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Distributing $800 billion: an early assessment of Medicare Part D risk adjustment.

ABSTRACT: The viability and stability of the Medicare Part D prescription drug program depend on accurate risk-adjusted payments. The current approach, prescription drug hierarchical condition categories (RxHCCs), uses diagnosis and demographic information to predict future drug costs. We evaluated the performance of multiple approaches for predicting 2006 Part D drug costs and plan liability. RxHCCs explain 12 percent of the variation in actual drug costs, overpredict costs for beneficiaries with low actual costs, and underpredict costs for beneficiaries with high actual costs. Combining RxHCCs with individual-level information on prior-year drug use greatly improves performance and decreases incentives for plans to select against bad risks.

SUBMITTER: Hsu J

PROVIDER: S-EPMC2743923 | biostudies-literature | 2009 Jan-Feb

REPOSITORIES: biostudies-literature

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Distributing $800 billion: an early assessment of Medicare Part D risk adjustment.

Hsu John J Huang Jie J Fung Vicki V Price Mary M Brand Richard R Hui Rita R Fireman Bruce B Dow William W Bertko John J Newhouse Joseph P JP

Health affairs (Project Hope) 20090101 1

The viability and stability of the Medicare Part D prescription drug program depend on accurate risk-adjusted payments. The current approach, prescription drug hierarchical condition categories (RxHCCs), uses diagnosis and demographic information to predict future drug costs. We evaluated the performance of multiple approaches for predicting 2006 Part D drug costs and plan liability. RxHCCs explain 12 percent of the variation in actual drug costs, overpredict costs for beneficiaries with low act ...[more]

PMID: 19124873

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Project description:ImportancePresent-on-admission (POA) indicators in administrative claims data allow researchers to distinguish between preexisting conditions and those acquired during a hospital stay. The impact of adding POA information to claims-based measures of hospital quality has not yet been investigated to better understand patient underlying risk factors in the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision setting.ObjectiveTo assess POA indicator use on Medicare claims and to assess the hospital- and patient-level outcomes associated with incorporating POA indicators in identifying risk factors for publicly reported outcome measures used by the Centers for Medicare & Medicaid Services (CMS).Design, setting, and participantsThis comparative effectiveness study used national CMS claims data between July 1, 2015, and June 30, 2018. Six hospital quality measures assessing readmission and mortality outcomes were modified to include POA indicators in risk adjustment models. The models using POA were then compared with models using the existing complications-of-care algorithm to evaluate changes in risk model performance. Patient claims data were included for all Medicare fee-for-service and Veterans Administration beneficiaries aged 65 years or older with inpatient hospitalizations for acute myocardial infarction, heart failure, or pneumonia within the measurement period. Data were analyzed between September 2019 and March 2020.Main outcomes and measuresChanges in patient-level (C statistics) and hospital-level (quintile shifts in risk-standardized outcome rates) model performance after including POA indicators in risk adjustment.ResultsData from a total of 6 027 988 index admissions were included for analysis, ranging from 491 366 admissions (269 209 [54.8%] men; mean [SD] age, 78.2 [8.3] years) for the acute myocardial infarction mortality outcome measure to 1 395 870 admissions (677 158 [48.5%] men; mean [SD] age, 80.3 [8.7] years) for the pneumonia readmission measure. Use of POA indicators was associated with improvements in risk adjustment model performance, particularly for mortality measures (eg, the C statistic increased from 0.728 [95% CI, 0.726-0.730] to 0.774 [95% CI, 0.773-0.776] when incorporating POA indicators into the acute myocardial infarction mortality measure).Conclusions and relevanceThe findings of this quality improvement study suggest that leveraging POA indicators in the risk adjustment methodology for hospital quality outcome measures may help to more fully capture patients' risk factors and improve overall model performance. Incorporating POA indicators does not require extra effort on the part of hospitals and would be easy to implement in publicly reported quality outcome measures.

Dataset Information

Distributing $800 billion: an early assessment of Medicare Part D risk adjustment.

Publications

Distributing $800 billion: an early assessment of Medicare Part D risk adjustment.

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