How Predictive Modeling in Healthcare Boosts Patient Care
Unlike prescriptive analytics, which uses data sets to help streamline existing processes and improve operational performance, predictive frameworks use machine learning and artificial intelligence models to discover correlations across disparate data sources and provide actionable recommendations.
To deliver on the potential of predictive analytics, healthcare providers need a combination of tactics and technology.
For Phan, effective deployment starts with a problem statement: Where are providers missing key insight, and what data could help improve patient outcomes? “This should work similarly to the building of a computer model,” says Phan.
This ties into the second pillar of predictive analytics: Advanced ML and AI technologies. By supplying these tools with verified healthcare data, it’s possible to develop reliable and responsive models capable of analyzing incoming data to identify potential patient issues, improve current operations and predict emerging trends.
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