Objective Construction and Practice of a Whole-Process Digitalized Hemodialysis Management System.Methods ① Integrate device data through the Internet of Things(IoT)to enable automatic collection of dialysis parameters and real-time validation of quality control rules.② Build an"intelligent error prevention system"based on Data Analysis,realizing the transformation from patient management to digital quality control management in hemodialysis treatment.③ Implement a closed-loop management system for the entire patient dialysis process.Results The system reduced clinicians' time spent manu-ally filling and verifying documentation by 75％,and increased the adequacy compliance rate for maintenance dialysis patients to 100％per quarter and other goals.Conclusion Traditional hemodialysis management relies on manual records,leading to issues such as fragmented data,delayed quality control,material waste,and high risks of hospital-acquired infections.The entire process,from patient appointment to post-dialysis summary management lacks coordination,and manual calculation of key indica-tors is prone to errors,with insufficient timeliness in quality surveillance warnings.The construction of an information system can break down operational silos and enable"data-driven decision-making",and provides a replicable model for the digital manage-ment of hemodialysis.

Objective Construction and Practice of a Whole-Process Digitalized Hemodialysis Management System.Methods ① Integrate device data through the Internet of Things(IoT)to enable automatic collection of dialysis parameters and real-time validation of quality control rules.② Build an"intelligent error prevention system"based on Data Analysis,realizing the transformation from patient management to digital quality control management in hemodialysis treatment.③ Implement a closed-loop management system for the entire patient dialysis process.Results The system reduced clinicians' time spent manu-ally filling and verifying documentation by 75％,and increased the adequacy compliance rate for maintenance dialysis patients to 100％per quarter and other goals.Conclusion Traditional hemodialysis management relies on manual records,leading to issues such as fragmented data,delayed quality control,material waste,and high risks of hospital-acquired infections.The entire process,from patient appointment to post-dialysis summary management lacks coordination,and manual calculation of key indica-tors is prone to errors,with insufficient timeliness in quality surveillance warnings.The construction of an information system can break down operational silos and enable"data-driven decision-making",and provides a replicable model for the digital manage-ment of hemodialysis.