A Mixed Methods Study to Assess the Clinical Effectiveness and Acceptability of qER Artificial Intelligence Software to Prioritise CT Head Interpretation.
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 16,800
- 试验地点
- 4
- 主要终点
- Reporting turnaround time with qER prioritisation
研究概览
简要总结
Non-Contrast Computed Tomography (NCCT) of the head is the most common imaging method used to assess patients attending the Emergency Department (ED) with a wide range of significant neurological presentations including trauma, stroke, seizure and reduced consciousness. Rapid review of the images supports clinical decision-making including treatment and onward referral.
Radiologists, those reporting scans, often have significant backlogs and are unable to prioritise abnormal images of patients with time critical abnormalities. Similarly, identification of normal scans would support patient turnover in ED with significant waits and pressure on resources.
To address this problem, Qure.AI has worked to develop the market approved qER algorithm, which is a software program that can analyse CT head to identify presence of abnormalities supporting workflow prioritisation.
This study will trial the software in 4 NHS hospitals across the UK to evaluate the ability of the software to reduce the turnaround time of reporting scans with abnormalities that need to be prioritised.
详细描述
Study background:
Emergency Departments (ED) across the UK are overburdened with increasing patient demand, radiology staff shortages and rising patient wait times. Head injuries are a frequent cause of emergency attendance in the UK with computed tomography(CT) scans usually the first imaging tests to diagnose head injuries and strokes.
A report issued by National Institute of Health and Care Excellence (NICE), confirms that each year 1.4 million people attend emergency departments in England and Wales with head injury. Among the 200,000 patients admitted annually, one-fifth of them suffer from a Traumatic Brain Injury with skull fracture or evidence of brain damage. Head injury is the most common cause of death and disability in people up to the age of 40. Early detection and prompt treatment is vital to save lives and minimise risk of disability, according to the NICE guidelines of Head injury: assessment and early management. Head CT scans are the gold standard for diagnosing these and it is critical that these are performed and reported by Radiologists in line with NICE guidelines.
The potential applications of AI in radiology go well beyond image analysis for diagnostic and prognostic opportunities. It is becoming increasingly clear that AI algorithms have the potential to improve productivity, operational efficiency, and accuracy in diagnostic radiology. AI tools are being developed to aide diagnosis and enhance processes at multiple point in the radiology workflow including:
(a) protocolling the prioritised scan,(b) clinical decision support systems for detection of critical findings, (c) worklist priority adjustment via AI results, and (d) reducing turnaround time through worklist prioritisation and semiautomated structures reporting. The adoption of AI tools is dependent on the demonstration of a tangible effect on patient care and improvement in radiologist workflow.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Individuals undergoing Head CT scan at the ED / A&E (Accident and Emergency Services).
- •Non-contrast axial CT scan series with consistently spaced axial slices.
- •Soft reconstruction kernel covering the complete Brain.
- •Maximum slice thickness of 6mm.
排除标准
- •There are no explicit exclusion criteria for qER as all scans in inclusion criteria will be processed by qER. Exclusion criteria are implicit within the inclusion criteria listed above.
结局指标
主要结局
Reporting turnaround time with qER prioritisation
时间窗: 1 year
Time taken to report NCCT head from acquisition for patients with prioritised findings in Emergency Department compared to standard of care. Measured as time in minutes from the scan acquisition to the final radiology report of prioritised scans.
次要结局
- Impact of qER supported reporting on teleradiology.(1 year)
- Time to initiation of treatment from NCCT acquisition for prioritised scans.(1 year)
- Percentage of qER non-prioritised scans but identified by the radiologist as absence of finding.(1 year)
- Reporting turnaround time with qER prioritisation for scans without prioritised findings in Emergency Department compared to standard of care.(1 year)
- Reporting turnaround time with qER prioritisation for scans with an absence of findings in Emergency Department compared to standard of care.(1 year)
- Percentage of NCCT heads that qER classifies as prioritised, non-prioritised and absence of findings.(1 year)
- Assess the impact of qER on radiology reporting workflow on other requests for CT scans.(1 year)
- Assess utility of qER to support clinical decision making of the patients from the emergency department requiring an NCCT.(1 year)
- Death within 28 days of NCCT head acquisition.(1 year)
- Percentage of qER non-prioritised scans but identified by the radiologist as prioritised.(1 year)
- Assess the safety of qER(1 year)
- Health Economic Assessment(1 year)
- Assess utility of qER to support referral or discharge of the patients from the emergency department requiring an NCCT.(1 year)
- Technical evaluation of product performance.(1 year)