Impact of Mandated Public Reporting in California on 30-Day readmission following CABG surgery: A Health policy analysis

Monika Ray; Banafsheh Sadeghi; Dominique Ritley; Patrick S. Romano

doi:10.1109/BigData47090.2019.9006332

Impact of Mandated Public Reporting in California on 30-Day readmission following CABG surgery: A Health policy analysis

Monika Ray, Banafsheh Sadeghi, Dominique Ritley, Patrick S. Romano

General Medicine, Geriatrics, and Bioethics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English (US)
Title of host publication	Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
Editors	Chaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	6205-6207
Number of pages	3
ISBN (Electronic)	9781728108582
DOIs	https://doi.org/10.1109/BigData47090.2019.9006332
State	Published - Dec 2019
Event	2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States Duration: Dec 9 2019 → Dec 12 2019

Publication series

Name	Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

Conference

Conference	2019 IEEE International Conference on Big Data, Big Data 2019
Country	United States
City	Los Angeles
Period	12/9/19 → 12/12/19

Keywords

all-cause readmission rate
coronary artery bypass graft surgery
difference-in-difference model
hierarchical logistic regression
mandating public reporting
recycled predictions
risk-adjustment

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Information Systems
Information Systems and Management

Access to Document

10.1109/BigData47090.2019.9006332

Fingerprint Dive into the research topics of 'Impact of Mandated Public Reporting in California on 30-Day readmission following CABG surgery: A Health policy analysis'. Together they form a unique fingerprint.

Cite this

Ray, M., Sadeghi, B., Ritley, D., & Romano, P. S. (2019). Impact of Mandated Public Reporting in California on 30-Day readmission following CABG surgery: A Health policy analysis. In C. Baru, J. Huan, L. Khan, X. T. Hu, R. Ak, Y. Tian, R. Barga, C. Zaniolo, K. Lee, & Y. F. Ye (Eds.), Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 (pp. 6205-6207). [9006332] (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData47090.2019.9006332

Impact of Mandated Public Reporting in California on 30-Day readmission following CABG surgery : A Health policy analysis. / Ray, Monika; Sadeghi, Banafsheh; Ritley, Dominique; Romano, Patrick S.

Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. ed. / Chaitanya Baru; Jun Huan; Latifur Khan; Xiaohua Tony Hu; Ronay Ak; Yuanyuan Tian; Roger Barga; Carlo Zaniolo; Kisung Lee; Yanfang Fanny Ye. Institute of Electrical and Electronics Engineers Inc., 2019. p. 6205-6207 9006332 (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ray, M, Sadeghi, B, Ritley, D & Romano, PS 2019, Impact of Mandated Public Reporting in California on 30-Day readmission following CABG surgery: A Health policy analysis. in C Baru, J Huan, L Khan, XT Hu, R Ak, Y Tian, R Barga, C Zaniolo, K Lee & YF Ye (eds), Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019., 9006332, Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019, Institute of Electrical and Electronics Engineers Inc., pp. 6205-6207, 2019 IEEE International Conference on Big Data, Big Data 2019, Los Angeles, United States, 12/9/19. https://doi.org/10.1109/BigData47090.2019.9006332

Ray M, Sadeghi B, Ritley D, Romano PS. Impact of Mandated Public Reporting in California on 30-Day readmission following CABG surgery: A Health policy analysis. In Baru C, Huan J, Khan L, Hu XT, Ak R, Tian Y, Barga R, Zaniolo C, Lee K, Ye YF, editors, Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 6205-6207. 9006332. (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019). https://doi.org/10.1109/BigData47090.2019.9006332

Ray, Monika ; Sadeghi, Banafsheh ; Ritley, Dominique ; Romano, Patrick S. / Impact of Mandated Public Reporting in California on 30-Day readmission following CABG surgery : A Health policy analysis. Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. editor / Chaitanya Baru ; Jun Huan ; Latifur Khan ; Xiaohua Tony Hu ; Ronay Ak ; Yuanyuan Tian ; Roger Barga ; Carlo Zaniolo ; Kisung Lee ; Yanfang Fanny Ye. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 6205-6207 (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019).

@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",

}

TY - GEN

T1 - Impact of Mandated Public Reporting in California on 30-Day readmission following CABG surgery

T2 - 2019 IEEE International Conference on Big Data, Big Data 2019

AU - Ray, Monika

AU - Sadeghi, Banafsheh

AU - Ritley, Dominique

AU - Romano, Patrick S.

PY - 2019/12

Y1 - 2019/12

N2 - 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.

AB - 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.

KW - all-cause readmission rate

KW - coronary artery bypass graft surgery

KW - difference-in-difference model

KW - hierarchical logistic regression

KW - mandating public reporting

KW - recycled predictions

KW - risk-adjustment

UR - http://www.scopus.com/inward/record.url?scp=85081407815&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85081407815&partnerID=8YFLogxK

U2 - 10.1109/BigData47090.2019.9006332

DO - 10.1109/BigData47090.2019.9006332

M3 - Conference contribution

T3 - Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

SP - 6205

EP - 6207

BT - Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

A2 - Baru, Chaitanya

A2 - Huan, Jun

A2 - Khan, Latifur

A2 - Hu, Xiaohua Tony

A2 - Ak, Ronay

A2 - Tian, Yuanyuan

A2 - Barga, Roger

A2 - Zaniolo, Carlo

A2 - Lee, Kisung

A2 - Ye, Yanfang Fanny

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 9 December 2019 through 12 December 2019

ER -

Impact of Mandated Public Reporting in California on 30-Day readmission following CABG surgery: A Health policy analysis

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Cite this