Toward Improved Goals-Of-Care Documentation in Advanced Cancer: Report on the Development of a Quality Improvement Initiative
Bibliographic record
Abstract
Background: Documentation of advance care planning for patients with terminal cancer is known to be poor. Here, we describe a quality improvement initiative. Methods: Patients receiving palliative chemotherapy for metastatic lung, pancreatic, colorectal, and breast cancer during 2010–2015 at the Cancer Centre of Southeastern Ontario were identified from electronic pharmacy records. Clinical notes were reviewed to identify documentation of care plans in the event of acute deterioration. After establishing baseline practice, we sought to improve documentation of goals of care and referral rates to palliative care. Using quality improvement methodology, we developed a guideline, a standardized documentation system, and a process to facilitate early referral to palliative care. Results: During 2010–2015, 456 patients were included in the baseline cohort: 63% with lung cancer, 16% with colorectal cancer, 13% with pancreatic cancer, and 7% with breast cancer. Care goals in the event of an acute illness were documented by medical oncologists in 6% of cases (26 of 456). Of the 456 patients, 47% (n = 214) were seen by palliative care; care goals were documented by palliative care in 48% of the patients seen (103 of 214). With those baseline data in hand, a local practice guideline and process was developed to facilitate the identification of patients for whom advance care planning and early palliative care referral should be considered. A system was also established so that goals-of-care documentation will be supported with a written framework and broadly accessible in the electronic medical record. Conclusions: Low rates of documentation of advance care planning and referral to palliative care persist and have stimulated a local quality improvement initiative.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.116 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".