Digital Hemodialysis Center

The real-time operating layer
for dialysis centers

Connect treatment devices, support infrastructure, environment, and clinical data into one risk-control operating layer.

Suntop is not positioning a single software tool. It is a software, hardware, and data-service foundation for dialysis centers: device connectivity, bedside terminals, central monitoring, AI risk alerts, and workflows that turn daily practice into an executable closed loop.

Whole-center IoTAI risk controlAsset performanceFlexible deployment

100+ smart dialysis centers | 5000+ connected machines | Cloud / local / hybrid deployment

100+
Smart dialysis centers
5000+
Connected machines
20+
Core and dynamic alarms
The Problem We Solve

Core Clinical Challenges in Traditional Dialysis

Intradialytic hypotension (IDH) occurs in 15-30% of sessions¹, causing myocardial stunning, ischemic brain white matter injury, and other organ damage². Studies show that nadir systolic BP <90 mmHg during dialysis is significantly associated with mortality³, and ultrafiltration rates >10 ml/h/kg increase mortality risk by 9%⁴. Additionally, dry weight assessment is highly experience-dependent, with significant variability between physicians.

Our AI system analyzes BP trends, UF tolerance patterns, and historical treatment data to predict IDH events minutes before onset, optimize UF profiling, and provide evidence-based dry weight recommendations.

References

1. Daugirdas JT. Measuring Intradialytic Hypotension to Improve Quality of Care. J Am Soc Nephrol 2015;26:512-514

2. McIntyre CW. Hemodialysis-Associated Cardiomyopathy: A Newly Defined Disease Entity. Semin Dial 2014;27:87-97

3. Flythe JE, et al. Association of Mortality Risk with Various Definitions of Intradialytic Hypotension. J Am Soc Nephrol 2015;26:724-734

4. Saran R, et al. Longer treatment time and slower ultrafiltration in hemodialysis: associations with reduced mortality in the DOPPS. Kidney Int 2006;69:1222-1228

IDH Prevention

Reduced intradialytic hypotension through predictive alerts

Dry Weight Management

Data-driven dry weight assessment, reducing inter-physician variability

Risk Detection

Multi-dimensional analysis to identify potential complication risks early

Data-Driven Care

Transforming dialysis from experience-driven to data-driven precision management

Core Capabilities

From device data to clinical and operational closed loops

Whole-center IoT becomes the foundation for data capture, AI analysis, alarm escalation, bedside execution, and quality-control review.

Center devicesDialysis / water / environment
Data platformHospital systems + device streams
AI risk centerPredict / assess / review
Closed-loop actionBedside / physician / QC

Whole-center IoT data infrastructure

Connect dialysis machines, bedside terminals, vital-sign devices, support systems, environmental sensors, and hospital systems into a center-level real-time data layer.

Machines and bedside terminalsWater / fluid / power / environmentHIS/LIS/EMR integration

Dialysis-specific AI and risk models

Build models around IDH, UF tolerance, dry weight, four-item monitoring, and alarm review while keeping clinical explainability and physician oversight central.

IDH risk warningUF and volume managementFour-item monitoring models

Bedside loop and operational control

Bring alerts, order confirmation, supply checks, quality metrics, and asset performance into one execution loop to reduce manual switching and missed steps.

Bedside terminal workflowAlarm escalation loopAsset performance management
Clinically Proven

Real Data, Real Improvements

60%
Pre-shock patients reduced
22.4% → 9.0%
70%
Lab evaluation efficiency up
8.3 min → 2.5 min
71%
Iron deficiency reduced
74.0% → 21.1%
37%
Renal bone disease reduced
48.4% → 30.7%

Based on data from Suntop-operated dialysis centers, 2024-2025

Validated Deployment Scale

30+
Self-operated Centers
100+
Partner Centers
100+
Smart Dialysis Centers
5000+
Connected Machines