Machine learning (ML) and Artificial intelligence (AI) are being extensively used in healthcare for health risk predictions. SquareML exploits the advantage of AI/ML in risk predictions and stratifications. The integrated three-tier platform of SquareML is comprised of an analytics layer (tier-1), an embedded Machine learning (ML) and Artificial intelligence (AI) layer for health risk stratifications for both the present and future risks and send alerts and notifications (tier-2), and an app-based health information management system (tier-3). From the EMR, multimodal patient data (structured or unstructured) is pushed into the SquareML data lake, where the data are wrangled and processed for various types of analytics, e.g., 360 patient journeys, patient snapshots, and predictive modeling, e.g., health risk stratifications to assist doctors, nurses, care managers, and administrators to take proactive actions to reduce the risk and the cost.
The presentation focuses on predicting a 30-day post-discharge unplanned readmission (PUDR) risks in the geriatric healthcare as a use case with the SquareML’s predictive model. The elderly population is growing across the globe, steadily. It is presently 10% and expected to reach 22% by 2050 costing around 1 Billion USD globally and expected to increase by 40% by 2030. They are the ‘high-need high-cost’ population suffering from multiple morbidities and comorbidities and require continuous healthcare monitoring and support. Elderly PDUR costs USD 20 Billion annually and is a recursive process.
A growing elderly population needs real-time healthcare support as many are bedridden and on IoTenabled wearable devices and RPM systems. The healthcare workforce struggles to optimize care due to staff shortages hindering high-quality care and needs technology support to bridge the care gaps. SquareML helps bridge the gap in a customized manner and helps save lives and money.