Decision AI for EnterpriseReadmission Avoidance | Patient Readmission Management
Business Problem:
Traditional approaches to managing hospital readmissions tend to be reactive, often driven by retrospective analysis of patient data and clinical outcomes. This can result in missed opportunities to intervene early and prevent readmissions. The complexity increases with multiple healthcare providers and stakeholders involved in discharge planning and follow-up care, leading to inefficiencies and potential oversights. A more proactive, data-driven solution is needed to predict readmission risks and personalize interventions.
Business Solution:
DiwoCatalyst enables readmission avoidance by focusing on the healthcare providers' approach to post-discharge care by enabling real-time, data-driven decisions for high-risk patients. The AI-driven DiwoCatalyst would analyse structured (e.g., demographics, lab results) and unstructured data (e.g., doctor’s notes, discharge summaries) to generate timely, actionable insights that can help healthcare providers optimize patient follow-up plans and preventive care. By implementing a Recommendation First System, this platform would deliver real-time, personalized discharge instructions and follow-up care plans based on predicted readmission risks. The system integrates patient health records, physician notes, and social determinants of health using a Semantic Knowledge Graph. This allows continuous learning and adaptation to patient outcomes, providing predictive insights and business scenarios to avoid readmissions while maintaining patient well-being. It also ensures compliance with healthcare regulations, offering secure and scalable readmission management solutions.
