Hospital Value-Based Payment Programs and Disparity In the United States: A Review of Current Evidence and Future Perspectives

Beginning in the early 2010s, an array of Value-Based Purchasing (VBP) programs has been developed in the United States (U.S.) to contain costs and improve health care quality. Despite documented successes in these efforts in some instances, there have been growing concerns about the programs\u27 unintended consequences for health care disparities due to their built-in biases against health care organizations that serve a disproportionate share of disadvantaged patient populations. We explore the effects of three Medicare hospital VBP programs on health and health care disparities in the U.S. by reviewing their designs, implementation history, and evidence on health care disparities. The available empirical evidence thus far suggests varied impacts of hospital VBP programs on health care disparities. Most of the reviewed studies in this paper demonstrate that hospital VBP programs have the tendency to exacerbate health care disparities, while a few others found evidence of little or no worsening impacts on disparities. We discuss several policy options and recommendations which include various reform approaches and specific programs ranging from those addressing upstream structural barriers to health care access, to health care delivery strategies that target service utilization and health outcomes of vulnerable populations under the VBP programs. Future studies are needed to produce more explicit, conclusive, and consistent evidence on the impacts of hospital VBP programs on disparities

Electronic medical records and medical procedure choice: Evidence from cesarean sections

This paper examines how hospital adoption of electronic medical records (EMRs) impacts medical procedure choice in the context of cesarean section deliveries. It provides a unique contribution by tying the literature on EMR diffusion to the literature on the utilization of expensive medical technology and provider practice style. Exploiting within‐hospital variation in three types of EMR adoption, we find that computerized physician order entry, an advanced EMR system that typically incorporates decision support, reduces C‐section rates for low‐risk mothers by 2.5%. Obstetric‐specific EMR systems and physician documentation have no statistically significant effect on C‐section rates. In addition, we find that the computerized practitioner order entry effect occurs predominantly in hospitals that were already performing fewer C‐sections and does not change the behavior of already high‐intensity providers

Supplementary Methods 1 from Isolating the Drivers of Racial Inequities in Prostate Cancer Treatment

Supplementary Methods 1: Explanation of decomposition method, prediction models, data sample, and codes used for treatment.</p

Evaluation of crowdsourced mortality prediction models as a framework for assessing artificial intelligence in medicine.

OBJECTIVE: Applications of machine learning in healthcare are of high interest and have the potential to improve patient care. Yet, the real-world accuracy of these models in clinical practice and on different patient subpopulations remains unclear. To address these important questions, we hosted a community challenge to evaluate methods that predict healthcare outcomes. We focused on the prediction of all-cause mortality as the community challenge question. MATERIALS AND METHODS: Using a Model-to-Data framework, 345 registered participants, coalescing into 25 independent teams, spread over 3 continents and 10 countries, generated 25 accurate models all trained on a dataset of over 1.1 million patients and evaluated on patients prospectively collected over a 1-year observation of a large health system. RESULTS: The top performing team achieved a final area under the receiver operator curve of 0.947 (95% CI, 0.942-0.951) and an area under the precision-recall curve of 0.487 (95% CI, 0.458-0.499) on a prospectively collected patient cohort. DISCUSSION: Post hoc analysis after the challenge revealed that models differ in accuracy on subpopulations, delineated by race or gender, even when they are trained on the same data. CONCLUSION: This is the largest community challenge focused on the evaluation of state-of-the-art machine learning methods in a healthcare system performed to date, revealing both opportunities and pitfalls of clinical AI

The Sedimentary Geochemistry and Paleoenvironments Project.

Authors thank the donors of The American Chemical Society Petroleum Research Fund for partial support of SGP website development (61017-ND2). EAS is funded by National Science Foundation grant (NSF) EAR-1922966. BGS authors (JE, PW) publish with permission of the Executive Director of the British Geological Survey, UKRI.Publisher PDFPeer reviewe