A Multidisciplinary Approach to Implement Personalized Breast Cancer Treatment and Care Plans
Bibliographic record
Abstract
Implementation of survivorship care plans remain a challenge. This quality improvement initiative aims to integrate personalized treatment plans (PTP) and care plans (PCP) into the existing workflow for breast cancer (BC) patients. Methods: Phase 1 was to identify multidisciplinary team members to generate and deliver PTP and PCP. Concurrently, Phase 2 was to deliver PTP and PCP to newly diagnosed invasive BC patients at chemotherapy initiation and completion, respectively. Iterative plan, do, study, act (PDSA) cycles were applied to refine the process. The proportion of information completed for PTP and PCP generation and its delivery by the care team were measured. Patient and provider satisfaction were also assessed. Implementation Process and Results: The care transfer facilitator (CTF) was identified to complete and deliver PTP, and their data entry increased from 0% to 76%, 80%, 92% consecutively during the last 4 PDSA cycles. PTP and PCP were provided to 85% of eligible BC patients. Patients agreed that PTP helped them to actively participate in their care (88%) and communicate with the oncology care team (86%). Primary care physicians agreed that PTP and PCP had the information needed to “stay in the loop” (80%), and oncologists agreed they should be incorporated into oncology clinics (100%). Conclusions: Integrating PTP and PCP generation and delivery into existing workflow has led to an increase in uptake, sustainability and provider buy-in. With limited resources, it remains difficult to find care team members to complete the forms. A dedicated personnel or survivorship clinic is required to successfully implement PTP and PCP as the standard of care.
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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 | 0.038 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".