@inproceedings{456930b934804b06bb1807d0905b2361,
title = "Impact of Mandated Public Reporting in California on 30-Day readmission following CABG surgery: A Health policy analysis",
abstract = "The 30-day all-cause readmission rate following coronary artery bypass graft (CABG) surgery is considered an important outcome measure for patients because higher rates can be an indicator of low quality and unnecessary health care costs. Our research uses rigorous methods to explore the impact of mandatory public reporting of all-cause readmission rates following CABG surgery in California. We used a hierarchical logistic regression model on 173, 823 CABG patient records. This model standardised outcomes across 10 U.S. states that were not previously comparable due to different CABG definitions and metrics. Additionally, in order to account for the differences in medical practice across different states, we applied a differencein-difference method to estimate the impact of public reporting. Finally, a recycled prediction method was used to estimate the number of averted readmissions following public reporting initiation in California.",
keywords = "all-cause readmission rate, coronary artery bypass graft surgery, difference-in-difference model, hierarchical logistic regression, mandating public reporting, recycled predictions, risk-adjustment",
author = "Monika Ray and Banafsheh Sadeghi and Dominique Ritley and Romano, {Patrick S.}",
year = "2019",
month = dec,
doi = "10.1109/BigData47090.2019.9006332",
language = "English (US)",
series = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6205--6207",
editor = "Chaitanya Baru and Jun Huan and Latifur Khan and Hu, {Xiaohua Tony} and Ronay Ak and Yuanyuan Tian and Roger Barga and Carlo Zaniolo and Kisung Lee and Ye, {Yanfang Fanny}",
booktitle = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",
note = "2019 IEEE International Conference on Big Data, Big Data 2019 ; Conference date: 09-12-2019 Through 12-12-2019",
}