Documenting Goals of Care Among Patients With Advanced Cancer: Results of a Quality Improvement Initiative
Bibliographic record
Abstract
PURPOSE: Guidelines recommend that oncologists discuss goals of care (GOC) with patients who have advanced cancer and that these patients be referred for early palliative care (PC). An audit of practice between 2010 and 2015 at the Cancer Centre of Southeastern Ontario suggested that these rates were suboptimal. We sought to improve the rate of documentation of GOC and referral to PC through the implementation of a quality improvement (QI) initiative. METHODS: Patients receiving palliative systemic treatment of lung, pancreatic, colorectal, and breast cancer were identified via electronic pharmacy records and the electronic patient care system. Using the Define, Measure, Analyze, Improve, Control QI methodology, we drafted a guideline for GOC documentation and PC referral and designed a standardized documentation system. E-mail reminders were sent to physicians and a QI scorecard was displayed to document overall and individual physician rates of GOC documentation. Data were analyzed monthly and presented on statistical process control P charts. RESULTS: Between May 2016 and November 2017, a total of 303 unique patients were identified (52%, 21%, 17%, and 10% with lung, breast, colorectal, and pancreatic cancer, respectively). GOC documentation increased significantly over the study period (baseline, 0%; passive phase, 3%; active phase, 31%); this increase was likely because of our intervention. PC referral rates also increased over the study period (baseline, 36%; passive phase, 35%; active phase 48%). We did not identify any patient, physician, or disease factors that were associated with GOC discussion or referral to PC. CONCLUSION: Our QI initiative was successful in improving rates of GOC documentation in patients with advanced cancer.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".