Using a statewide collaborative approach to improve primary palliative care performance.
Bibliographic record
Abstract
53 Background: The Michigan Oncology Quality Consortium (MOQC) is a statewide collaborative of oncology practices. Using the Quality Oncology Practice Initiative (QOPI) measurement tool, MOQC identified a gap in the provision of palliative care. We designed and tested interventions to enhance the capacity and capabilities of the oncologist to deliver primary palliative care earlier in a patient’s course. Methods: MOQC created a process to assist oncology care teams in providing primary palliative care services using the Edmonton Symptom Assessment Scale tool. 11 practices participated in two pilots over 18 months. During and after these pilots, we disseminated tools for improvement, including customized palliative care dashboards, to the entire consortium. Pilot teams also shared their successes, insights, and best practices during semiannual live consortium meetings. Results: Shown are palliative care-focused QOPI results, comparing baseline (Fall 2011, F11) and post project (Spring 2013, S13) for all MOQC practices compared with all participating QOPI practices, using a paired t-test. MOQC sites outperformed the QOPI national average on multiple palliative care measures. Furthermore, the MOQC improvement rate since the project initiation was greater than that of national. Although clinically important, the measures did not reach standard statistical significance. Conclusions: Running successive pilot projects improved primary palliative care performance of the teams involved; additionally, this momentum and gain in knowledge facilitated dissemination of innovation and measurable improvement in all members of a statewide consortium. [Table: see text]
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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 | 0.024 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".