From Remote Monitoring to Actionable Care: Real-World Implementation of an Interoperable Digital Ecosystem for Peritoneal Dialysis

Background/Objectives: Remote patient monitoring (RPM) has expanded the capacity of peritoneal dialysis (PD) programs to supervise home-based therapy; however, its full clinical value may be limited when patient-generated data remain disconnected from electronic medical records (EMRs). This study evaluated the implementation and impact of integrating the Sharesource RPM platform with the Versia EMR system in routine PD care. Methods: This multicenter real-world implementation study was conducted between November 2024 and February 2025 across three dialysis centers in Colombia. The integration enabled automated transfer of home treatment-related information from Sharesource to Versia and supported population health management through interactive dashboards. Workflow efficiency was assessed using a before-and-after time and motion analysis, and user experience was evaluated through structured surveys administered 12 weeks after implementation. Analyses were descriptive. Results: The integration was successfully implemented across the participating centers, achieving complete data concordance and operational stability. During follow-up of 287 patients, 501 patient-clinic communications, 159 preemptive evaluations, and 122 prescription modifications were recorded, reflecting proactive identification and management of clinical issues. Nursing consultation time decreased from 34 to 26 min per patient, and nephrologist time from 31 to 25 min per patient. Surveys showed high acceptance, supporting proactive clinical decision-making, and greater workflow efficiency. Conclusions: Real-world implementation of an interoperable RPM-EMR ecosystem in PD care was feasible, operationally stable, and was related to improvements in workflow efficiency while supporting proactive population health management. By transforming fragmented patient-generated data into actionable clinical insights, this digital ecosystem may support more efficient, data-driven, and proactive models of chronic kidney disease care.