Artificial Intelligence (AI) and the future of Iran’s Primary Health Care (PHC) system
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
OBJECTIVE: The rapid adoption of Artificial Intelligence (AI) in health service delivery underscores the need for awareness, preparedness, and strategic utilization of AI's potential to optimize Primary Health Care (PHC) systems. This study aims to equip Iran's PHC system for AI integration by envisioning potential futures while addressing past challenges and recognizing current trends. METHOD: This study developed a conceptual framework based on the "Future Triangle" (FT) and the "Health Systems Governance" (HSG) models. This framework delineates the characteristics associated with the 'pulls on the future' for desired and intelligent PHC, as identified by a panel of experts. Additionally, the 'weights of the past'-referring to the challenges faced by Iran's PHC system in utilizing AI-, and the 'push of the present'-which captures the impacts of AI implementation in global primary care settings-were extracted through a review of relevant literature. The integration and analysis of the collected evidence facilitated the formulation of a range of potential future scenarios, including both optimistic and pessimistic scenarios. FINDINGS: The interaction between the three elements of the FT will shape the future states of Iran's PHC, whether optimistic or pessimistic. Building an optimistic scenario for an AI-driven PHC system necessitates addressing past challenges, including deficiencies in the referral and family doctor systems, the absence of evidence-based decision-making, neglect of essential community health needs, fragmented service delivery, high provider workload, and inadequate follow-up on the health status of service recipients. Consideration must also be given to the current impacts of AI in primary care, including comprehensive, coordinated, and need-based service delivery with systematic and integrated monitoring, quality improvement, early disease prevention, precise diagnosis, and effective treatment. Furthermore, fostering a shared vision among stakeholders by defining and advocating for a future system characterized by foresight, resilience, agility, adaptability, and collaboration is essential. CONCLUSION: Envisioning potential future states requires a balanced consideration of the influence of past, present, and future, recognizing the dual potential of AI to drive either positive or negative outcomes. Achieving the optimistic future or the "utopia of intelligent PHC" and avoiding the pessimistic future or the "dystopia of intelligent PHC" requires coherent planning, attention to the tripartite considerations of the future, past, and present, and a clear understanding of the roles, expectations, and needs of stakeholders.
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.
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 | 0,000 | 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 | 0,000 | 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