Measuring the geographic variation in US health care outcomes in PLOS ONE.Read the full study
The most comprehensive study of health outcomes in hospitals to date reveals significant variations across the US—with results that break down preconceived notions of where to get the best care.
According to data from a pioneering study of more than 22 million hospital admissions—analyzing 24 specific health outcomes, or what happens to patients after admission—the differences in hospital performance were extreme. The results of this peer-reviewed study, conducted by BCG and partnering health care institutions and published in PLOS ONE, also revealed that these variations persisted across geographies and could not be explained by patient demographics, health upon admission, or a variety of other health-system factors.
Challenging common beliefs, the study found regions in the US with hospitals serving high-income, largely white populations that had poor health outcomes. Conversely, many regions with hospitals serving low-income, mostly minority populations had outstanding results. The variation in outcomes exists across the US, between states, and within states.
The results are telling. Patients in low-performing hospitals (the bottom 10%) are 3 times more likely to die and 13 times more likely to experience complications than those admitted to high-performing hospitals (the top 10%).
The variation in hospital outcomes is likely driven by factors inside the institutions themselves, such as specific care protocols, hospital culture, and the experience of clinical teams—information that is not publicly available. The focus of health care reform has to move beyond cost and include the transparency of this kind of data, encouraging an environment that promotes the sharing of best practices across the health care system.
The data in this study has been adjusted to comply with the Agency for Healthcare Research and the Quality Healthcare Cost and Utilization Project/State Inpatient Databases (HCUP/SID) data use agreement. As a result, all HSAs or counties with only one hospital were merged with adjacent HSAs/counties so that the resulting region contains at least two hospitals as required by this data use agreement.
The BCG VBHC Outcomes Visualization Tool is presented in a beta version. To report any questions with the mapping in this version, please contact VBHCOutcomesTool@bcg.com.
The data shall not be copied or given to any person or entity other than the User (each such person or entity, a “Third Party”) without the prior written consent of BCG. This data serves only as the focus for discussion and is incomplete without the accompanying oral commentary, and should not be relied on as a standalone document. Further, Third Parties may not, and it is unreasonable for any Third Party to, rely on these materials for any purpose whatsoever. To the fullest extent permitted by law (and except to the extent otherwise agreed upon in a signed writing by BCG), BCG shall have no liability whatsoever to any Third Party, and any and all Third Parties hereby waive any rights and claims they may have at any time against BCG, with regard to the data provided by BCG and this study or other materials, including the accuracy or completeness thereof. Receipt and review of this data or document shall be deemed agreement with and consideration for the foregoing.
BCG does not provide fairness opinions or valuations of market transactions, and these materials should not be relied on or construed as such. Further, any financial evaluations, projected market information, and/or data, and conclusions contained in these materials are not based on an in-depth assessment of any entity's portfolio, business, or industry; are not definitive forecasts; and are not guaranteed by BCG. Any projections contained herein do not necessarily represent the full value potential of the portfolio/business. BCG has used public and/or confidential data and assumptions based on market research, and BCG has not independently verified the data and assumptions used in these analyses. Changes in the underlying data or operating assumptions will clearly impact the analyses and conclusions herein.