Care Coordination Between Family Physicians and Palliative Care Physicians for Patients With Cancer: Results of a Quality Improvement Initiative
Bibliographic record
Abstract
PURPOSE: At our institution's cancer palliative care (PC) clinic, new referrals from oncologists were scheduled for consultation and ongoing follow-up by PC physicians without input from the patients' family physicians (FPs). FPs reported that they felt out of the loop. We implemented a quality improvement (QI) initiative aimed at systematically facilitating care coordination between FPs and PC physicians. METHODS: A coordination toolkit was sent from the PC physician to the FP whenever the PC physician received a consultation request from an oncologist. The toolkit included an introduction to the PC physician team; an opportunity for the FP to choose how best to collaborate with PC physicians to meet the patient's PC needs; and contact information for access to 24/7 PC physician support. Responses from FPs regarding their preferred level of engagement with PC determined further care planning in the clinic. We measured feasibility, response rate, and qualitative surveys of FPs about the usefulness of the intervention. RESULTS: Two hundred fourteen new consultations were eligible for a standardized letter over the 6-month implementation period. Feasibility for sending the toolkit was 90.0% and response rate for collaborative care preference from FPs was 86.0%, with median response time of 3-4 days. 78.9% of FPs indicated they would prefer ongoing consultative care by the PC physician, while 18.6% indicated that PC physician consultation was not needed, or that the FP would provide primary PC after a one-time PC physician consultation. CONCLUSION: We successfully implemented a QI initiative to improve care coordination between FPs and PC physicians for patients with cancer. The coordination toolkit can protect the patient-FP primary PC relationship and optimize specialist PC resource utilization for complex patients.
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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 | 0.027 | 0.039 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".