Ejyle Risk Adjustment solution brings in a combination of rule-driven, diagnostic, predictive and machine learning algorithms that address the numerous challenges facing health insurance providers. It is the ultimate AI-driven risk adjustment solution to prevent revenue leakage, enhance efficiency and reduce cost.
- FULLY AI-POWERED RAF SOLUTION
A true AI-based Risk Adjustment Factor solution to adjust risk and prevent revenue leakage.
- SIMPLE AND ACCURATE SCORE ANALYTICS
Get a clear picture of risk stratification of the population based on known health conditions and potential predicted health conditions
- CHASE STRATEGY AND CHART REVIEW ANALYSIS
Clear chase prioritizations driven by potential revenue impact, probability of potentially missed conditions to convert to actual misses, recommended claims and providers to chase. Gap closure tracking based on processing Chart Reviews.
- COMPREHENSIVE RAF GAP ANALYSIS
Identify gaps up-front with multiple algorithms and multiple sources aiding the gap identification. Identification of suspect members and missed conditions
- MULTIPLE DESCRIPTIVE, PREDICTIVE AND FORECASTING MODELS
Numerous drilldowns by members, health conditions, providers, risk levels, co-occurrence including Financial Forecasting and Target Revenue Planning, Health Conditions Prediction for future, Forecast of Provider level Claim volumes, member visits, Utilization Analysis, Planning for Wellness Campaigns for Co-occuring Health Conditions.
Over the last few years the US government has come up with several programs to ensure the affordability and accessibility of healthcare for the population. Some of the well known programs are the Medicare, the Medicaid and the ACA Marketplace.
Considering that these plans (which are administered by Private health plans on behalf of the government), are funded typically by a Global Capitated Model, one of the greatest challenges facing such plans is the problem of Revenue Leakage which typically occurs due to certain health conditions which get missed while being reported to CMS (as part of submitted encounters).
The dire need is for a platform that can
- Calculate Risk Scores Accurately
- Identify Potential Gaps in Health Conditions for specific members with potential revenue impact
- Provide a Targeted Chase List for Chart Reviews to plug gaps and enhance revenues
- Ability to Forecast Potential Revenue Targets for future year
RISK SCORE CALCULATION
Ability to identify high risk members/conditions
Identify suspect members/Gaps in coding & revenue impact validated by ML based probability models
Analyse the impact of chart reviews on RAF Score
Financial forecasting of RAF and revenue for future year
Ability to view Cost/Utilisation against revenues
ML DRIVEN PREDICT ALGORITHMS
To predict potential health conditions
- Identification of high risk members that need specific proactive care and wellness campaigns
- Identification of high utilization members where avoidable services can be focused upon
- Inputs for Population Health Management Programs
- Provider engagement programs to enhance awareness on coding related issues