Supporting Breast Cancer Patients and Providers Through Treatment and Survivorship: Implementation and Usability Evaluation of the MyJourney Platform (Preprint)
Bibliographic record
Abstract
Abstract Background Breast cancer is the most common cancer among Canadian women, bringing complex demands for timely decision-making, coordination of multidisciplinary care, and efficient communication between patients and providers. The increasing reliance on fragmented and noninteroperable health information systems exacerbates workflow and documentation burdens, leading to inefficiencies and gaps in continuity of care. While nurse navigation programs partially bridge these gaps, most digital platforms remain poorly integrated into provider workflows, requiring manual tracking, which results in duplicated effort and reduced efficiency. Our team developed “MyJourney” at North York General Hospital (NYGH) in Ontario. It is a digital navigation platform that supports breast cancer care throughout the entire continuum, from diagnosis to survivorship. Objective This study aimed (1) to map the breast cancer journey and workflow to inform the design and adaptation of MyJourney at 2 oncology settings at NYGH; (2) to identify barriers and guide local implementation; and (3) to evaluate the implementation, usability, and perceived utility of MyJourney’s Clinical Navigation Tool for breast cancer care teams. Methods A multimethod, 3-phase study was conducted at NYGH’s Breast Diagnostic Centre (BDC) and Chemotherapy Clinic (CC) in Toronto, Canada. Phase 1 involved qualitative interviews with patients with breast cancer to map their care journey and inform user-centered platform design. Phase 2 included preimplementation interviews with nurses, pharmacists, and administrative staff to map workflows and customize MyJourney for the CC. Phase 3 evaluated MyJourney’s implementation and usability over 6 weeks using the System Usability Scale, technology acceptance model surveys, and follow-up interviews with platform users. Data were analyzed via interpretive description and descriptive statistics. Ethics approval was obtained. Results In total, 13 patient interviews revealed distinct challenges and communication needs across prediagnosis, diagnosis, treatment, and survivorship phases, emphasizing the need for personalized, integrated resources (phase 1). A total of 8 participants (3 nurses and 5 pharmacists), providers, and clinic staff highlighted pain points in fragmented information systems, inefficient manual processes, and limited team coordination (phase 2). MyJourney’s phased implementation led to high user acceptance, with mean System Usability Scale scores rated “excellent” (BDC: 81.3; CC: 86.3) at the 6-week follow-up (phase 3). The 4 participants (1 charge nurse, 1 administrative staff at the BDC, and 2 charge nurses at the CC) described the platform as intuitive, efficient, and well-organized, citing consolidated patient records, streamlined appointment management, and improved workflow as major benefits. Recommendations included improved interoperability, enhanced notifications, role-specific customization, and integration with electronic medical records for broader scalability. Conclusions The iterative, interest-holder engaged design and phased implementation of MyJourney facilitated rapid uptake and high usability among breast cancer provider teams. The Clinical Navigation Tool component of the MyJourney platform reduced documentation burden, improved workflow efficiency, and facilitated care coordination across oncology settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".