Abstract Rationale Home medical equipment (HME) providers are essential in managing chronically ventilated patients, but oftenlack tools to proactively monitor clinical risk. 1 The Nexus Platform (Encore Healthcare, Livingston, TN) is an AI-based, algorithm-driven software system that integrates patient assessments, telemonitoring, predictivealerts, and data analytics to guide clinical decision-making for home ventilator patients. This study evaluatedthe platform’s impact on hospitalizations, respiratory infections, and healthcare costs among adult, non-COPDventilator-dependent patients. Methods A retrospective analysis was conducted on a cohort of 3, 939 adult home ventilator patients enrolled in theNexus Program for an average of 29 months. The population included patients with neuromuscular disease (NMD), interstitial lung disease (ILD), and non-COPD hypoventilation disorders; 20% were invasively ventilatedand 80% non-invasively ventilated. Baseline hospitalization and respiratory infection rates from the 12 monthsbefore Nexus enrollment were compared with outcomes during program participation. AI-driven alerts andpredictive analytics within the platform guided timely interventions. Estimated healthcare cost savings werecalculated based on avoided hospitalizations, using an average hospitalization cost of approximately 20, 000per stay, based on prior estimates for mechanically ventilated adult patients. 2 Results Deployment of the AI-based platform was associated with a 68% reduction in hospitalizations and a 35%reduction in respiratory infections compared to the 12 months prior to enrollment. Across the cohort, thisequated to 4, 193 hospitalizations avoided and an estimated 83 million in healthcare cost savings. Thealgorithm-driven workflows enabled proactive monitoring and intervention, enhancing patient stability, supporting clinicians, and optimizing resource utilization in the home care setting. Conclusion The Nexus Platform demonstrates that AI-enabled, algorithm-driven management of home ventilator patientscan substantially reduce hospitalizations and respiratory infections while generating significant cost savings. By embedding predictive analytics, standardized clinical workflows, and patient engagement tools into HMEoperations, this approach supports proactive, data-driven care and aligns with value-based healthcareinitiatives. Broader adoption of AI-assisted home ventilator management may improve quality of life, clinicaloutcomes, and reduce the healthcare system burden for complex respiratory populations. References: 1. Klingshirn, H. , et al. How to improve the quality of care for people on home mechanical ventilation from the perspective ofhealthcare professionals: a qualitative study. BMC Health Serv Res 21, 774 (2021). https: //doi. org/10. 1186/s12913-021-06743-32. Hayman J, et al. Cost comparison of mechanically ventilated patients across the age span. Respir Care. 2015;60 (9): 1287-1295. This abstract is funded by: none
Landon et al. (Fri,) studied this question.