Artificial Intelligence Decision Support Tools for End-of-Life Care Planning Conversations
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Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle