Evaluating Integrating artificial intelligence into Structured Reports to Improve Efficiency and Quality：In the modern healthcare system, radiology plays a critical role. However, traditional radiology reporting predominantly relies on free-text documentation, which not only leads to inconsistencies in expression and difficulties in structuring information but also impacts the efficiency and quality of report generation. Addressing this challenge, our hospital has proactively implemented artificial intelligence for the interpretation of chest X-ray images. We aim to establish a standardized radiological image interpretation workflow by integrating AI-assisted diagnostic results with structured reporting formats. This technology not only enhances the consistency of report writing but also improves the data usability of reports, further facilitating clinical decision support, patient tracking, and medical quality improvement. This implementation addresses the limitations of traditional radiology reports in terms of efficiency and clinical application scalability, showcasing the hospital's innovative practices in the field of smart healthcare.
Between June and September 2024, our hospital conducted a comparative study on the effectiveness of implementing AI technology for emergency department chest X-ray reports. Three types of reports were compared: free-text reports with only the original image (NULL), traditional structured reports supplemented by AI image assistance (AI), and structured reports pre-filled with integrated AI inference results (AI+SR). The results showed that the AI+SR group had the shortest average completion time at 56.82 seconds, significantly reducing report generation time compared to the other two modes (p<0.05). Regarding timeliness, although the initial completion rate for AI+SR was slightly lower, its improvement potential reached an impressive 1218%, indicating substantial optimization possibilities once the workflow becomes familiar. This outcome confirms that integrating AI with structured reporting not only boosts report generation efficiency but also contributes to the refinement of clinical workflows, aligning with the core values of smart healthcare.
In conclusion, the application of artificial intelligence in chest X-ray interpretation is not merely a technological implementation but an innovative, patient-centric action aimed at enhancing medical quality. By integrating AI inference results with structured reporting, our hospital has established a highly efficient and standardized radiological diagnostic workflow, demonstrating a profound fusion of medical technology and clinical application. Looking ahead, this model is expected to be extended to other imaging modalities and clinical settings, further consolidating the hospital's leading position in the smart healthcare transformation and providing patients with more timely, accurate, and high-quality diagnostic and treatment services.