Quality of clinicians’ conversations with patients and families before and after implementation of the Serious Illness Care Program in a hospital setting: a retrospective chart review study
Bibliographic record
Abstract
Background: Seriously ill patients in hospital have indicated that better communication with practitioners is vital for improving care. The aim of this study was to assess whether the quality of conversations about serious illness improved after implementation of the Serious Illness Care Program (SICP). Methods: In this retrospective chart review study, we evaluated patients who were admitted to a medical ward at Hamilton General Hospital, had a stay of at least 48 hours, and were at risk for a lengthy stay or increased need for community-based services (inter-RAI Emergency Department Screener score of 5 or 6). The SICP study period was from Mar. 1, 2017, to Jan. 19, 2018. We used a validated codebook to assess the quality of documented conversations regarding serious illness for eligible patients before (usual care [control group]) and after SICP implementation (intervention group), specifically examining the following domains: patients’ values and goals, understanding of prognosis and illness, end-of-life care planning, and code status or desire for other life-sustaining treatments. Results: The study sample included 56 patients in the control group and 56 patients in the intervention group. The overall quality of documented conversations about serious illness was significantly higher in the intervention group than in the control group (p < 0.001) and was significantly higher in the subdomains of values and goals (p < 0.001), understanding of prognosis and illness (p < 0.001) and life-sustaining treatments (p = 0.03) but not end-of-life care planning (p = 0.48). Interpretation: Implementation of the SICP in a hospital setting was associated with higher quality of documented conversations regarding serious illness with patients at high risk for clinical or functional deterioration. The SICP is transferable and adaptable to a hospital setting, and was associated with an increase in adherence to best practices compared to usual care.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".