Accuracy and generalizability of using automated methods for identifying adverse events from electronic health record data

使用自动化方法从电子健康记录数据中识别不良事件的准确性和普遍性

• 批准号: 346592
• 负责人: Rochefort Christian
• 金额: $ 22.28万
• 依托单位: Universit de Sherbrooke
• 依托单位国家: 加拿大
• 项目类别: Operating Grants
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-03-01 至 2020-03-01
• 项目状态: 已结题
• 来源: https://webapps.cihr-irsc.gc.ca/decisions/p/project_details.html?applId=346592&lang=en
• 关键词: Acute Care Hospitals; Adverse Events; Automated Detection; Central Line-Associated Bloodstream Infection; Electronic Health Record; Health Informatics; Hospital-Acquired Pneumonia; Validation Study; Ventilator-Associated Events

Adverse events (AEs) are injuries caused by medical management rather than by the underlying condition of the patient. AEs are estimated to occur in 2.9% to 16.6% of all acute care hospitalizations, and studies have suggested that 30% to 58% of all AEs ar

不良事件（AE）是由医疗管理而不是患者的潜在状况造成的伤害。 AE估计在所有急性护理住院治疗的2.9％至16.6％中发生，研究表明，所有AES AR的30％至58％