Operationalizing Artificial Intelligence and Machine Learning Tools to Make Use of Predictive Analytics
As healthcare and other industries continue to adopt artificial intelligence (AI) solutions built on machine learning (ML), the key challenge is effectively organizing and leveraging the vast amounts of data required to power solutions and create new, more efficient processes.
To truly succeed, enterprises will need to craft data strategies that are understood and embraced by leadership and built into the technology. These solutions will not be based on updates or datasets; they will revolve around effectively organizing and distributing actionable, real-time data across the enterprise. This will position organizations to better serve all stakeholders and enable them to leverage predictive capabilities that benefit payors, providers, and, most importantly, patients.
- How to Bridge the Gap Between Legacy Infrastructure and New, Real-time Capabilities
- Where to Start: Use Case Highlights
- How to Build Dynamic and Efficient Processes
- The Need to Align Key Players and Technology from Concept to Output
- How to Operationalize Data More Efficiently and Intelligently
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