Optimizing Advance Care Planning in the Acute Cardiac Care Setting: A combined quality improvement and knowledge translation approach.
Bibliographic record
Abstract
Advance care planning (ACP) is a process by which patients are able to prepare for future in-the-moment medical decision-making and share their values, wishes and preferences. ACP is important as patients are often not well informed about life-sustaining treatments, they can endure more invasive care at end of life than they would want, and they spend more time in hospital than they prefer. Despite known benefits of ACP and recognition of its importance, its integration into regular clinical workflow remains limited. We conducted three studies to examine and address the problem of integrating ACP process into clinical workflow. The first study utilized qualitative methods to characterize ACP process across clinical contexts. In the second study, we utilized an integrated knowledge translation approach to design and implement a multifaceted intervention to routinize ACP process in one hospital unit. We assessed outcomes using an interrupted time series design, and collected data for thirty-two weeks; before, during and after the intervention period. In our third study, we utilized multiple methods to conduct a process evaluation to better understand the effectiveness of our ACP intervention implementation procedure. From our first study, we found that there was significant variability of ACP process both across and within clinical contexts. Segmented regression analysis from our ACP intervention, showed an increase in the proportion of patients to be discharged with a prepared green sleeve, containing their ACP documentation. No significant change was measured for the remaining process and outcome measures. The process evaluation indicated that limitations in the engagement of physicians may have constrained the impact of the intervention. Future opportunities have already begun to address implementation challenges of this work and are using tailored and targeted approaches to improve the reach of intervention components. This program of study comprised of an effort to improve the integration of ACP process into clinical workflow using an iKT approach. Process evaluation helped to provide a deeper understanding of the implementation process. Future research can help to address implementation challenges of this study by focusing on tailored engagement of knowledge users and a strengthening of skill and team building.
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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 | 0.022 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".