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August 02, 2017 01:00 AM

Physicians with high-risk patients struggle under value-based pay model

Maria Castellucci
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    Physicians who serve low-income patients with complex conditions are more vulnerable to financial losses in value-based payment models, according to a new study that found these providers, many of them safety-net providers, didn't have the technological infrastructure to report the necessary data.

    The report, published Tuesday in JAMA, found that physician practices with a large proportion of high-risk patients were likely to receive a penalty in the CMS' Physician Value-based Payment Modifier program because they reported low quality outcomes and higher costs.

    "It is possible that physician practices that care for these high-risk populations will fair poorly in pay-for-performance programs," wrote Dr. Lena Chen, an assistant professor medicine at the University of Michigan, along with her co-authors.

    The study analyzed 2015 payment data from the CMS' Physician Value-based Payment Modifier program made to 899 physician practices who treat 5.2 million Medicare beneficiaries. The mandatory pay-for-performance program penalized or rewarded physicians based on their quality outcomes and cost of care. The program was a precursor to and was replaced by the Medicare Access and CHIP Reauthorization Act (MACRA).

    To evaluate the level of patient risk among practices, the authors used the Hierarchical Condition Category, a risk-coding model that adjusts for different demographics and conditions and whether patients were dually eligible for Medicare and Medicaid.

    The study found that practices categorized as high-risk were more likely to receive a penalty compared to low-risk practices because they didn't provide the necessary performance data for the program during its first year. About 45.9% of high medical and social risk practices were penalized while only 20.8% of low-risk practices were penalized for not reporting the data.

    In total, only 122 physician practices reported the data necessary for performance payments in the first year of the CMS program. The biggest hurdle to reporting the data was likely lack of technical support such as electronic health records. Only about 8% of high medical and social risk practices achieved Stage 1 meaningful use in 2015.

    But even if more of the high-risk practices had reported the necessary performance data, they likely would have suffered a penalty based on their low quality and high cost scores, the authors said. In analyses where performance-based bonuses and penalties were applied to all practices, 13.1% of practices with high medical and social risks would have received a penalty versus 3.7% of low-risk practices.

    High-risk practices serve patients who face challenges with transportation, food and housing, the authors note. They also acknowledged that quality of treatment could just be lower at high-risk practices. "Fewer resources may also make it difficult for practices who serve these patients to attract qualified clinicians," they said.

    A better understanding of the disparities between high- and low-risk practices will become increasingly important under MACRA and its quality reporting system, the Merit-based Incentive Payment System, or MIPS, the authors wrote.

    The MIPS track, which began this year, reimburses doctors based on their performance in four performance categories: quality, resource use, clinical practice improvement and health information technology.

    Practices that serve a high population of socially and medically complex patients could "fare poorly" under the MIPS track based on these reporting requirements, the authors said.

    They suggest development of measures that address health equity will help ensure fair comparisons between provider performance. There have been some efforts to do this. For example, the National Quality Forum released a report last month that evaluated the feasibility of measuring social risk factors.

    Click here to see our MACRA guide and follow our on-going coverage.

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