Implementation of the Serious Illness Care Program on Hospital Medical Wards
Bibliographic record
Abstract
Background Poor communication with hospitalized patients facing serious, life-limiting illnesses can result in care that is not consistent with patients’ values and goals. The Serious Illness Care Program (SICP) is a communication intervention originally designed for the outpatient oncology setting that could address this practice gap. Methods A multihospital quality improvement initiative adapted and implemented the SICP on the medical wards of four teaching hospitals in Calgary, Hamilton, Ottawa, and Montreal. The SICP consists of three main components: tools (including the Serious Illness Conversation Guide for clinicians), training for frontline clinicians to practice using the Guide, and system change to trigger and support serious illness conversations in practice. Implementation of the SICP at each site followed a phased approach: (1) Building a Foundation; (2) Planning; (3) Implementation; and (4) Sustainability. To assess the success of implementation and its impact, we developed an evaluation framework that includes process measures (e.g., number and proportion of eligible clinicians trained, number and proportion of eligible patients who received a serious illness conversation), patient-reported outcomes (including a validated, single-item “Feeling Heard and Understood” question), and clinician-reported outcomes. Conclusion Based on our adaptation and implementation efforts to date, we have found that the SICP is readily adaptable to an inpatient medical ward setting. Future manuscripts will report on the fidelity of implementation, impact on patient- and clinician-reported outcomes, and lessons learned about how to implement and sustain the program.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 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".