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. The objective of this study was to implement ACP through adapted Serious Illness Care Program (SICP) training sessions, and to understand primary care provider (PCP) perceptions of implementing ACP into practice. Methods We conducted a quality improvement project guided by the Normalization Process Theory (NPT), in an interprofessional academic family medicine group in Hamilton, Ontario, Canada. NPT is an explanatory model that delineates the processes by which organizations implement and integrate new work. PCPs (physicians, family medicine residents, and allied health care providers), completed pre- and post-SICP self-assessments evaluating training effectiveness, a survey evaluating program implementability and sustainability, and semi-structured qualitative interviews to elaborate on barriers, facilitators, and suggestions for successful implementation. Descriptive statistics and pre-post differences (Wilcoxon Sign-Rank test) were used to analyze surveys and thematic analysis was used to analyze qualitative interviews. Results 30 PCPs participated in SICP training and completed self-assessments, 14 completed NoMAD surveys, and 7 were interviewed. There were reported improvements in ACP confidence and skills. NoMAD surveys reported mixed opinions towards ACP implementation, specifically concerning colleagues’ abilities to conduct ACP and patients’ abilities to participate in ACP. Physicians discussed busy clinical schedules, lack of patient preparedness, and continued discomfort or lack of confidence in having ACP conversations. Allied health professionals discussed difficulty sharing patient prognosis and identification of appropriate patients as barriers. Conclusions Training in ACP conversations improved PCPs’ individual perceived abilities, but discomfort and other barriers were identified. Future iterations will require a more systematic process to support the implementation of ACP into regular practice, in addition to addressing knowledge and skill gaps.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| 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".