Project Overview
NefroCloud is an advanced digital solution designed to support predictive medicine in the field of nephrology
By collecting and analyzing data from dialysis machines, NefroCloud enables healthcare professionals to anticipate a potential decline in dialysis efficiency before it becomes clinically significant, offering a valuable decision-support resource
Please note: Albeit functioning as a digital health device, NefroCloud is not classified as a medical device since it provides early warning signals rather than clinical diagnoses
AIM
To improve the quality and safety of dialysis treatment by reducing the incidence of inadequate dialysis efficiency, through the development of a comprehensive software and cloud infrastructure that enables:
Automated real-time data acquisition from dialysis equipment
Secure cloud-based environment to store and process clinical and device-generated data
Implementation of predictive algorithms to estimate dialysis efficiency and detect anomalies early
User-friendly web interface for healthcare staff to input relevant clinical data and receive alerts and recommendations generated by the system
Method
System architecture and data flow:
both on-premises and cloud components;
the only local element is the interface connected to the dialysis devices (which transmits data securely to the cloud infrastructure);
the cloud hosts the web interface for healthcare providers, the data storage system and the predictive analytics engine
Validation roadmap:
will be conducted in collaboration with NefroCenter, the lead partner in the project, through its extensive dialysis network, providing with access to over 24,000 dialysis sessions per month for a significant and clinically relevant dataset;
will start at Technology Readiness Level (TRL) 4, with the aim of reaching TRL 6 by project completion, demonstrating real-world applicability and scalability
Results
- Measurable improvements across multiple levels of patient care and organizational performance:
- Streamlined patient management, with improved workflow efficiency for dialysis centers
- Continuous patient monitoring, ensuring better tracking of treatment quality
- Proactive identification and communication of issues during or after dialysis sessions
- Early detection of potential treatment risks, allowing clinicians to intervene before complications arise.
- Ultimately contribute to:
- Enhanced clinical outcomes
- Reduced risk of adverse events
- Optimized use of healthcare resources
Conclusion
- Expected benefits directly impacting end-users and various stakeholders:
- Simplification and optimization of patient management
- Active monitoring of the patient during dialysis sessions
- Timely communication of treatment-related issues
- Early detection of potential risks related to reduced dialysis efficiency