Lessons Learned from the Implementation of a Person-Centred Digital Health Platform in Cancer Care
Bibliographic record
Abstract
The SARS-CoV-2 (COVID-19) pandemic has accelerated the development and use of digital health platforms to support individuals with health-related challenges. This is even more frequent in the field of cancer care as the global burden of the disease continues to increase every year. However, optimal implementation of these platforms into the clinical setting requires careful planning and collaboration. An implementation project was launched between the Centre intégré universitaire de santé et de services sociaux (CIUSSS) du Centre-Ouest-de-I'Île-de-Montreal and BELONG-Beating Cancer Together-a person-centred cancer navigation and support digital health platform. The goal of the project was to implement content and features specific to the CIUSSS, to be made available exclusively for individuals with cancer (and their caregivers) treated at the institution. Guided by Structural Model of Interprofessional Collaboration, we report on implementation processes involving diverse stakeholders including clinicians, hospital administrators, researchers and local community/patient representatives. Lessons learned include earlier identification of shared goals and clear expectations, more consistent reliance on virtual means to communicate among all involved, and patient/caregiver involvement in each step to ensure informed and shared decision making.
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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 | 0.057 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".