Reducing the Burden of Haemodialysis Treatment Interruptions Due to Machine Failures by Continuous Predictive Surveillance
Abstract
BACKGROUND: Survival and well-being of patients on haemodialysis depend on the provision of regular and effective therapy. Interruptions that shorten a haemodialysis session have adverse effect on patient outcomes. With increasing demands for haemodialysis, machines nowadays are in constant use with usually two or three daily shifts throughout the week. Increased machine malfunction is therefore inevitable, resulting in inadequate treatment and considerable stress for patients and care staff. Unplanned service demands are increasingly a reality. Service technicians often need to be summoned at short notice, but without full knowledge of the nature of the failure, appropriate spare parts often are unavailable on site, meaning that additional visits to replace components need to be planned. OBJECTIVE: To mitigate these problems, the Clinics Predictive Maintenance tool was devised to monitor the 'technical health' of haemodialysis machines throughout their operational life. The tool enables continuous and remote assessment of haemodialysis machines to predict failures, thereby allowing technicians to proactively schedule onsite servicing to replace affected components. DESIGN: The Clinics Predictive Maintenance tool was assessed in a field study (1235 machines in 34 dialysis units of a provider network) in Portugal between 2019 and 2023. RESULTS: Compared to the pre-monitoring period, breakdown rates decreased while mean time between failures increased. Unplanned service actions were reduced by 27%. CONCLUSIONS: The study shows that pre-emptive and timely servicing of haemodialysis equipment prevents disruptions of treatments by machine failures. The Clinics Predictive Maintenance tool enables staff to manage multiple patients and shifts throughout the day more effectively, mitigating potential risks associated with ineffective or improperly maintained equipment.
Fields of Study & Subjects
Quality and Safety in Healthcare
Reliability and Maintenance Optimization
Dialysis and Renal Disease Management
Publication details
- Journal / source
- Journal of Renal Care
- Volume
- 52
- Issue
- 1
- Pages
- e70054–e70054
- Publication date
- 1 March 2026
- Language
- EN