Healthcare payer proof of concept: Enabling value-based care analytics & AI applications

POC Scenario & Goals

Meaningful integration of member data for a population that is clinically diagnosed with diabetes to control costs and improve quality of care.

• Deploy a platform that can handle complexity of health data, seamlessly collect, move, aggregate and meaningfully integrate data.
• 800+ discrete fields for patient level clinical data set; 20-30 fields for financial transactions.

POC Overview Leverage

HULFT to build a limited purpose integrated environment from disparate data sources and disconnected systems.

• Claims Processing System
• PBM system/ Prescription Data
• Claims payment
• Provider directory
• Provider contract system
• Eligibility Verification System
• Medical case management
• Referral management system
• EHR systems in provider networks
• Call center management
• EMPI

POC Outcome

Identify care gaps and generate select reports:

• Who did not have a retinal or dilated eye exam
• Who did not have a nephropathy test
• Who did not have an HbA1c test
• With no blood pressure record

Identify member leakage due to out-of-network referrals, generate select reports:

• Percentage of out-of-network claims by zip codes
• Percentage of distribution of cause of the leakage (ie, seeking second opinion, lack of cost transparency, appointment scheduling frustrations, communication breakdowns)

Go-Forward Engagement

HULFT offers a low-code/no-code integration platform that enables payers to:

• Engage members
• Optimize risk adjustment and quality
• Accelerate speed to market
• Prevent fraud
• Establish environment for analytics and AI applications
• Uniquely suitable to feed PHM applications
• Can build layers of bucket, payers can apply ML and AI tools to develop models for multiple analysis: More precise phenotyping of member populations; predictive models to identify patients at various levels of risk; models for unsupervised learning to generate hypotheses