人工智能可以改善加拿大预防失明的经济效益
Swetha R Chakravarthy1, Dora Mugambi1, Karim Keshavjee1
1Institute of Health, Policy and Management, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
Studies in health technology and informatics
|February 19, 2024
概括
在加拿大,使用人工智能 (AI) 来识别高风险个体的糖尿病视网膜病变查可能更具成本效益. 这种方法旨在预防视力丧失,并减少糖尿病并发症带来的经济负担.
科学领域:
- 眼科医生 眼科 眼科
- 公共卫生 公共卫生
- 卫生经济学 卫生经济学
背景情况:
- 糖尿病视网膜病变是加拿大可预防的视力损失的主要原因,造成了巨大的经济和社会负担.
- 尽管是可以预防的,但许多加拿大人仍然处于风险之中,导致显著的视力障碍和残疾.
研究的目的:
- 对预防因糖尿病视网膜病变导致的失明的干预措施进行经济分析.
- 评估人工智能 (AI) 在优化高风险患者查和召回过程中的潜力.
主要方法:
- 对两种潜在的预防失明干预措施的经济分析.
- 模拟使用人工智能来识别面临高视力损失风险的个体.
- 评估光学家和家庭医生之间数据互操作性的影响.
主要成果:
- 人工智能驱动的高风险个体识别可以显著降低与患者识别,召回和查相关的成本.
- 人工智能方法可以实现与传统的,全面的选和召回计划相似的结果.
- 最小的数据互操作性与人工智能相结合,提高了大规模选的可行性和成本效益.
结论:
- 整合人工智能用于高风险患者识别,为预防加拿大的糖尿病视网膜病变相关失明提供了具有成本效益的策略.
- 医疗保健提供者之间更好的数据共享,再加上人工智能,可以提高查和治疗计划的效率.
- 这种方法有可能减轻视力丧失对个人和加拿大医疗保健系统的经济和社会影响.
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