OptMyCare™ provides clinically pre-mapped precision analytics which facilitate the accurate identification of emerging clinical and financial risks. Our patient-specific and evidence-based prescriptive analytics guide “next-best actions,” helping organizations actively mitigate risk.

1.  Descriptive Analytics: OMC’s descriptive analytics provide quality care gap identification, provider performance metrics, patient risk stratification by disease state, patient as well as providers engagement assessments, and SDOH risk assessments.

2.  Predictive Analytics: OMC’s prescriptive analytics calculate risk projections across 55 disease states as well as MCC for the upcoming year, generate cost projections for the upcoming year (total claims costs, patient-level costs, and costs by site and service), identify leakage projections, and track risk progression across chronic conditions for the upcoming year.

• • -Patient Level: Emerging Clinical Risk Data: Multi-Chronic by Disease State (55 Conditions), Claims Risk, Hospitalization Risk, Disease Progression Emerging Financial Risk Data (Upcoming Year Spend): Total Predicted Cost, Predicted Cost by Disease State
  • -Population Level: Emerging Clinical Risk Data: Total Risk Levels, Risk Levels by Disease State Emerging Financial Risk Data: Total Predicted Costs, Total Costs by Disease State, PMPY Total Predicted Spend, PMPY Spend by Disease State

3. Prescriptive Analytics: OMC’s prescriptive analytics offer precise and actionable patient level CDS, cost avoidance recommendations at provider and group level, patient-level tasks and recommendations, and care recommendations to reduce leakage.

• -Patient Level: Actionable Risk Level Stratification (Low, Moderate, High)Actionable Clinical Risks: Patient-Specific Care Recommendations by Disease State Patient as well as Risk Level

Previously recommendations and decisions were made based on inputs largely derived from the judgements of individual case managers and clinical reviewers. These recommendations, not always accurate, did not derive the benefit of lessons from past recommendations and their ultimate outcomes. By leveraging ML, we are able to both capture and scale the collective intelligence of past learning to improve future outcomes.