Increasing Referrals of Patients With Gastrointestinal Cancer to a Cancer Rehabilitation Program: A Quality Improvement Initiative
Bibliographic record
Abstract
BACKGROUND: People with cancer are at risk for initial, late, and long-term effects of cancer and its treatments. Cancer rehabilitation (CR) focuses on prevention/treatment of these sequelae and optimization of physical, social, and vocational functioning. Our center has a multidisciplinary impairment-driven outpatient CR program, but referrals of patients with GI cancer were low. AIMS: We aimed (for 2019, relative to 2018) (1) to increase CR referrals of patients with GI cancer by 50% and (2) to increase the proportion of referrals coming from oncologists. Balancing measures included inappropriate referrals and cancellations. METHODS: A rapid cycle improvement approach was used to optimize GI referrals to the CR program. Barriers to CR referral were identified through a literature review and informal interviews of GI clinicians. Barriers included (a) knowledge of CR program existence, (b) awareness of the referral process, (c) time, and (d) lack of CR program exposure. The team used Plan-Do-Study-Act (PDSA) cycles every 2 months from January to December 2019 to address barriers. A p-chart was used to analyze the results. RESULTS: PDSA cycles included CR program advertisement, a presentation to GI staff, nurse-led patient identification, patient-facing posters, and clinician thank-you emails. The p-chart showed a 100% relative increase in referral numbers and an improvement in the percentage of patients referred by oncologists from 51% to 75%. There was no significant change in inappropriate referrals or cancellations. CONCLUSION: Through PDSA cycles, we improved the total number of patients with GI cancer and percentage referred by an oncologist to a CR program. Future work will assess sustainability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".