- What are important goals to consider for a single standard for non-FFS payment data collection?
- What are the key use cases for capturing non-FFS payment data?
- What process should be used to designate whether a non-FFS payment or portion thereof is primary care spending?
- What are relative benefits and drawbacks for each of the data collection decisions?
Primary care spending as a percentage of total health care spending is a relatively new measure that allows end users to assess the degree to which a health system is oriented toward primary care delivery. The Milbank Memorial Fund recently supported research to develop a methodology to calculate primary care spending as a percentage of total health care spending by commercial health plans and to adapt this methodology for use in examining primary care spending in fee-for-service (FFS) Medicare. However, there is concern that calculation of primary care spending and total health care spending using FFS payment data alone underestimates both primary care spending and total health care spending by excluding non-FFS payments. Several states and other multistakeholder entities have begun to collect non-FFS spending data from insurers in their states to measure primary care spending or total health care spending and changes over time.
The authors interviewed states and other multistakeholder entities, as well as payers, about their current capture of non-FFS payment data as an initial effort to inform a clearer discussion of how to develop standardized methods to collect and report these data. Based on the interviews and discussion of findings with a technical expert panel, the researchers summarize current practice and offer preliminary recommendations about considerations to inform future efforts to develop a standardized methodology for collecting non-FFS payment data. The goal is to support more-complete measurement of primary care spending and total health care spending.
- States varied in their approaches to collecting non-FFS payment data. Some states (Vermont and Rhode Island) collected high-level total non-FFS payment data from each payer, and others (Oregon, Colorado, and Massachusetts) collected disaggregated payments at the level of the provider group.
- Discussion with the expert panel revealed varied viewpoints around how to categorize different types of non-FFS payments (e.g., capitation payments, alternative payment model [APM] payments) and how to identify the full range of potential primary care payments. For some use cases, cumulative non-FFS payments from each payer might be sufficient; for others, provider- or patient-level data might be needed.
- States or other entities might have an interest in assessing use cases, such as measurement of APM payments; enabling complete spending estimates for specific health care services; and assessment of the relationship between non-FFS payments to providers and their performance on measures of cost, access, low-value care, and clinical quality for different units of analysis.
- Developing a standard approach that can be used across states will improve the comparability of estimates of total spending for primary care and non-FFS spending across states and among payers and ease the burden placed on payers who operate in multiple states because they can apply the same methods in all states to aggregate and report data. However, compiling and reporting these data could be time-consuming and burdensome for payers, as payers' current processes and systems to administer and track non-FFS payment vary widely.
- Develop a single approach for categorizing types of non-FFS payments.
- Select a common approach for identifying what types of non-FFS payments are considered primary care payments.
- Define a uniform population or frame for data collection on the basis of situs of insurance contracts as is most feasible for payers.
- Work toward disaggregated data reporting by provider organization and patient zip code, as opposed to cumulative payments from each payer.
- Convene key stakeholders and build consensus around seminal features of non-FFS payment data-collection definitions.
- Assess in future work whether different approaches for the collection of non-FFS payment data and primary care spending data meaningfully affect primary care spending as a percentage of total health care spending and other spending metrics of interest to policymakers.
- Understand how measures of primary care spending, with and without inclusion of non-FFS spending, correlate with desired outcomes of care delivery.
- Consider a broad set of potential use as non-FFS payment data-collection standards are developed and formalized.
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