Background: Palliative care improves patients’ health-related quality of life (HRQoL) by delivering specialty-level management for #Cancer symptoms. Digital symptom monitoring programs (SMPs) improve patients’ HRQoL through early symptom identification during #Cancer care. Through complementary approaches, SMPs and palliative care align with symptom management theory by linking the patient-reported symptom experience with clinician recommendations for symptom management to improve health outcomes for patients with serious illness. While both SMPs and palliative care reduce health care use and improve survival through detection and treatment of #Cancer symptoms, few studies have attempted to integrate palliative care into SMPs for #Cancer symptom management. Objective: This study aimed to assess patients’ perspectives about the #feasibility and acceptability of an SMP with a prompted palliative care self-referral option (Symptom Monitoring Program Linked to Electronic Referrals [SyMPLER]) via a mobile health (mHealth) app during lung #Cancer treatment. Methods: This is a single-institution qualitative study of patients newly diagnosed with lung #Cancer who were enrolled in a single-arm #feasibility pilot of an mHealth app that facilitates on-demand reporting of #Cancer-related symptoms and prompts patients to self-refer to palliative care for symptom management. Interview participants were purposively sampled based on high (≥3 uses) vs low (0-2 uses) engagement with the app during the first 3 months of access to the SMP. Questions focused on program #feasibility and acceptability as well as perceptions of the 5 components of the SMP intervention (introduction to palliative care, reminders, symptom reporting, palliative care self-referral, and oncology team callback). Interviews were conducted from May 2024 to May 2025 by telephone, recorded, transcribed, and analyzed using thematic analysis. Results: A total of 31 patients were interviewed; 21 had high and 10 had low app engagement. Participants were mostly White (n=29, 93.5%), female (n=21, 67.7%), and aged 60 years or older (n=19, 61.3%). In high and low engagement groups, participants voiced willingness and ability to report symptoms through the app and self-refer to palliative care if needed for symptom management. #feasibility barriers differed by group, with high-engagement participants noting forgetting to use the app for symptom reporting, while low-engagement participants cited low symptom burden, technology concerns, and time constraints as reasons for limited use of the SMP. Participants described uncertainty about when or why to request palliative care as a major barrier to self-referral. Conclusions: Participants’ perspectives on the #feasibility and acceptability of SyMPLER differed by their level of engagement with the SMP app. Participants in both groups voiced acceptance of the option to self-refer to palliative care, although #feasibility was limited by low uptake of palliative care self-referral. Participants suggested improvements to educational outreach, digital accessibility, customizable reminders, and recommendations for help-seeking behaviors based on symptom scoring thresholds to improve patient engagement with iterations of this SMP intervention. Trial Registration:

THIS IS TO ACCESS RECORDINGS TO THE COURSE  Welcome to the advanced course 2026. We are delighted to deliver the course once again and hope that you find the recordings helpful to your practice. The a...

THIS IS TO ACCESS RECORDINGS TO THE COURSE  Welcome to the advanced course 2026. We are delighted to deliver the course once again and hope that you find the recordings helpful to your practice. The a...

YouTube video by Palliative Care Explained

Background: Patients undergoing cancer treatment experience significant symptom burden. The standard process of symptom management includes patient reporting and clinical response following symptom escalation. Emerging predictive symptom models utilize AI components of machine learning and deep learning to identify the risk of symptom deterioration, facilitating earlier intervention to prevent downstream effects. However, integrating predictive symptom models into clinical practice will require oncology #nurses to adopt innovative approaches. Objective: The object of our evaluation was to explore oncology #nurses' perceptions of the use of predictive symptom models in cancer care and the factors influencing the adoption of this symptom care innovation. Methods: The evaluation was guided by Rogers’ Diffusion of Innovation Theory, which describes the process of how individuals adopt new technologies. The investigators developed an interview guide that asked oncology #nurses to rate their perceptions of AI symptom models on a Likert scale. Participants were also asked to provide qualitative comments to support their ratings for each question, in order to better understand the key factors that would influence AI predictive model adoption. Investigators analyzed demographic data and Likert ratings with descriptive statistics. Qualitative analysis of participant comments included content analysis and inductive coding to identify themes. #nurses’ perception of factors that would influence the adoption of AI symptom models, based on Rogers’ theory, included relative advantage, compatibility, complexity, trialability, and observability. Results: Responses of 15 oncology #nurses with greater than one year of experience in oncology were analyzed. There was high agreement among #nurse participants that an AI model could improve symptom management for oncology patients (n = 10, 67%) and increase early intervention to prevent the escalation of symptoms (n = 12, 86%). All participants (n= 15, 100%) agreed that receiving symptom information would be helpful. Nearly three-quarters of participants (n = 11, 73%) endorsed that the information would save time. Most (n = 12, 80%) recommended that clinicians receive information about predicted symptom deterioration of their patients. Among open-ended responses, key themes were consistent with factors identified in the Diffusion of Innovation theory including: 1) perceptions related to the AI model (compatibility/ complexity), 2) #nurses’ perception of patients benefit, (observability) 3) improved clinical processes, (relative advantage/ observability) 4) apprehension over model accuracy and impact, (compatibility/ trialability/observability) and 5) implementation/ adoption (trialability/ complexity/observability). Conclusions: Oncology #nurses agree that predictive symptom models could help improve symptom management for patients undergoing cancer treatment. However, #nurses noted that transparency in the factors included in the AI model was essential, that #nurses should be involved in the development and testing of models, and that the observability of the benefit in symptom care would need to be evident for ultimate adoption.