Utilization outcomes of a cancer rehabilitation (CRNav) program: getting to the quadruple aim in cancer care
Bibliographic record
Abstract
BACKGROUND: A cancer rehabilitation navigation (CRNav) program is an evidence-based care delivery model that uses a rehabilitation professional in the navigation role to support oncology care delivery, provide functional screening for early identification of impairment, and coordinate care delivery services to optimize early rehabilitation. There is limited research showing how a CRNav impacts healthcare utilization. The objective of this study was to assess utilization data for a CRNav Program and demonstrate how the program influences the effectiveness of cancer care delivery and patient and provider satisfaction. METHODS: Data was collected from the electronic health record of the Brooks Rehabilitation/Halifax systems at a community cancer center to assess program and service utilization over 3.2 years using a retrospective design. RESULTS: Over 3.2 years, the CRNav program received 1585 referrals and screened 1447 (91.3%) patients. Of the 1447 screenings performed, 73.6% were recommended to receive outpatient rehabilitation (n = 1065). Among patients screened, breast cancer was the most common cancer diagnosis (47%) followed by head and neck cancers (14%). There were 638 total rehabilitation visits identified for patients who were seen for services within the health system, with physical therapy encounters accounting for the greatest number (n = 462). The most common reasons for receiving physical therapy services included lymphedema (27%), pain (25%), and limited range of motion (12%). Patients reported high satisfaction (≥ 95.4%) in the areas of how well rehabilitation met expectations and overall satisfaction with the rehabilitation experience. CONCLUSIONS: Using a CRNav in a community cancer center resulted in efficient care of patients with cancer, improved patient satisfaction and patient outcomes, and an enhanced clinician experience. This program provides a value-based approach to care supporting the quadruple aim and improving the identification and management of cancer-related functional morbidity.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".