6:28 AM
Over two years ago, I summarized a research paper from Karen E. Joynt and Ashish K. Jha at Brigham and Women's Hospital that suggested that a one-size-fits-all readmission rate penalty policy would have the unintended consequence of harming safety net hospitals. They said:
Conclusions—Given that many poor-performing hospitals also have fewer resources, they may suffer disproportionately from financial penalties for high readmission rates. As we seek to improve care for patients with heart failure, we should ensure that penalties for poor performance do not worsen disparities in quality of care. (Circ Cardiovasc Qual Outcomes. 2011;4:53-59.)
To the best of my knowledge, these concerns were never addressed by the federal government, or by state governments applying similar standards through Medicaid.
Now comes this thoughtful argument by Richard Fuller at 3M Clinical and Economic Research in an article entitled: "Payment policy bias against high Disproportionate Share Hospitals (DSH)."An excerpt:
The bias against high Disproportionate Share Hospitals (DSH) apparent in the CMS payment policy is particularly concerning. This comes at a time when Medicare DSH payments are being directly adjusted as part of ongoing reforms and Medicaid DSH payments are being cut nationally in accordance with the Affordable Care Act. This heightened sensitivity brings urgency to the discussion of how to correct for the perceived SES [socioeconomic status] bias, specifically whether measures reflecting SES should be included in the current risk-adjustment formula. If additional measures reflecting SES are to be considered, it will be important to separate the effects that may be attributed to generally lower performance in low income areas from those attributable to the complexities of treating a challenging population. In other words, can the risk adjustment method help us distinguish whether hospitals that care for poorer patients perform worse because they don’t do a good job, or because their patients are more difficult to care for?
His summary:
Risk adjustment for provider rates that account for SES should include additional clinical and demographic factors that can be shown to improve predictive performance. In addition, those additional factors should be incorporated into a model that is based on continuous variables rather than a categorical model based on peer groups.
Conclusions—Given that many poor-performing hospitals also have fewer resources, they may suffer disproportionately from financial penalties for high readmission rates. As we seek to improve care for patients with heart failure, we should ensure that penalties for poor performance do not worsen disparities in quality of care. (Circ Cardiovasc Qual Outcomes. 2011;4:53-59.)
To the best of my knowledge, these concerns were never addressed by the federal government, or by state governments applying similar standards through Medicaid.
Now comes this thoughtful argument by Richard Fuller at 3M Clinical and Economic Research in an article entitled: "Payment policy bias against high Disproportionate Share Hospitals (DSH)."An excerpt:
The bias against high Disproportionate Share Hospitals (DSH) apparent in the CMS payment policy is particularly concerning. This comes at a time when Medicare DSH payments are being directly adjusted as part of ongoing reforms and Medicaid DSH payments are being cut nationally in accordance with the Affordable Care Act. This heightened sensitivity brings urgency to the discussion of how to correct for the perceived SES [socioeconomic status] bias, specifically whether measures reflecting SES should be included in the current risk-adjustment formula. If additional measures reflecting SES are to be considered, it will be important to separate the effects that may be attributed to generally lower performance in low income areas from those attributable to the complexities of treating a challenging population. In other words, can the risk adjustment method help us distinguish whether hospitals that care for poorer patients perform worse because they don’t do a good job, or because their patients are more difficult to care for?
His summary:
Risk adjustment for provider rates that account for SES should include additional clinical and demographic factors that can be shown to improve predictive performance. In addition, those additional factors should be incorporated into a model that is based on continuous variables rather than a categorical model based on peer groups.
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