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Record W4416294240 · doi:10.2196/87973

Supporting Breast Cancer Patients and Providers Through Treatment and Survivorship: Implementation and Usability Evaluation of the MyJourney Platform (Preprint)

2025· article· en· W4416294240 on OpenAlexvenueaboutno aff
Monika Kastner, Isabella Herrington, Julie Makarski, Krystle Amog, Leigh Hayden, Norna Abbo, Nicole Jedrzejko, Fahima Osman

Bibliographic record

VenueJMIR Cancer · 2025
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsnot available
Fundersnot available
KeywordsUsabilityWorkflowBreast cancerDocumentationHealth careMultidisciplinary approachQualitative research

Abstract

fetched live from OpenAlex

<sec> <title>BACKGROUND</title> 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 non-interoperable 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 in Ontario. It is a digital navigation platform that supports breast cancer care throughout the entire continuum, from diagnosis to survivorship. </sec> <sec> <title>OBJECTIVE</title> This study aimed to (1) map the breast cancer journey and workflow to inform the design and adaptation of MyJourney at two oncology settings at NYGH; (2) identify barriers and guide local implementation; and (3) evaluate the implementation, usability, and perceived utility of MyJourney's Clinical Navigation Tool for breast cancer care teams. </sec> <sec> <title>METHODS</title> A multi-method, three-phase study was conducted at NYGH’s Breast Diagnostic Centre (BDC) and Chemotherapy Clinic in Toronto, Canada. Phase 1 involved qualitative interviews with breast cancer patients to map their care journey and inform user-centered platform design. Phase 2 included pre-implementation interviews with nurses, pharmacists, and administrative staff to map workflows and customize MyJourney for the chemotherapy clinic. Phase 3 evaluated MyJourney’s implementation and usability over six 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. </sec> <sec> <title>RESULTS</title> 13 patient interviews revealed distinct challenges and communication needs across pre-diagnosis, diagnosis, treatment, and survivorship phases, emphasizing the need for personalized, integrated resources. Eight providers and clinic staff highlighted pain points in fragmented information systems, inefficient manual processes, and limited team coordination. MyJourney’s phased implementation led to high user acceptance, with mean System Usability Scale scores rated “excellent” (BDC: 81.3; Chemotherapy Clinic: 86.3 at six-week follow-up). Users 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. </sec> <sec> <title>CONCLUSIONS</title> The iterative, stakeholder-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. </sec>

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.082
GPT teacher head0.524
Teacher spread0.442 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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