Process Mapping to Examine How Goals of Care Discussions and Decisions about Life-Sustaining Treatments Occur On Medical Wards: A MultiCenter Observational Study
Bibliographic record
Abstract
Background Poor quality communication about goals of care with seriously ill, hospitalized patients is associated with substantial discordance between prescribed medical orders for life-sustaining treatment and patients’ stated preferences. Designing tailored solutions to this discordance requires a better understanding of this communication process. Objective To acquire a detailed understanding of the process of communication about goals of care and decision making about life-sustaining treatments for hospitalized patients, and to seek opportunities for improvement. Setting Medical wards of three university-affiliated teaching hospitals in Canada. Method At each site, we used drop-in sessions and one-on-one interviews to consult with health care workers on eligible wards to create cross-functional (swim lane) maps of the process of communication about goals of care and decision making about life-sustaining treatments. Healthcare workers were also asked about barriers to this process to enable the identification of opportunities for improvement. Results A total of 112 healthcare workers provided input into the creation of process maps across the three sites. Common elements across sites were that: (1) physicians play a central role, (2) the full process for a given patient involves several interactions amongst members of the interprofessional team, and (3) the process is iterative. We also noted between-site variations in the location of GoC discussions and the extent to which trainees and multi-disciplinary team members were involved. Finally, we identified several key barriers that may serve as targets for future quality improvement efforts: suboptimal location of conversations, insufficient support of physician learners in goals-of-care conversations, and incomplete engagement of the interprofessional team. Conclusion Efforts to improve the quality of goals-of-care discussions and decision making about life-sustaining treatments in the hospital setting need to account for the central role played by physicians in the process but can be enhanced if they can more fully engage the inter-professional health care team.
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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 | 0.021 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".