Methodological Considerations and Best Practices in Multi-Payer PCMH Evaluations
Evaluations of PCMHs or transformation initiatives often include analyses of multi-payer claims datasets, now that these databases are more readily available. The use of these datasets provides new opportunities and new challenges for funders and evaluators. Evaluation methods should adapt to these datasets by addressing the varying populations included in them and by attempting to identify the mechanisms by which costs are or are not affected by the initiative evaluated.
PCMHs can affect health care costs by (1) changing the care processes such that the needed or selected service mix is different for patients served, or (2) by simply steering consumers to lower cost providers with no changes in the needed service mix. These two mechanisms for driving costs are conceptually different and evaluators conducting cost analyses on health care interventions may want to differentiate between them.
Adjusting for prices or payment rates is also important for evaluating health care transformation initiatives such as PCMHs because price variation can affect the results of evaluations or the interpretation thereof. The amounts paid for identical or similar services can vary widely across payers and providers. Medicaid, Medicare, and private insurers pay different amounts for the same services, and like services cost more at academic medical centers than at community hospitals. Price-adjustment standardizes the amounts paid in health care claims to reduce or eliminate the effect of differential prices or payments across health plan contracts, different payers, and different places of service for like medical procedures and treatments. When assessing health care transformation with multiple payers and populations, price adjustment may become just as widely used and essential as risk-adjustment.
This paper discusses our application of the HealthPartners Total Cost of Care methodology to a multi-payer health care claims database constructed to assess Rhode Island’s PCMHs supported by its Care Transformation Collaborative (RI-CTC). We calculated price-adjusted health care expenditures using the amounts paid on the claims to determine whether the effects of medical homes on costs would be different when the cost data were adjusted to account for differential prices and payment rates. Other methodological consideration are also discussed, including the importance of assessing cost components rather than total costs, options for patient assignment, and handling differential observation periods among those in the dataset.