Breaking the Rules: Replacing Rules-Based Risk Stratification with AI
Listen to the webinar to learn how CareATC partnered with ClosedLoop to replace rules-based risk stratification with machine learning, improving accuracy by 75%.
CareATC is a leading population health management company that empowers employers to inspire healthier, happier employees and reduces their healthcare spend. Starting with being the first in its sector to implement rules-based predictive analytics for member costs and health risks, CareATC has continually pursued innovation in health services and technology.
To further improve predictive accuracy and the efficiency of its outreach programs, CareATC decided to explore artificial intelligence/machine learning (AI/ML) as an alternative to rules-based risk stratification.
CareATC selected ClosedLoop’s AI/ML solution based on its healthcare content and expertise, ability to incorporate social determinants of health (SDoH) from public datasets, and history of fast time to value. With ClosedLoop, CareATC built AI/ML models to predict individuals’ total cost of care and risk of having an unplanned hospital admission over the next 6 months.
Hear from Phil Bruns, Chief Technology Officer at CareATC, and Carol McCall, Chief Health Analytics Officer at ClosedLoop in our webinar recording. Hear how an AI/ML approach enabled CareATC to:
- Incorporate social determinants of health (SDoH) into predictions of cost and risk
- Predict costs more accurately than a commercial rules-based system in 75% of individuals
- Identify 30% of all unplanned hospital admissions in the riskiest 5% of individuals