cardiac disease associated with an increased risk of stroke, cardiac complications, and general mortality. Mobile health (mHealth) interventions, including wearable devices and apps, can aid in the detection, screening, and management of AF to improve patient outcomes. The inclusion of approaches that consider user experiences and behavior in the design of health care interventions can increase the usability of mHealth interventions, and hence, hopefully, yield an increase in positive outcomes in the lives of users.

Implementing Patient Safety Incident Reporting and Learning (PSIRL) guidelines is critical in guiding clinical practice and improving clinical outcomes in specialized care units (SCUs). There is limited research on the evidence of the implemented PSIRL guidelines in SCUs at the global level.

Hospital pharmacy plays an important role in ensuring medical care quality and safety, especially in the area of drug information retrieval, therapy guidance, and drug-drug interaction management. ChatGPT is a powerful artificial intelligence language model that can generate natural-language texts. Here, we explored the applications and reflections of ChatGPT in hospital pharmacy, where it may enhance the quality and efficiency of pharmaceutical care. We also explored ChatGPT’s prospects in hospital pharmacy and discussed its working principle, diverse applications, and practical cases in daily operations and scientific research. Meanwhile, the challenges and limitations of ChatGPT, such as data privacy, ethical issues, bias and discrimination, and human oversight, are discussed. ChatGPT is a promising tool for hospital pharmacy, but it requires careful evaluation and validation before it can be integrated into clinical practice. Some suggestions for future research and development of ChatGPT in hospital pharmacy are provided.

Machine learning offers quantitative pattern recognition analysis of wearable device data and has the potential to detect illness onset and monitor influenza-like illness (ILI) in patients who are infected.

Medical texts present significant domain-specific challenges, and manually curating these texts is a time-consuming and labor-intensive process. To address this, natural language processing (NLP) algorithms have been developed to automate text processing. In the biomedical field, various toolkits for text processing exist, which have greatly improved the efficiency of handling unstructured text. However, these existing toolkits tend to emphasize different perspectives, and none of them offer generation capabilities, leaving a significant gap in the current offerings.

The COVID-19 pandemic accelerated telehealth adoption across disease cohorts of patients. For many patients, routine medical care was no longer an option, and others chose not to visit medical offices in order to minimize COVID-19 exposure. In this study, we take a comprehensive multidisease approach in studying the impact of the COVID-19 pandemic on health care usage and the adoption of telemedicine through the first 12 months of the COVID-19 pandemic.

The challenge of preventing in-patient falls remains one of the most critical concerns in health care.

Several machine learning (ML) prediction models for neurodegenerative diseases (NDs) in type 2 diabetes mellitus (T2DM) have recently been developed. However, the predictive power of these models is limited by the lack of multiple risk factors.

The first-line treatment for polycystic ovary syndrome (PCOS) is lifestyle modification. However, it is currently unknown whether digital medicine can assist patients with PCOS in maintaining a healthy lifestyle while alleviating PCOS symptoms.

A clinical dashboard is a data-driven clinical decision support tool visualizing multiple key performance indicators in a single report while minimizing time and effort for data gathering. Studies have shown that including patient-reported outcome measures (PROMs) in clinical dashboards supports the clinician’s understanding of how treatments impact patients’ health status, helps identify changes in health-related quality of life at an early stage, and strengthens patient-physician communication.

Peer-supported mobile health (mHealth) programs hold the promise of providing a low-burden approach to increasing access to care and improving mental health. While peer support has been shown to improve engagement in care, there is limited investigation into the impact of peers on symptom outcomes. Trauma-exposed populations frequently endure co-occurring posttraumatic stress and depressive symptoms as well as difficulties in day-to-day functioning. This study evaluated the potential benefits of a peer-supported, transdiagnostic mHealth program on symptom outcomes and functioning.

Digital interventions to improve retention in HIV care are critical to ensure viral suppression and prevent further transmission. AIDS Healthcare Foundation Healthcare Centers are centers across the United States that provide primary HIV care. Traditionally, the Healthcare Centers conduct phone calls with patients to schedule and confirm appointments, as well as share laboratory results. In 2017, Healthvana piloted a digital platform at AIDS Healthcare Foundation Healthcare Centers to send patients SMS text message appointment reminders and allow patients to review their upcoming appointment and view their laboratory results in the web-based patient portal.