常规数据质量评估对肯尼亚电子病历数据质量的影响

数据集获取（Figshare）

• 源站标题：The impact of routine data quality assessments on electronic medical record data quality in Kenya
• DOI：10.1371/journal.pone.0195362
• 发布日期：2018-04-18
• 文件数：2 个
• 文件总体积：271.80 KB
• 源站页面：https://plos.figshare.com/articles/dataset/The_impact_of_routine_data_quality_assessments_on_electronic_medical_record_data_quality_in_Kenya/6155720

命令行下载

curl -L -o "S1 Table.pdf" "https://ndownloader.figshare.com/files/11135063"
curl -L -o "S2 Table.pdf" "https://ndownloader.figshare.com/files/11135066"

文件清单

• S1 Table.pdf（131.82 KB）
• S2 Table.pdf（139.97 KB）

文件总体积 271.80 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
Routine Data Quality Assessments (RDQAs) were developed to measure and improve facility-level electronic medical record (EMR) data quality. We assessed if RDQAs were associated with improvements in data quality in KenyaEMR, an HIV care and treatment EMR used at 341 facilities in Kenya.
Methods
RDQAs assess data quality by comparing information recorded in paper records to KenyaEMR. RDQAs are conducted during a one-day site visit, where approximately 100 records are randomly selected and 24 data elements are reviewed to assess data completeness and concordance. Results are immediately provided to facility staff and action plans are developed for data quality improvement. For facilities that had received more than one RDQA (baseline and follow-up), we used generalized estimating equation models to determine if data completeness or concordance improved from the baseline to the follow-up RDQAs.
Results
27 facilities received two RDQAs and were included in the analysis, with 2369 and 2355 records reviewed from baseline and follow-up RDQAs, respectively. The frequency of missing data in KenyaEMR declined from the baseline (31% missing) to the follow-up (13% missing) RDQAs. After adjusting for facility characteristics, records from follow-up RDQAs had 0.43-times the risk (95% CI: 0.32–0.58) of having at least one missing value among nine required data elements compared to records from baseline RDQAs. Using a scale with one point awarded for each of 20 data elements with concordant values in paper records and KenyaEMR, we found that data concordance improved from baseline (11.9/20) to follow-up (13.6/20) RDQAs, with the mean concordance score increasing by 1.79 (95% CI: 0.25–3.33).
Conclusions
This manuscript demonstrates that RDQAs can be implemented on a large scale and used to identify EMR data quality problems. RDQAs were associated with meaningful improvements in data quality and could be adapted for implementation in other settings.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0195362（source: https://plos.figshare.com/articles/dataset/The_impact_of_routine_data_quality_assessments_on_electronic_medical_record_data_quality_in_Kenya/6155720）。