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Record W4409057567 · doi:10.2196/57388

Perceptions and Needs of Stakeholders Regarding MyPal Project’s Electronic Patient-Reported Outcome App: Cross-Sectional Qualitative Focus Group Study

2025· article· en· W4409057567 on OpenAlexvenueno aff
Dimitrios Kyrou, Panos Bonotis, Christine Kakalou, Maria Vasilopoulou, Marcel Meyerheim, Annette Sander, Eleni Kazantzaki, Christina Karamanidou

Bibliographic record

VenueJMIR Cancer · 2025
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintFocus (optics)PerceptionFocus groupBusinessPsychologyMarketingComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: Palliative care is crucial for patients with life-threatening and serious diseases such as cancer, as it addresses their physical, psychosocial, and spiritual needs. Hematological malignancies significantly contribute to global cancer cases, impacting both older adults and children. To meet the increasing demand for palliative care, electronic patient-reported outcome (ePRO) interventions offer valuable insights into patient monitoring and treatment decision-making. The MyPal project developed a digital ePRO solution to improve palliative care by enabling structured symptom reporting and promoting physician-patient communication. OBJECTIVE: This study aims to explore the perceptions, opinions, and needs of adult and pediatric patients with cancer, caregivers, and health care professionals (HCPs) regarding low-fidelity versions of the MyPal project's digital solution, which is designed to improve palliative cancer care. METHODS: A qualitative, cross-sectional study was conducted using 12 prepilot focus groups (FGs) across 4 European countries (Greece, Italy, Germany, and the Czech Republic) at participating hospitals and research centers. The FGs, held in person, included 61 participants, including 27 (44%) adult patients with chronic lymphocytic leukemia or myelodysplastic syndromes, 19 (31%) children with hematological malignancies or solid tumors and their parents, and 15 (25%) HCPs specializing in oncology and palliative care. A semistructured discussion guide, informed by vignettes and user personas, was used to facilitate discussions. Sessions were audio recorded, transcribed, and analyzed using thematic analysis to identify and extract themes and subthemes from the FG discussions. RESULTS: Three main themes emerged from the FG discussions. The first theme, improved care, showcased the project's potential to enhance health care through patient-reported measures by improving symptom monitoring, streamlining decision-making, and strengthening physician-patient communication. Patients and caregivers valued the ability to report symptoms remotely, reducing unnecessary hospital visits, while HCPs appreciated having structured patient data to guide treatment. The second theme, digital communication framework, revealed that while participants recognized the benefits of digital tools, they had concerns about data security, privacy, and clarity regarding communication protocols. Questions emerged about how and when HCPs would review and respond to patient-reported data. In the third theme, applicability for use in health care, participants emphasized the importance of the system's ease of use, particularly for older patients and young children. Concerns were raised about the potential intrusiveness of the system, particularly regarding notification frequency and the impact on daily life. HCPs highlighted workload challenges, suggesting the need for a structured alert system to prioritize urgent cases. CONCLUSIONS: Our findings indicate that ePRO-based interventions such as MyPal can improve palliative care by facilitating communication and patient monitoring. However, addressing privacy concerns, optimizing usability for diverse populations, and ensuring seamless integration into clinical workflows are critical for successful adoption. Insights from this study will inform future development and optimization of eHealth interventions in palliative care.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.108
GPT teacher head0.438
Teacher spread0.330 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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