Standardized Health Spending Analytic Definitions and Methods: Cross-State Collaboration Leads to Stronger Results

States are becoming increasingly sophisticated in measuring and analyzing health care spending. They have established efficient processes to collect and analyze data collected directly from health insurers, an important first step in understanding health care costs. States have also built the capacity to examine health care cost drivers and study individual components of spending so they can understand what fuels health care cost growth. To date, some states have borrowed methods for doing so from each other; however, substantive differences remain in states’ approaches.

Robust consensus definitions for health care service categories like primary care or medical pharmacy, and methodologies for tracking spending in these areas, can strengthen analyses and facilitate cross-state comparisons. They also have the potential to strengthen the national discussion around health care cost drivers and cost growth targets, and could result in greater attention to and uptake of state health care transformation strategies including cost growth mitigation and priorities for increased investment to improve population health.

In 2024 and 2025, Bailit Health convened two work groups to establish consensus definitions and methodologies for health care spending analyses with support from the Peterson-Milbank Program for Sustainable Health Care Costs. The Work Group on Health Care Cost Driver Definitions worked to create consensus service category definitions (i.e., which services, providers, and care settings are included within each service category) and standard approaches to methodological adjustments for cost driver analyses with step by step instructions to allow states to replicate these methods. The Work Group on Health Care Cost Growth Target Measurement and Analytics developed standardized approaches for measuring claims-based and non-claims spending, as well as methodologies and adjustments specific to analyses of cost growth target data. The work groups benefited from active participation from 13 states, several analytic vendors, and outside subject matter experts. For both groups, Bailit Health documented the finalized definitions and methodologies (Consensus Administrative Specifications for Health Care Cost Driver Analyses; Consensus Administrative Specifications for Health Care Cost Growth Target Programs), which include clear instructions on how to extract data based on these definitions, relevant code lists with which to extract the data (as applicable), and step-by-step instructions for carrying out the recommended analyses.

The topic of medical pharmacy highlights the challenges of inconsistent state definitions and methodologies. States have identified medical pharmacy — sometimes called “physician-administered drugs” — as an area of growing spending. However, spending on medical pharmacy is notoriously difficult to capture because it is billed under multiple claim types. Presently, states use different combinations of claim types and codes to identify medical pharmacy spending. Additionally, some states prioritize capturing just those medications that represent significant spending, rather than the totality of medical pharmacy spending. These inconsistencies make it challenging for states to compare their medical pharmacy spending to that of their peers.

The standardized definition of medical pharmacy and accompanying code lists developed by the Work Group on Health Care Cost Driver Definitions solve this problem. Providing states with a defined code list and analytic methodology equips them to measure spending using the same parameters. Additionally, working from the same code list enables states to calculate and compare utilization and price per unit with confidence that they are analyzing spending for the same set of medications. Standardization also promotes transparency and accountability by ensuring that states’ reported data reflects these common service categories and definitions. Ultimately, a consistent approach lays the groundwork for states to better understand this category of spending and can help them identify opportunities to slow spending growth in this area.

Primary care is another topic for which a standardized definition is of great value. A growing number of states — including a subset of cost growth target states — have identified increasing primary care spending as a policy priority, recognizing that primary care is a critical foundation for the health care system. Currently, many states use state-specific primary care definitions. This discrepancy in how states define primary care makes it challenging for states to compare their spending levels to peer states and to identify high performers that may have best practices to share. A common cross-state definition enables states to measure spending on the same parameters, and promotes transparency and accountability by ensuring that states’ reported data reflects these common service categories and definitions. Ultimately, a consistent approach lays the groundwork for increased investment in primary care and improvement in overall health system performance.

In addition to developing a common method for identifying medical pharmacy and a primary care definition, each work group discussed a variety of other topics; their recommended definitions, measurement considerations, and analytic approaches are documented for use by state analysts.

Achieving shared definitions across states is no small feat. States benefit from increased credibility of their analytic methods and analyses when they can point to peers who have adopted the same approach. This is especially helpful when states are challenged by providers and other industry players seeking to undermine states’ efforts to slow spending growth.

Some states have begun to implement these standardized definitions in their analyses, while others will need to consult with their technical advisory groups and get buy-in before doing so. Ultimately, states will be able to produce insightful cross-state comparisons, benchmark performance, target areas for additional analysis, and accelerate the dissemination of analytic and policy best practices.