Evaluation of Clinical Effectiveness and Implementation of an Artificial Intelligence Based Decision Support Tool That Guides Early Rehabilitation After Gastrointestinal and Oncology Surgery
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 103
- 试验地点
- 1
- 主要终点
- Proportion of patients requiring unplanned escalation of hospital-specific care within 30 days after early transfer to rehabilitation area.
研究概览
简要总结
After gastrointestinal or oncology surgery, it can be difficult to determine when a patient is ready to safely begin early rehabilitation or move toward discharge. Delays may prolong hospital stay, while premature decisions may increase risks.
This study evaluates an artificial intelligence (AI)-based decision support tool that analyzes routinely collected hospital data to identify patients who are likely ready for early rehabilitation and discharge planning after surgery. The tool provides a simple yes/no output to support clinicians in their decision-making.
The AI tool does not replace clinical judgment. Treating physicians remain fully responsible for all care decisions.
The purpose of this study is to examine how well this tool performs in clinical practice and how it can be safely and effectively implemented to support postoperative care.
详细描述
Patients who undergo gastrointestinal or oncology surgery often require careful monitoring after their operation. During the days following surgery, healthcare professionals assess many factors, such as vital signs, laboratory results, recovery progress, and the need for hospital-based treatments. Based on this information, decisions are made about when patients can safely start early rehabilitation or move toward discharge planning.
In this study, researchers are evaluating an artificial intelligence (AI)-based decision support tool designed to assist clinicians with these decisions. The tool analyzes routinely collected information from the electronic patient record, including demographic data, type of surgery, vital signs, laboratory values, and medication information. Using these data, the system provides a simple yes/no output indicating whether a patient is likely ready for early rehabilitation and discharge planning on the second day after surgery.
The AI tool is advisory only. It does not make treatment decisions and cannot initiate any actions. The treating physician always reviews the patient's condition independently and makes the final decision about care, rehabilitation, and discharge planning.
The study focuses on two main aspects:
- How accurately the AI tool identifies patients who are ready for early rehabilitation and discharge planning.
- How the tool can be safely and practically integrated into everyday clinical workflows.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Diagnostic
- 盲法
- None
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
- •Adults aged 18 years or older
- •Undergoing gastrointestinal or oncological surgery
- •Postoperatively admitted to the surgical ward
- •Expected to remain admitted for at least 2 days after surgery
排除标准
- •Admitted to the intensive care unit (ICU) at the time of prediction on postoperative day 2
- •Inability to provide informed consent in Dutch or English
研究组 & 干预措施
Cohort of 103 patients undergoing GE/oncological surgery and admitted >2 days after surgery
干预措施: DESIRE: AI-Based Clinical Decision Support for Postoperative Rehabilitation Planning (Device)
结局指标
主要结局
Proportion of patients requiring unplanned escalation of hospital-specific care within 30 days after early transfer to rehabilitation area.
时间窗: From postoperative day 2 (time of AI prediction and potential transfer to rehabilitation area) through 30 days after surgery
This is a composite outcome, consisting of any of the following events: ICU admission Re-operation Radiological intervention Administration of intravenous antibiotics Respiratory failure (new need for supplemental oxygen) 30-day mortality 30-day emergency readmission
次要结局
未报告次要终点
研究者
D.E. Hilling
Surgeon and Data Scientist
Erasmus Medical Center