Evaluating the Cost-Efficiency and Workflow Impact of AI-Supported Fracture Detection in an Orthopedic Emergency Care Unit

简要总结

详细描述

This clinical trial aims to evaluate the cost-efficiency and workflow impact of AI-assisted fracture detection in an orthopedic emergency care unit. The study is designed as a prospective, randomized, controlled trial to assess whether integrating AI technology can improve diagnostic accuracy, streamline workflow, and reduce healthcare costs compared to the traditional diagnostic approach. Study Objectives Primary Objectives: The primary objective of the SMART Fracture Trial is to assess the impact of AI-assisted X-ray interpretation on physician decision-making and clinical workflows. The study will therefore provide deeper insights into AI's potential benefits and limitations beyond theoretical performance metrics. Secondary Objectives: While the primary focus of the SMART Fracture Trial is on AI's clinical integration, the study will also comprehensively assess diagnostic accuracy and classification performance - key factors that influence real-world implementation. By analyzing these secondary objectives, the study will provide deeper insights into AI's theoretical performance metrics. Study Design This is a prospective, randomized, controlled trial conducted as an international multi-center study. It includes two parallel arms: Control Group: Standard diagnostic procedures without AI assistance. Intervention Group: AI-based diagnostic tools assist in interpreting radiological images. Both groups will follow the same diagnostic imaging protocol, including standard X-ray imaging in two planes. The AI software, pre-validated for fracture detection, will be integrated into the hospital's Picture Archiving and Communication System (PACS). Intervention Details The AI fracture detection systems (Aidoc, Gleamer) are designed to identify fracture patterns, bone lesions, effusion and dislocations on X-rays and highlight areas of potential concern for physician review. The software operates in real time, providing marked-up images to physicians. The AI output serves as a diagnostic aid, with final diagnoses made by the attending physician. Population and Sampling Population: Patients of all ages presenting to the emergency care unit with isolated extremity injuries or isolated joint complaints. Sample Size: Approximately 4,800 participants (2400 per group) to ensure sufficient statistical power for primary outcomes. Randomization: Participants will be randomly assigned to the control or intervention group using a 1:1 allocation ratio. Outcome Measures Primary Outcome Measures: Diagnostic accuracy: Sensitivity, specificity, and AUC of AI-assisted vs. traditional diagnosis. Time to diagnosis: Total time from patient triage to final diagnosis. Secondary Outcome Measures: Cost analysis: A detailed cost comparison of the diagnostic process in both groups. Diagnostic confidence: Assessed using a Likert scale (1-10) completed by physicians after reviewing each case. Study Procedures Baseline Data Collection: Demographics, clinical history, and presenting symptoms will be recorded at enrollment. Standard radiological imaging will be conducted for all participants. AI Integration (Intervention Group): Radiological images will be processed by AI software, providing annotated images to physicians. AI-assisted diagnostic workflows will be compared to standard workflows. Outcome Assessment: All diagnoses will be independently reviewed by a panel of experts, including an experienced radiologist and orthopedic surgeon, to establish a reference standard for comparison. Ethical Considerations The study adheres to the principles of the Declaration of Helsinki and has received approval from the local ethics committee. Written informed consent will be obtained from all participants before enrollment. Data will be pseudonymized to maintain confidentiality. Expected Impact This study aims to provide robust evidence regarding the effectiveness of AI in improving diagnostic workflows in emergency care settings. Findings may inform the future integration of AI tools into clinical practice, improving patient outcomes and optimizing resource utilization in high-volume emergency care environments.

主要结局

次要结局

• Time to Diagnosis(During the patient's emergency department visit, typically within 4 hours of presentation.)
• Physician Diagnostic Confidence(Measured immediately after the diagnosis)
• Cost-Efficiency of Diagnostic Workflow(Calculated at the end of the study for all enrolled participants, approximately 6 months from study initiation.)