JMIR Cancer

Cancer survivorship is a complicated chronic and long-lasting experience, causing uncertainty and a wide range of physical and emotional health concerns. Due to the complexity of cancer, patients often seek out multiple different sources of health information to better understand aspects of their cancer diagnosis. The high variability among cancer patients presents significant challenges in treatment, prognosis, and overall disease management. Artificial intelligence (AI) chatbots can further personalize cancer care delivery. However, there is a knowledge gap around cancer survivors’ facilitators and barriers to adopting and using AI-chatbots.

Cutting-edge oncology care often depends on patients’ ability to use rapidly evolving health technology. Digital health literacy (DHL, the capacity to understand health-related information with electronic media) is an emerging, yet underexplored social determinant of health in patients with cancer.

Long-term breast cancer survivors often continue to experience physical and psychological sequelae, despite being cancer-free; these challenges can negatively impact their quality of life and self-efficacy. Mobile health interventions (mHealth) constitute a promising strategy for providing personalized support. However, the feasibility and acceptability of these tools in long-term breast cancer survivors has not yet been sufficiently explored.

The growing importance of real-world data (RWD) as a source of evidence for drug effects has led to increased interest in clinical research utilizing secondary use data from electronic medical record systems. Although immune checkpoint inhibitors and targeted therapies have advanced lung cancer treatment, managing complications such as interstitial lung disease (ILD) remains challenging. Early detection and prevention of ILD are crucial for improving patient prognosis and quality of life; however, predictive biomarkers have yet to be established. Therefore, methods to identify ILD risk factors and enable early detection using RWD are needed.

Cancer-related cognitive impairment (CRCI) is frequently reported during cancer treatment, with 35% of patients experiencing cognitive issues even after treatment completion. Commonly reported impairments include difficulties with memory, attention, executive function, and processing speed, which often reduce daily functioning and quality of life (QoL). Despite its prevalence, CRCI remains underresearched across various cancer types, limiting understanding of the patient experience.

Although its receipt has declined since the pandemic, telehealth remains a popular care delivery mode across the cancer care continuum. A greater understanding of telehealth in the cancer care context is vital, especially because the benefits and barriers of telehealth, as with other digital health tools, may vary across different population groups and geographic contexts.

Outpatient treatments in oncology are increasing, including targeted therapies. The implementation of remote patient monitoring (RPM) between therapy sessions has the potential to enhance patient care and therapy outcomes in the future.

Lung cancer (LC) is the leading cause of cancer-related deaths worldwide and has a substantial impact on patients’ quality of life (QoL) and psychological well-being, due to complex physical, emotional, and social challenges. Addressing these needs is critical; yet, many patients go unsupported. eHealth (using information and communication technology to deliver health-related services) offers a scalable way to provide timely, personalized care for people living with LC.

While numerous models have been developed to predict overall survival in postoperative patients with esophageal squamous cell carcinoma (ESCC), few have specifically focused on predicting postoperative recurrence.

Over the past decade, Fast Healthcare Interoperability Resources (FHIR) have become increasingly relevant in healthcare data standardization. However, the complex structure of FHIR makes cohort analytics with many-to-many relations extremely time-consuming, and, in many cases, impossible. To support exploratory cohort building and data visualization in oncology, especially for non-technical users, we developed the DermaDashboard, an interactive dashboard built on top of a relational FHIR-compliant PostgreSQL database. Relevant oncology data was pre-aggregated with a materialized view, and the subsequent visualization layer was implemented using an open-source visualization tool, enabling clinicians to filter and analyze data without requiring familiarity with FHIR or SQL. The database encompassed data from 3,949 melanoma patients and included 82,783 health records. Core FHIR resources were Patient, DiagnosticReport, and QuestionnaireResponse, with 54 mapped attributes spanning demographics, stagings, mutations, and treatments. The resulting dashboard allowed filtering across 29 variables to construct subcohorts and generate aggregation analyses. This implementation shows how open interoperability data standards, such as FHIR, can be used in the development of modular, user-friendly clinical dashboards for cohort analysis, and the architecture demonstrates a feasible path toward democratizing access to structured healthcare data.

Survivors of metastatic lung cancer (MLC) face a heightened risk of developing second primary cancers (SPCs), which significantly impact long-term outcomes. Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, but their potential role in reducing SPC risk remains underexplored. This study investigates the association between ICI treatment and the incidence of SPCs in a large, real-world cohort of MLC patients.