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Patient Arrivals Forecaster, A Case Study

Patient Arrivals Forecaster, A Case Study

Patient Arrivals Forecaster

Xyonix partnered with a leading healthcare solutions provider to develop an AI-powered patient census forecasting system. This solution leverages time series models to predict patient arrivals up to five days in advance, enabling hospitals to optimize staffing and resource planning. With explainability features like SHAP analysis and intuitive error metrics, the system builds trust and provides teams with data-driven insights. The project included robust data preparation, fusing multiple data sources while adhering to strict HIPAA compliance, and hands-on guidance in product design and organizational integration. [Read more]

Virtual Patient Intake Specialist for Online Health Services, A Case Study

Virtual Patient Intake Specialist for Online Health Services, A Case Study

Virtual Intake Specialist

We helped a company build a powerful virtual intake specialist that automatically communicates with patients to route them to the best care for their condition. Our AI system listens to patients and asks them just the right follow up questions, much like a traditional intake nurse might, to help schedule the best care option. [Read more]

AI Backed Surgery, A Case Study

AI Backed Surgery, A Case Study

AI Backed Surgery

For a fortune 500 company, our Xyonix built models are in production today constantly analyzing thousands of recent in body surgery videos and reviews across many hundreds or thousands of surgery rooms around the world. Our models watch never ending streams of surgery videos, and read countless reviews. Our models power dashboards that help surgeons improve. [Read more]

Manufacturing Engagement Optimizer, A Case Study

Manufacturing Engagement Optimizer, A Case Study

Manufacturing Engagement Optimizer

We worked closely with a large industrial parts marketplace provider to help them better understand their current customers and partners. We developed AI models trained on their customer and partner site usage, profile and external attribute data to analyze engagement. We were able to successfully forecast which customers and partners were destined for high and low engagement. Perhaps more importantly, our models were able to explain why customers were likely to be high or low engagers. These deep, data driven insights, when used to power a living intelligence dashboard, are able to direct sales and marketing activities so they are more efficient and impactful.