AI-Based Healthcare Cost Prediction Delivering Superior Predictive Accuracy
Machine Learning based cost prediction models developed using over 20 million member claim records and other clinical data.
Built with claims data including cost, utilization, diagnosis, lab, pharmacy, and patient characteristics, Lumiata Health AI Cost Prediction has demonstrated better accuracy than conventional methods in predicting healthcare cost for individuals, high cost patients, and groups.
Health plans, self-funded employers and other risk-bearing organizations will benefit from the better predictive precision Lumiata provides to
Optimize underwriting for large group renewal
Effectively manage healthcare spend
Efficiently allocate resources
Help improve health for their population
AI and Machine Learning Advancement is Transforming Healthcare with Big Data
For health plans Accurate cost prediction would enhance plan design, network design, underwriting and membership growth. It would further enable targeted care management to optimize clinical resource allocation, improve clinical outcome and patient satisfaction.
For self-funded employers Improved cost prediction would help the organizations better understand their healthcare risk exposure, develop programs to promote healthy workforce, and improve workforce productivity.
For individuals Understanding their likely expenditures for next year can help them select health insurance plan with appropriate premiums and deductibles for their specific needs.
Lumiata Health AI Cost Prediction Provides:
Individual Cost Prediction Predict healthcare cost for individuals for customer specified time periods
Group Cost Prediction Provide insured account/employer group level prediction for the next renewal period
High Cost Claimant Identification Identify persistent high cost and newly high cost claimants and segment likely high cost claimants with predicted cost
Lumiata Disease Code Provides disease groupers based on diagnosis, procedure, pharmacy, and lab data Disease Onset Prediction Predicts member disease onset risk with timeline