Improving Medicare's Hospital Compare Mortality Model.
Ontology highlight
ABSTRACT: Objective
To improve the predictions provided by Medicare's Hospital Compare (HC) to facilitate better informed decisions regarding hospital choice by the public.Data sources/setting
Medicare claims on all patients admitted for Acute Myocardial Infarction between 2009 through 2011.Study design
Cohort analysis using a Bayesian approach, comparing the present assumptions of HC (using a constant mean and constant variance for all hospital random effects), versus an expanded model that allows for the inclusion of hospital characteristics to permit the data to determine whether they vary with attributes of hospitals, such as volume, capabilities, and staffing. Hospital predictions are then created using directly standardized estimates to facilitate comparisons between hospitals.Data collection/extraction methods
Medicare fee-for-service claims.Principal findings
Our model that included hospital characteristics produces very different predictions from the current HC model, with higher predicted mortality rates at hospitals with lower volume and worse characteristics. Using Chicago as an example, the expanded model would advise patients against seeking treatment at the smallest hospitals with worse technology and staffing.Conclusion
To aid patients when selecting between hospitals, the Centers for Medicare and Medicaid Services (CMS) should improve the HC model by permitting its predictions to vary systematically with hospital attributes such as volume, capabilities, and staffing.
SUBMITTER: Silber JH
PROVIDER: S-EPMC4874942 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:There is a concern that the Oncology Care Model (OCM), a voluntary bundled payment program, may incentivize mergers and acquisitions among physician practices leading to reduced competition and price increases. These concerns are heightened if OCM is preferentially adopted in competitive health care markets because it could result in reduced competition, but little is known about the characteristics of markets where OCM is adopted. OBJECTIVE:To measure the association between regional market competition among medical oncologists with the initial adoption of OCM. RESEARCH DESIGN:The Herfindahl-Hirschman Index (HHI), a measure of competition, was calculated for hospital referral regions (HRRs) using secondary data from the Centers for Medicare and Medicaid Services. The relationship between HHI and OCM adoption was assessed using a 2-part regression model adjusting for the market-level number of practices, physician density, average practice size, sociodemographic characteristics, and medical resources. A count model on all HRRs was also estimated to assess an overall effect. SUBJECTS:A total of 10,788 physicians in 3,537 practices who billed Medicare for oncology services in 2015. RESULTS:OCM was adopted in 114 (37%) of the 306 HRRs. We found that practices in competitive health care markets were more likely to adopt OCM than in noncompetitive markets. Two-part regression analysis indicated a nonlinear relationship between HHI and OCM adoption. Average practice size, number of practices in an HRR, and the hospital bed rate were positively associated with adoption, whereas the rate of full-time equivalent hospital employees to 1000 residents was negatively associated with adoption. CONCLUSIONS:OCM adoption was higher in HRRs with greater competition. Careful monitoring of market-level changes among OCM adopters should be undertaken to ensure that the benefits of the OCM outweigh the negative consequences of possible changes in competition.
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