Advance Care Planning Conversations in Primary Care: a Quality Improvement Project Using the Serious Illness Care Program
Bibliographic record
Abstract
Abstract Background: Advance care planning (ACP) conversations are associated with improved end-of-life healthcare outcomes and patients want to engage in ACP with their healthcare providers. Despite this, ACP conversations rarely occur in primary care settings. Therefore, the objective of this study was to implement ACP through adapted Serious Illness Care Program (SICP) training sessions, and to understand primary care provider perceptions of implementing ACP into practice. Methods: We conducted a quality improvement project guided by the Normalization Process Theory (NPT). NPT is an explanatory model the delineates the processes by which organizations implement and integrate new work. The project was implemented in an interprofessional academic family medicine group in Hamilton, Ontario, Canada. Primary care providers (PCP), consisting of physicians, family medicine residents, and allied health care providers, completed Pre- and post-SICP training self-assessments, NoMAD surveys, and structured interviews. Results: 30 PCPs participated in SICP training and completed self-assessments, 14 completed NoMAD surveys, and 7 were interviewed. Training self-assessments reported improvements in ACP confidence and skills. NoMAD surveys reported mixed opinions towards ACP implementation into primary care, raising concerns with their colleagues’ abilities to conduct ACP and their patients’ abilities to participate in ACP conversations. Interviews identified barriers to successful implementation including busy clinical schedules, patient preparedness, and provider discomfort or lack of confidence in having ACP conversations. Allied health identified discussing prognosis, scope of practice limitations and identification of appropriate patients as barriers. Conclusions: Our findings complement existing literature regarding high PCP confidence and willingness to conduct ACP, but low participation which may be attributed to logistical challenges. Our findings identified areas of the SICP that were more difficult to implement (i.e., prognostication). Future iterations will require a more systematic process to support the implementation of ACP into regular practice and to address knowledge gaps identified with targeted training.
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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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| 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".