To investigate the real-world effectiveness of the microservice-based "Cloud Anytime Practice" platform in improving the clinical procedural skills and learning efficiency of standardized residents.

Aligned with the hospital’s standardized training model, we developed the Cloud Anytime Practice platform using a microservice architecture that integrates rubric management, multimodal skill-content management, mobile learning and practice, and video-assignment submission with remote feedback. Thirty 2024-cohort socialized residents from the People’s Hospital of Xinjiang Uygur Autonomous Region were randomly recruited. They used the platform for fragmented learning, simulation training, and video-assignment submission, while instructors provided remote comments and assessments. Pre- and post-training scores (0–100) were compared for four stations—head-and-neck, cardiac, chest, and abdominal examinations by t-test. A questionnaire gauged user acceptance.

Post-training scores for all four stations rose significantly (t=-12.65, -10.29, -13.71, -8.94, all P<0.001), head-and-neck examination improved from 68.53±6.25 to 84.45±2.91; cardiac examination from 59.93±7.86 to 77.46±5.04; chest examination from 70.73±5.15 to 85.78±3.10; and abdominal examination from 69.30±5.85 to 81.58±4.74. Mean clinical skill scores increased by more than 10 points. Survey results showed high resident ratings for the platform’s convenience and practicality, and instructors strongly endorsed its educational value.

The Cloud Anytime Practice platform effectively transcends time and location constraints. Through personalized learning paths, simulation training, and real-time feedback, it significantly enhances the clinical skills and assessment results of residents in Xinjiang, earning broad recognition from both learners and faculty. It represents a viable tool for optimizing local standardized training programs and mitigating uneven distribution of medical education resources.