使用临床决策智能应用程序来改善早期检测未被认可的认知障碍的途径
A S Khachaturian1, B Cassin2, G R Finney3
1Executive Vice-President, Campaign to Prevent Alzheimer's Disease 20/20.
JAR life
|May 26, 2023
概括
全球痴呆症护理成本正在升,给医疗保健系统带来压力. 实施人工智能/机器学习 (AI/ML) 驱动的临床决策智能应用程序 (CDIA) 需要确保对更好的痴呆症护理准备的见解采取行动.
科学领域:
- 老年学是指老年学的学科.
- 医疗保健信息学 医疗保健信息学
- 人工智能在医学中的应用
背景情况:
- 痴呆症护理的全球成本正在上升,预计到2025年将达到1万亿美元.
- 现有的医疗保健基础设施在及时识别痴呆症方面面临挑战,特别是在服务不足的地区.
- 未被诊断的认知障碍对医疗保健系统的能力构成重大风险.
研究的目的:
- 突出全球日益增长的痴呆症和认知障碍的负担.
- 确定当前医疗保健系统中对于痴呆症患者识别的关键障碍.
- 探索医疗保健生物信息学和AI/ML驱动的CDIA的潜力,以提高准备.
主要方法:
- 审查当前全球医疗保健基础设施的局限性.
- 对痴呆症护理的成本预测分析.
- 探索AI/ML驱动的临床决策智能应用程序 (CDIA) 作为解决方案.
主要成果:
- 世界各地的医疗保健系统没有足够的装备来应对不断增加的痴呆病例.
- 人员,基础设施和诊断缺乏,阻碍了早期检测.
- 人工智能/ML-CDIA是一个有前途的途径,但需要用户采用才能有效.
结论:
- 紧急实施准备计划是必要的,以满足未来的需求.
- 医疗保健生物信息学和AI/ML-CDIA可以提高获取和效率.
- 确保患者和从业人员对AI/ML见解采取行动,对于成功实施至关重要.
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