The Surprise Question and Serious Illness Conversations: A pilot study
Bibliographic record
Abstract
BACKGROUND: Serious Illness Conversations aim to discuss patient goals. However, on acute medicine units, seriously ill patients may undergo distressing interventions until death. OBJECTIVES: To investigate the feasibility of using the Surprise Question, "Would you be surprised if this patient died within the next year?" to identify patients who would benefit from early Serious Illness Conversations and study any changes in the interdisciplinary team's beliefs, confidence, and engagement as a result of asking the Surprise Question. DESIGN: A prospective cohort pilot study with two Plan-Do-Study-Act cycles. PARTICIPANTS/CONTEXT: Fifty-eight healthcare professionals working on Acute Medicine Units participated in pre- and post-intervention questionnaires. The intervention involved asking participants the Surprise Question for each patient. Patient charts were reviewed for Serious Illness Conversation documentation. ETHICAL CONSIDERATIONS: Ethical approval was granted by the institutions involved. FINDINGS: Equivocal overall changes in the beliefs, confidence, and engagement of healthcare professionals were observed. Six out of 23 patients were indicated as needing a Serious Illness Conversation; chart review provided some evidence that these patients had more Serious Illness Conversation documentation compared with the 17 patients not flagged for a Serious Illness Conversation. Issues were identified in equating the Surprise Question to a Serious Illness Conversation. DISCUSSION: Appropriate support for seriously ill patients is both a nursing professional and ethical duty. Flagging patients for conversations may act as a filtering process, allowing healthcare professionals to focus on conversations with patients who need them most. There are ethical and practical issues as to what constitutes a "serious illness" and if answering "no" to the Surprise Question always equates to a conversation. CONCLUSION: The barriers of time constraints and lack of training call for institutional change in order to prioritise the moral obligation of Serious Illness Conversations.
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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 | 0.020 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".