Artificial Intelligence for Chest Radiography: Impact on Economics, Patient Outcomes and Radiology Service Delivery
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 10,000
- 主要终点
- Time to discharge
研究概览
简要总结
Randomized Clinical Trial of the impact of Chest radiograph AI-assisted triage and report generation upon clinical outcomes and an economic analysis of impact of AI decision support on radiology service delivery.
详细描述
Randomized, prospective selection of patients. Control group involves radiologists reporting chest radiographs as per reference standard clinical workflow Intervention group involves radiologists assisted with AI reporting an AI-triaged worklist of chest radiographs using an AI report generation tool Clinical outcomes on patients are studied at pre-determined study endpoints, including time to discharge from the hospital and re-admission rates.
Economic analysis on cost-avoidance from man-hours saved from report generation and triage.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 年龄范围
- 14 Years 至 130 Years（Child, Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •All patients attending radiography to have chest radiographs during the study period
排除标准
- •age below 14
- •deceased before discharge
- •chest radiograph performed in non-standard projections
研究组 & 干预措施
AI assisted
AI assisted detection, triage and reporting of CXR
干预措施: AI (Diagnostic Test)
Control Arm
Chest radiographs reported with AI assistance
结局指标
主要结局
Time to discharge
时间窗: 12 months
Time from patient arrival at radiography department to time to discharge from hospital
Report generation time
时间窗: 12 months
Time for radiologist to produce each individual CXR report
Turnaround Time
时间窗: 12 months
Time from patient arrival at radiography department to time for clinical team to receive report
次要结局
- 30-day patient readmission rate(12 months)
研究者
Charlene Liew
Clinical Assistant Professor
Duke-NUS Graduate Medical School