Serious Illness Care Programme—contextual factors and implementation strategies: a qualitative study
Bibliographic record
Abstract
OBJECTIVES: The Serious Illness Care Programme (SICP) is a multicomponent evidence-based intervention that improves communication about patients' values and goals in serious illness. We aim to characterise implementation strategies for programme delivery and the contextual factors that influence implementation in three 'real-world' health system SICP initiatives. METHODS: We employed a qualitative thematic framework analysis of field notes collected during the first 1.5 years of implementation and a fidelity survey. RESULTS: Analysis revealed empiric evidence about implementation and institutional context. All teams successfully implemented clinician training and an electronic health record (EHR) template for documentation of serious illness conversations. When training was used as the primary strategy to engage clinicians, however, clinician receptivity to the programme and adoption of conversations remained limited due to clinical culture-related barriers (eg, clinicians' attitudes, motivations and practice environment). Visible leadership involvement, champion facilitation and automated EHR-based data feedback on documented conversations appeared to improve adoption. Implementing these strategies depended on contextual factors, including leadership support at the specialty level, champion resources and capacity, and EHR capabilities. CONCLUSIONS: Health systems need multifaceted implementation strategies to move beyond the limited impact of clinician training in driving improvement in serious illness conversations. These include EHR-based data feedback, involvement of specialty leaders to message the programme and align incentives, and local champions to problem-solve frontline challenges longitudinally. Implementation of these strategies depended on a favourable institutional context. Greater attention to the influence of contextual factors and implementation strategies may enable sustained improvements in serious illness conversations at scale.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".