Real World Implementation of the Serious Illness Care Program in Cancer Care: Results of a Quality Improvement Initiative
Bibliographic record
Abstract
Purpose: Guidelines suggest that advance care planning (ACP) and goals-of-care discussions should be conducted for patients with advanced cancer early in the course of their disease. A recent audit of our health system found that these discussions were rarely being documented in the electronic medical record (EMR). We conducted a quality improvement initiative to improve rates of documentation of goals and wishes among patients with advanced cancer. Methods: On the basis of previous analyses of this problem, we determined that provider capability and opportunity were the main barriers to conducting and documenting serious illness conversations. We implemented the serious illness care program (SICP), a systematic multicomponent intervention that has shown potential for conducting and documenting ACP discussions in two oncology clinics. Our goal was to conduct at least 24 serious illness conversations over the implementation period, with documentation of at least 95% of all conversations. Results: The SICP was implemented in two outpatient medical oncology clinics. A total of 15 serious illness care conversations occurred and 14 (93%) of these conversations were documented in the EMR. Total rates of documentation increased between the preimplementation and implementation period (4.2%–5.4% for clinician A and 0%–7.3% for clinician B). Conclusion: Implementation of the SICP resulted in increased rates of documentation, but the target number of conversations was not met. Further improvement cycles are required to address barriers to conducting and documenting routine serious illness conversations.
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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 | 0.051 | 0.094 |
| 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 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".