Building health plan databases to risk adjust outcomes and payments

Area of Science:

• Health Services Research
• Health Informatics
• Health Economics

Background:

• Managed care organizations (MCOs) routinely collect extensive data on member health status, utilization, and costs.
• These data are crucial for understanding population health, managing care, and financial risk adjustment.
• However, the quality, consistency, and accessibility of these data can vary significantly across organizations.

Purpose of the Study:

• To identify the types and sources of medical risk and outcomes data collected by MCOs.
• To assess the quality and consistency of these routinely collected data.
• To describe challenges in data collection, pooling, and utilization for research and operational purposes.

Main Methods:

• Synthesis of experiences from two risk-adjustment modeling projects.
• Assembled large volumes of demographic, diagnostic, and expense data from multiple health maintenance organizations (HMOs) over several years.
• Employed three data extraction approaches: health plan staff, study staff access, or contract programmers.

Main Results:

• Successfully collected and integrated comprehensive utilization, morbidity, demographic, and cost data from six large HMOs.
• Data spanned multiple years and covered entire member populations or significant subsets.
• Demonstrated feasibility of assembling large-scale MCO datasets for research.

Conclusions:

• MCO data systems are improving in quality and comprehensiveness over time.
• Automated health plan data systems are valuable resources for health risk and outcomes information.
• These data can effectively benchmark disease management programs and support risk adjustment for payments and outcomes.