Identifying and tracking health condition prevalence, severity and progression among member populations is essential for effective care management and initiatives related to quality management, population health, and SDOH. Our team of experts use clinical and AI-based algorithms built on evidence-based clinical guidelines to identify, stratify, prioritize, and monitor members for payer care-management programs. Additionally, you can utilize clinical lab insights to measure the effectiveness of their care and quality interventions and programs.
Rapidly Improve the following for your Care Management Programs:
Disease identification and monitoring
Medical cost management
Chronic care outreach
Rising cost predictions
Disease trend evaluation
Enhance Disease Identification and Monitoring
For improved success with care-management and population-health programs, payers can leverage additional insight from clinical lab-testing data to identify and monitor member conditions earlier and more accurately. Now you can proactively identifying members with worsening conditions and complications versus relying solely on latent and clinically unspecific information found in medical and pharmacy claims.
In physician offices, 70% of guidelines are aimed at establishing a diagnosis or managing disease require laboratory tests.
The clinical lab-testing values drive refinement of your algorithms for timely disease identification.
Better Balance Medical Costs with Care Management
Payers understand the importance of balancing overall costs while ensuring members get appropriate and impactful care. Our solutions are designed to measure, track, and respond to medical cost trends. These include:
Improved cost efficiencies and outcomes
Optimize Targeted and Timed Chronic Care Outreach
As part of care management programs, members receive personalized educational information, calls from care managers, and online resources. Prognos makes that care management outreach more targeted, accurate and personalized. With clinical lab values, payers can better:
Identify members with the highest opportunity for impact
Be more clinically specific and proactive with timely interventions
Align the approaches aimed at maximizing member engagement and minimizing provider abrasion
Better Predict Rising Cost with Clinical Data Assets
Clinical lab data is valuable for predicting costs as lab values change across the membership. Research shows that 65% of high-cost members in the current year were not considered to be high-cost patients last year. The ability to identify, predict, and create impactful interventions to target these members is critical for managing medical costs. With powerful and precise insights based on advanced machine learning analytics, Prognos has built powerful models that predict rising costs associated with chronic disease progression and emerging potential clinical issues that require attention.
Payers benefit from our insights for predicting rising costs by more precisely:
Planning member interventions
Measuring the effectiveness of population-health programs
Investing in programs that have the potential to bend the cost curve
Disease Trend Evaluation
Retrospective evaluation of a population’s disease trends allows health plans to better prioritize members and initiatives, including projects that are aimed at addressing the SDOH. Having deep insight into past trends gives payers a vision for how to manage future programs for members, population management, government programs, and underwriting.