JMIR Cancer

Systematic symptom management is a crucial component in patient-centred cancer care. Despite advancements in symptom management and the development of numerous electronic patient-reported outcome tools (ePROMs), integrating these tools into clinical practice remains challenging. Engaging key stakeholders, including patients, in the development of ePROM tools is pivotal to fostering the adaption of such tools. As part of an innovation and implementation study aimed at enhancing efficiency and patient-centred care through the development of digital patient-centred care pathways, we explored cancer patients’ perspectives on current clinical practice regarding symptom management and patient-centred care, as well as their needs and preferences related to ePROMs.

Electronic prospective surveillance models (ePSMs) have the potential to improve the management of cancer-related impairments by systematically screening patients using electronic patient-reported outcomes (ePROs) during and after treatment, and linking them to tailored self-management resources and rehabilitation programs. However, their successful implementation into routine care requires careful consideration of patient and provider needs and must align with clinical workflows, which may vary across settings and require adaptation to the local context. The aim of this paper is to describe the development of REACH, a web-based ePSM designed to remotely screen for physical cancer-related impairments and direct patients to rehabilitation resources based on need. The development of REACH followed an integrated knowledge translation (iKT) approach, engaging key knowledge users including patients, clinicians, administrators, and information technology specialists. The development process involved collaboration across five working groups. The system content and logic group selected the impairments to be screened, measures used, frequency of screening, and resources recommended based on results of a survey with oncology providers and researchers, patient feedback, a literature review, and an environmental scan. The machine learning group explored predictive modeling approaches to optimize the assessment frequency using retrospective patient data. The implementation group identified features from existing systems that could be built to promote assessment completion and integration into clinical workflows through a scoping review, interviews with clinic staff, and focus groups with patients. The design group conducted co-design workshops and usability testing with patients to iteratively refine the interface and develop a prototype. Lastly, the software development group converted the prototype to a web-based application and conducted privacy and security assessments and quality assurance. The integration of key knowledge users through an iKT approach played a critical role in determining the design and functionality of REACH. REACH allows patients to remotely complete assessments tailored to their cancer type and treatment status on any electronic device. The system generates automated advice based on the assessment responses including links to educational resources for self-management, suggestions for community programs to register for, and recommendations to contact their oncology team for further assessment and possible referral to rehabilitation services. These recommended resources are stored in the patient’s personalized library, organized by type and severity of cancer-related impairments reported, and is updated following each new ePRO assessment completed. Additional key system features include a patient-driven and structured process for managing high impairment scores, usability enhancements to improve navigation, and safeguards to ensure data security. The development of REACH demonstrates how an iKT approach can be used to design an ePSM that is user-friendly, clinically relevant, and aligned with implementation considerations. The system has been implemented at four Canadian cancer centres, and its implementation is being evaluated to inform future refinements.

Clinical trials are important for all stages of the cancer control continuum, including cancer survivorship.

About 40% of cancers are preventable through evidence-based interventions; however, uptake remains suboptimal. Knowledge and acceptance of primary and secondary preventive measures in the general population is not sufficient. We hypothesized that a web-based tool providing comprehensive, easily accessible, and individualized information on preventive strategies for multiple tumor entities could support informed decisions.

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.