Artificial Intelligence Decision Support Tools for End-of-Life Care Planning Conversations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Why Is This an Issue? End-of-life care provides support for patients and their families during the last stage of life. End-of-life conversations aim to help people better understand their disease prognosis and expected survival, enabling them to make informed decisions regarding end-of-life care. Palliative care focuses on relieving symptoms and improving quality of life for patients with serious or life-threatening diseases. Approximately 89% of patients with life-limiting diseases, such as cancer, can benefit from palliative care. However, not all patients receive it in a timely manner. Due in part to prognostic uncertainty and optimism bias, end-of-life planning conversations and palliative care decisions do not occur early enough to have maximum benefit. Interventions that aim to prompt or help identify those patients who can benefit from palliative and/or end-of-life planning could improve the quality of care. What Is the Technology? An artificial intelligence (AI)–based “nudge” is a decision-making support tool that uses prompts and alerts to aid clinicians in deciding whether and when to discuss end-of-life planning with patients. The nudge sends alerts and/or reminders to clinicians to prompt end-of-life conversations with patients who are at high risk of short-term mortality. These patients are identified by machine learning mortality prediction algorithms incorporated in the electronic health record (EHR) system. Two AI-based nudges designed for patients with cancer were identified. Both tools were developed and internally validated in the US. What Is the Potential Impact? AI-based nudges have the potential to increase the number of end-of-life planning conversations between clinicians and patients as well as the number of referrals to end-of life services. Implementing the nudges into clinical workflows could also help clinicians more easily identify patients with palliative care needs. What Else Do We Need to Know? No AI-based nudges have been approved for use in Canada at the time of this writing nor have there been validation studies using Canadian data. As with many AI algorithms, there is uncertainty about the validity and generalizability of the mortality predictive algorithms used in the nudges. The acceptance of AI-based nudges by clinicians is unclear due to varying clinician attitudes and experiences with nudges and because we did not identify any studies that reported the experience of AI-based nudges from the patient perspective.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it