Digital Automation in Hemodialysis: Multinational Implementation and Usability Outcomes of the Treatment Guidance System (TGS) (Preprint)

Abstract

<sec> <title>BACKGROUND</title> Manual documentation of hemodialysis treatments remains resource-intensive, prone to missing values, and delays data availability for decision support. Digital innovations integrating automation with EMRs have potential to reduce cognitive burden, enhance usability, and improve patient–provider interactions. </sec> <sec> <title>OBJECTIVE</title> To evaluate the impact of the Treatment Guidance System (TGS), an automated documentation tool, on data quality, staff workload, satisfaction, and efficiency in multinational hemodialysis settings. </sec> <sec> <title>METHODS</title> We conducted a prospective, observational, multicenter study (November 2021–January 2023) in 20 clinics across Saudi Arabia, Spain, Portugal, the UK, and Kazakhstan. Eligible participants were registered nurses with ≥1 year dialysis experience and no prior TGS exposure. TGS integrates machine-generated treatment data with an EMR interface via d.Connect. Outcomes were assessed across three phases (pre-implementation, 1 month, 3 months). Workload was measured using NASA-TLX; satisfaction through structured surveys; documentation time by direct observation; and accuracy/completeness by comparing TGS vs. manual records (n=89 sessions, Saudi Arabia). </sec> <sec> <title>RESULTS</title> A total of 223 nurses participated.Staff satisfaction increased significantly (3.86 to 4.03 on a 5-point scale). NASA-TLX demonstrated improvements across all workload domains, with physical demand decreasing by &gt;60% and mental demand by ~40%. Median documentation time dropped from 9:35 minutes pre-TGS to 5:22 minutes at 3 months (p&lt;0.001), with the largest reduction in the UK. Data quality analysis showed ~80% excellent correlation between TGS and dialysis machine black-box records, compared with substantially lower accuracy and &gt;60% missing values in manual records pre-TGS. There was some variation of the observed time used for documentation between different countries, however, the observed time decreased in all countries from a median (25-75%)value of 9:35 (5:36-14:41) minutes at timepoint phase 1 to a median of 5:22 (2:05-9:22) minutes at timepoint phase 3 (p&lt;0.001). </sec> <sec> <title>CONCLUSIONS</title> Implementation of TGS enhanced usability, reduced cognitive and physical workload, improved data completeness and accuracy, and freed up nursing time for patient interaction. This study provides real-world evidence of how digital automation can transform workflow and care delivery in high-volume hemodialysis environments. TGS illustrates how integrating automation with EMRs can contribute to sustainable, patient-centered, and digitally optimized healthcare. </sec> <sec> <title>CLINICALTRIAL</title> non </sec>

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