OP27 Implementation of the serious illness care programin the hospital setting: emerging results of a multi-site quality improvement collaborative
Bibliographic record
Abstract
Background Seriously ill, hospitalized patients often receive treatment that is not aligned with their values and goals. The Serious Illness Care Program (SICP) is a multi-faceted health system intervention aimed at enabling more person-centered conversations about goals-of-care (GoC) with patients who have serious, life-limiting illness. Methods We conducted a multi-site quality improvement study to adapt and implement the SICP on the medical wards of 3 Canadian hospitals. Our primary outcome measure was the change in patient or family member responses to the validated question: “Over the past 2 days, how much have you felt heard and understood?” (1=not at all; 5=completely) before versus after a conversation about GoC with a clinician trained in the use of the Serious Illness Conversation Guide (SICG). At one site, we also examined health resource use before and after implementation. Results With phased implementation across sites, we trained 57 clinicians in use of the SICG, delivered conversations using the SICG to 205 patients (mean age 76 years), or their family members. Of these conversations, 139 were documented in the electronic medical record. After these guided conversations, participants felt more heard and understood (increase of 0.4 ± 1.1 points; P=0.005). Compared to historical controls, conversations using the SICG were associated with a reduction in length of stay as an acute care patient (5 vs. 19 days, P=0.001). Conclusion The SICP was associated with improvement in patients’ and family members’ perception of being heard and understood by their healthcare team and a reduction in health resource use.
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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 | 0.067 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".