Health economic simulation modeling of an AI-enabled clinical decision support system for coronary revascularization
Notice bibliographique
Résumé
Abstract Importance Coronary revascularization decision-making can be challenging. While artificial intelligence (AI) models have been developed to support this decision-making, health economic evaluation of such models has been rare. Objective To evaluate the economic value of an AI-enabled coronary revascularization decision support system in terms of cost savings and gains in quality adjusted life years (QALY). Design Retrospective health economic simulation modeling study using real-world patient data and AI-generated patient outcome predictions. Setting 26,605 adult patients with obstructive coronary artery disease who underwent diagnostic coronary angiography between 2009 and 2019 in Alberta, Canada. Exposures Clinicians deciding among medical therapy only, percutaneous coronary intervention, and coronary artery bypass grafting were simulated to be provided with AI-generated decision support information in the form of 3- and 5-year major adverse cardiovascular event and all-cause mortality predictions. Main Outcomes and Measures Average cost savings and gains in QALY, represented as a willingness-to-pay, per patient resulting from treatment decisions altered by the AI-generated decision support. Results Most actual coronary revascularization decisions could have been improved by AI decision support from a health economic perspective. At a willingness-to-pay of $50,000 per QALY, as many as 51% of all actual treatment decisions shifted to another treatment, resulting in an average cost saving of $31,204 and a QALY gain equivalent to up to $2,406 per patient. Even in a conservative scenario where clinicians’ AI adoption was limited by ignoring AI recommendations unless the gain in QALY was substantial, 22.4% of the actual decisions shifted, resulting in an average gain of 0.327 QALY, equivalent to up to $16,371, per patient. Conclusions and Relevance AI can help clinicians to optimize coronary revascularization decisions. The health system level economic value of optimized treatment decisions can be substantial in the form of reduced costs stemming from fewer future complications and improved patient outcomes. Key Points Question How much cost saving and gain in quality adjusted life years (QALY) can be expected from using an AI-enabled clinical decision support system for coronary revascularization decision-making? Findings AI was able to improve the cost-effectiveness of 51% percent of actual treatment decisions. Pursuing AI-based optimal treatments would have resulted in an average cost saving of $31,204 and a QALY gain equivalent to $2,406 per patient. Meaning AI can help optimize coronary revascularization decisions, leading to substantial economic value in the form of cost savings and improved patient outcomes.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
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 | 0,005 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».