You will be amazed by the detailed pop health data you can identify, and then apply, through medical, pharmaceutical, and biometric benefits data. Gathered through employer health benefits, state medical assistance benefits programs, or other sources, claims data can be used for targeted analytics, such as:
- Correlate whether a current low-risk member is projected as a future high risk member
- Correlate whether a current low-cost claimant is projected to trigger stop loss spec threshold as a high-cost claimant
- Isolate members by primary chronic and co-morbid conditions, including all related medical and pharma incurred costs by site of care
- Trigger early-warning alert for members with a new diagnosis typically associated with a subsequent Specialty drug prescription
The purpose of targeted analytics such as these is for the Pop Health Provider to then apply these insights into targeted solutions which help support the individual member, their employer, their insurer, and systematically drive tangible improvement in the population’s health.
About the Presenter:
Ned Laubacher has spearheaded growth strategies and financial turnarounds throughout his healthcare career as both a CXO and strategic advisor to the C-team. His subject matter expertise in business analytics and financial risk strategies are the core of his consulting services through Health Spectrum Advisors. Ned’s current focus is a rapid bacterial diagnostic device capable of revolutionizing sepsis management, antimicrobial stewardship, and antibiotic therapy protocols across the globe. Ned holds dual masters’ degrees in business administration and public health as well as a bachelors’ degree in Economics from the University of California, Los Angeles.