数据表1：第三方支付方环境中虚拟心脏康复项目的回顾性分析与经济影响预测

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Retrospective Analysis and Forecasted Economic Impact of a Virtual Cardiac Rehabilitation Program in a Third-Party Payer Environment.DOCX
• DOI：10.3389/fdgth.2021.678009.s001
• 发布日期：2021-11-24
• 文件数：1 个
• 文件总体积：30.00 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Retrospective_Analysis_and_Forecasted_Economic_Impact_of_a_Virtual_Cardiac_Rehabilitation_Program_in_a_Third-Party_Payer_Environment_DOCX/17075864

命令行下载

curl -L -o "Data_Sheet_1_Retrospective Analysis and Forecasted Economic Impact of a Virtual Cardiac Rehabilitation Program in a Third-Party Payer Environment.DOCX" "https://ndownloader.figshare.com/files/31572098"

文件清单

• Data_Sheet_1_Retrospective Analysis and Forecasted Economic Impact of a Virtual Cardiac Rehabilitation Program in a Third-Party Payer Environment.DOCX（30.00 KB）

文件总体积 30.00 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

Background: Participation in cardiac rehabilitation (CR) is recommended for all patients with coronary artery disease (CAD) following hospitalization for acute coronary syndrome or stenting. Yet, few patients participate due to the inconvenience and high cost of attending a facility-based program, factors which have been magnified during the ongoing COVID pandemic. Based on a retrospective analysis of CR utilization and cost in a third-party payer environment, we forecasted the potential clinical and economic benefits of delivering a home-based, virtual CR program, with the goal of guiding future implementation efforts to expand CR access.
Methods: We performed a retrospective cohort study using insurance claims data from a large, third-party payer in the state of Pennsylvania. Primary diagnostic and procedural codes were used to identify patients admitted for CAD between October 1, 2016, and September 30, 2018. Rates of enrollment in facility-based CR, as well as all-cause and cardiovascular hospital readmission and associated costs, were calculated during the 12-months following discharge.
Results: Only 37% of the 7,264 identified eligible insured patients enrolled in a facility-based CR program within 12 months, incurring a mean delivery cost of $2,922 per participating patient. The 12-month all-cause readmission rate among these patients was 24%, compared to 31% among patients who did not participate in CR. Furthermore, among those readmitted, CR patients were readmitted less frequently than non-CR patients within this time period. The average per-patient cost from hospital readmissions was $30,814 per annum. Based on these trends, we forecasted that adoption of virtual CR among patients who previously declined CR would result in an annual cost savings between $1 and $9 million in the third-party healthcare system from a combination of increased overall CR enrollment and fewer hospital readmissions among new HBCR participants.
Conclusions: Among insured patients eligible for CR in a third-party payer environment, implementation of a home-based virtual CR program is forecasted to yield significant cost savings through a combination of increased CR participati

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fdgth.2021.678009.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Retrospective_Analysis_and_Forecasted_Economic_Impact_of_a_Virtual_Cardiac_Rehabilitation_Program_in_a_Third-Party_Payer_Environment_DOCX/17075864）。