Risk Adjustment Analytics & AI Risk Solutions

Payers need more insight into member risk when underwriting commercial products, developing pricing for government markets, and managing risk-adjusted populations. Identifying missing Hierarchical Condition Categories (HCCs) with additional data-driven clinical insights will yield a better ROI.

Factoring Prognos’ laboratory-based solutions into the risk-modeling equation solves for a more complete, accurate view of the member. Prognos gives historic and ongoing clinical insights into member risk profiles, including newly-enrolled members. With the power of thousands of algorithms to interpret and identify conditions, Prognos AI Risk Solutions are a force multiplier to help payers better understand and manage risk.

Risk Adjustment Analytics

Payers and providers need to have an effective approach to updating risk accuracy; which typically relies on analyzing medical claims information. Prognos can measurably improve the ability to identify and manage health risks with clinical information. Prognos uses cross-payer lab data, 24-month historic member lab results from existing and prior health plans, and current lab data to identify member suspect risk and hierarchical condition categories (HCCs). Since lab data maps directly to 79 CMS HCCs for Medicare Advantage, 129 HHS HCCs for ACA, and the various state-specific managed Medicaid risk-adjustment models,  you gain a more complete, efficient, and accurate risk adjustment program.

With Prognos’s risk adjustment solution, payers can identify 12-15% more risk (HCCs ) over and beyond methods based on claims data alone.

Seamless Integration into Risk Adjustment Programs

The Prognos Risk Alerts solution integrates seamlessly with existing workflows and programs, using analysis of lab-based testing data to identify new or previously undetected risk, for both retrospective and prospective review. Payers incorporate the new data-driven insights identified by enriched clinical lab data into workflows, and use them to prioritize chart retrievals and in-home visits to confirm HCCs. Additionally, payers are able to act earlier when using clinical lab-testing data to identify HCCs compared with using medical claims alone. This results in faster reimbursement for prospective risk adjustment.

Iceberg Graphic
Beware of risk you cannot see; Prognos brings a more holistic approach to managing risk.
Lower operational costs associated with chart retrievals, home visits and unnecessary member engagement. 
  • Identify chronic disease in a more timely manner and reduce false positives for more targeted and effective member outreach or chart retrieval and review
  • Equip operations teams with access to more complete member profiles to inform their efforts

Enhance Risk Scoring for New Groups

Payers’ underwriting teams traditionally do not have access to clinical lab test information for pricing group health insurance plans for new employer groups. Instead, they rely on limited demographics and actuarial tables, as well as self-reported data from surveys, if available. As a result, you may be setting prices without claims experience, which can affect net profit and cause mis-matched risk to premium. Reinsurers and stop-loss insurance providers face similar challenges. 

Working with Prognos, payers offering employer group health insurance plans can better predict risk with AI-driven models that produce a predictive group risk score. Leveraging our core registry asset comprised of billions of clinical records and millions of unique patients, the Prognos Risk Score solution gives insight that payers need to accurately price group health plans without prior medical claim history.

After a payer provides a de-identified employer census, it is matched to our de-identified clinical registry of lab-testing results for de-identified patients. Prognos runs unique and highly-trained models to produce the most accurate underwriting risk score in the industry.

The underwriting team gains immediate benefits to:

  • Better align the original premium price with actual risk
  • Enjoy a longer-term profitable customer relationship 
  • Benefit from more accurate pricing to avoid undetected group risk  

Actionable Risk Prediction for Renewal Groups

At mid-year, payers are often required to submit group pricing requests with incomplete claims. Prognos can help by supplementing your information with AI-driven clinical lab results to develop more-accurate pricing. By combining three years of historical claims with cross-payer clinical lab insights we can predict a 12-month cost at the individual insured level. The results are de-identified and rolled up to the group level to provide an aggregated de-identified risk prediction. Prognos delivers a risk score prediction for each group and per-member-per-month (PMPM) cost. 

Our solutions support your quantification of:

  • Mean Squared Error/ Mean Absolute Error
  • Double lift curves
  • R2
  • Gini Statistic
  • Outlier Analysis

More Informed Risk Triage

Understanding the potential risk and health of members informs care management, population health, risk adjustment, and case-management initiatives. Risk stratification based on member-level risk scores enables you to prioritize care-management resources to maximize impact. For all lines of business, Prognos uses historic, member-level clinical lab data even before members are enrolled with the payer — to more accurately predict the 12-month cost of each member.

With this insight, payers can identify those members who would most benefit from care-management resources to reduce the need for more costly medical interventions related to:

  • Hospital stays
  • Emergency room visits
  • Worsening of chronic conditions

Medical Risk Assessment for New Enrollees

For new enrollees, payers do not have a complete view of members’ health conditions and clinical history. Traditional member health risk assessments have not been very accurate. With a clinical understanding of members’ conditions on the first day of enrollment, you’ll have a much clearer picture of their health including disease severity, and progression.