Hospitals in low-income communities less likely to participate in bundled payment programs

A new study has shown that hospitals in low-income communities were less likely to participate in bundled payment programs for joint replacement surgery. Hospitals with more low socioeconomic status individuals were less likely to participate in voluntary – and to an even greater extent, mandatory – bundled payment programs, according to the study published in the peer-reviewed journal Population Health Management.

Joshua Liao, MD, from the University of Washington School of Medicine, and coauthors, used Medicare claims and data from the Bundled Payments for Care Improvement initiative and Comprehensive Care for Joint Replacement model to examine whether hospitals' bundled payment participation is related to the proportion of historically marginalized individuals in the communities they serve. The authors discuss the policy implications of their findings.

"These findings raise concerns about generalizability of overall program results and potential disparities and suggest that policymakers should consider communities' social factors and participation type in the design of future bundled payment programs," conclude the investigators.

Bundle payments work to reduce waste and improve outcomes. However, if ethnic disparities persist in deploying bundle payments, everyone loses. Research like this is important to uncover these shortcomings and provide an incentive to change."

David Nash, MD, MBA, Editor-in-Chief of Population Health Management and Founding Dean Emeritus and Dr. Raymond C. and Doris N. Grandon Professor, Jefferson College of Population Health, Philadelphia, PA

Source:
Journal reference:

Liao, J.M., et al. (2022) The Proportion of Marginalized Individuals in US Communities and Hospital Participation in Bundled Payments. Population Health Management. doi.org/10.1089/pop.2021.0334.

Comments

The opinions expressed here are the views of the writer and do not necessarily reflect the views and opinions of News Medical.
Post a new comment
Post

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.

You might also like...
How AI is advancing mammographic density-based breast cancer risk prediction