Effect of intervention on quality measures of symptom management in the Michigan Oncology Quality Consortium (MOQC).
Bibliographic record
Abstract
70 Background: Michigan oncology practice groups that participated in MOQC [JOP 5(6):281, 2009] used the Quality Oncology Practice Initiative (QOPI) tool. Adherence to processes of disease specific care was high, but poor in domains associated with palliative care. These measures did not change over time [Health Affairs. 31(4):718, 2012]. These findings prompted us to test interventions to improve quality in palliative care domains. Methods: MOQC created a process, based on the IHI Framework for Spread, to assist oncology practice groups in establishing their own primary Palliative Care services, including the implementation of Edmonton Symptom Management Scale. 8 practice groups formed teams of local change agents to participate in the Palliative Care Demonstration (PC Demo) project. The teams participated in 3 in-person and 4 online learning sessions over 8 months, led by palliative care and quality experts. Teams were provided tools, training materials, and necessary support to implement the improvements and measure their success. The learning network facilitated the sharing of best practices and lessons learned throughout the process. The teams presented their results broadly to other MOQC participants at project conclusion. Results: Success was measured using palliative care-focused ASCO QOPI results. PC Demo sites consecutively improved their scores in many of the QOPI measures, and their rate of improvement from Fall 2011 to Spring 2012 was greater than that of their peers. Conclusions: We observed that collecting and distributing data in our consortium was insufficient to improve palliative oncology care. Providing practice groups with the appropriate infrastructure improved their capacity and capability to make the necessary changes to improve performance. [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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".