The top three reason why most AI-Driven, Predictive Analytics projects fail in value-based care systems
Healthcare is going through a major transformation where providers and payers are focusing on value-based care with pay-for-performance and risk-based contracts. This transformation has yielded innovation and adoption of technologies that have proven effective in other industries. Why are AI-driven predictive analytics not being adopted as aggressively in value-based care like it has in the financial, consumer and technology industries?
Attendees will learn the common challenges faced by Value-Based Care Systems as they attempt to use AI-driven predictive analytics and deliver projects at affordable costs, with quick turnaround times, and high success rate. Attendees will understand the importance of the following:
- Leveraging an AI-driven, ETL process to create a granular 360-degree member record
- Developing a multi-dimensional OLAP model to identify “Mover” patterns to tease out key input variables and features for AI-modeling
- Identifying the right ensemble of models to use based on the specific population health, care management, quality optimization and risk management problems in value-based care models