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Prospective or retrospective ACO attribution matters for seriously ill patients
Brystana G Kaufman1, William K Bleser, Robert Saunders
1Margolis Center for Health Policy, Duke University, 230 Science Dr, Durham, NC 27705.
Objectives:
Since 2019, the Medicare Shared Savings Program (MSSP) has allowed accountable care organizations (ACOs) to choose either retrospectively or prospectively attributed ACO populations. To understand how ACOs' choice of attribution method affects incentives for care among seriously ill Medicare beneficiaries, this study compares beneficiary characteristics and Medicare per capita expenditures between prospective and retrospective ACO populations.
Study Design:
This retrospective, cross-sectional analysis describes survival, patient characteristics, and Medicare spending for Medicare fee-for-service beneficiaries identified with serious illness (n = 1,600,629) using 100% Medicare Master Beneficiary Summary and MSSP beneficiary files (2014-2016).
Methods:
We used generalized linear models with ACO and year fixed effects to estimate the average within-ACO difference between potential retrospective and prospective ACO populations.
Results:
Dying in the first 90 days of the performance year was associated with reduced odds of retrospective ACO attribution (odds ratio [OR], 0.24; 95% CI, 0.24-0.25) relative to beneficiaries surviving 270 days or longer. Similarly, hospice use was associated with reduced odds of retrospective assignment (OR, 0.80; 95% CI, 0.79-0.80). Among ACOs that did not achieve shared savings, average per capita Medicare expenditures (after truncation) were $2459 (95% CI, $2192-$2725) higher for prospective vs retrospective ACO populations. The difference was $834 (95% CI, $402-$1266) greater per capita among ACOs that achieved shared savings.
Conclusions:
The difference in survival and spending for ACO populations captured by prospective vs retrospective attribution methods means that ACOs may need to employ different care management strategies to improve performance depending on their attribution method.
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