优化美国学术中心放射科医疗服务及时性

RATIONALE AND OBJECTIVES: Timely radiology access is essential for accurate diagnosis, treatment planning, and efficient care delivery. U.S. academic health centers face distinctive pressures, including workforce shortages, complex workflows, health inequities, and the need to balance trainee education with increasing clinical demand.

MATERIALS AND METHODS: This article summarizes consensus conclusions and literature review findings from the Radiology Research Alliance task force on optimizing healthcare delivery in U.S. academic centers. Task force members reviewed operational and systemic contributors to delayed imaging access and radiology report turnaround time (RTAT), with attention to disparities, workflow design, trainee involvement, and emerging artificial intelligence (AI) applications.

RESULTS: Delays in radiology care arise from social and insurance-related barriers, staffing shortages, workflow inefficiencies, and reporting delays. Underserved populations experience reduced access to preventive imaging, which may be improved through culturally tailored outreach, frontline staff engagement, and patient navigation. In academic settings, RTAT is influenced by image transmission, case complexity, reporting practices, and trainee participation. Evidence suggests that structured reporting, subspecialty or optimized workflow organization, critical results notification, and dual-reader trainee-attending models can improve efficiency while maintaining educational value. AI and machine learning (ML) show promise for clinical decision support (CDS), scheduling, no-show reduction, risk prediction, workflow optimization, and demand forecasting.

CONCLUSION: Improving radiology delivery in academic centers requires coordinated system-level strategies that address equity, staffing, workflow, and reporting efficiency. Thoughtful integration of AI, alongside operational redesign that preserves trainee development, can help academic radiology advance timely, high-quality, and equitable care.