Post-Care Continuum: Predictive Analytics Platform for Continuous Patient Monitoring After Hospital Discharge

Through the implementation of an effective post-discharge patient monitoring system, we intend to significantly reduce the risk of Cognitive Heart Failure (CHF). This study proposes a highly advanced machine-learning platform for real-time detection and risk assessment of current patient data. More specifically, reinforced learning and supervised learning will be used. We also plan to integrate historical data, providing both healthcare providers and patients with more accurate results. Patients can enter daily health metrics to facilitate trend analysis and capture oncoming health issues. After this input, the patient data will be assessed with a built-in risk assessment algorithm developed by our model. Based on results from this algorithm, healthcare providers will be notified. This patient-centered design facilitates the process of proactive care, enhances the level of communication between patients and their providers, and shortens diagnosis and time required for treatment initiation. Data sources used by the platform to model predictive ability include PhysioNet, OpenNeuro, and OpenfMRI. Rigorous validation through clinical trials shall prove the reliability, effectiveness, and scalability of the platform toward bettering patient outcomes and supporting global healthcare enhancements. Through this continuously adapting model, we hope to successfully bridge the gap between accurate healthcare monitoring and post-discharge care. Clinical Relevance- Currently, healthcare providers are not able to have an easy method of tracking patient conditions post-discharge, especially when it comes to chronic conditions like Cognitive Heart Failure (CHF). This model allows for both providers and patients to have a device that simulates whether or not the patients need to go in for a check-up, based on the machine learning and risk assessment model that we developed. This can significantly improve patient-provider communication.