Risk assessment
基于青光眼患者登记的质量管理系统构建与风险评估数据集，包含标准操作程序和风险控制计划。
基本信息
资源简介
Risk assessment
【数据集背景】
本数据集源站标题为《Risk assessment.》。源站首次发布：2023-06-02。
【数据内容】
源站原始描述：Background Disease/patient registries are underutilized despite their multiple advantages over clinical trials in the clinical evaluation of drugs, such as the capacity for long-term curation, provision of patient outcome data in routine clinical practice, and provision of benchmark data for comparison. Ensuring the fit-for-purpose quality of data generated from such registries is important to informing regulatory decision making. Here, we report the construction of a quality management system a…
【数据结构与技术规格】
文件数 1 个，合计 9.73 KB。Table 2.xls：application/vnd.ms-excel，9.73 KB。数据集 DOI：10.1371/journal.pone.0286669.t002。
【主题与分类】
源站学科分类：Medicine、Cell Biology、Biotechnology、Biological Sciences not elsewhere classified、Developmental Biology、Science Policy。主题标签：official guidelines issued、major ophthalmology measurements、included training sessions、multiple health authorities、computerized system validation、electronic data capture、routine clinical practice、regulatory guidelines relevant、xlink "> disease、22 risk items。
【适用方向】
文本类数据可用于医学自然语言处理，如命名实体识别、信息抽取与文本分类。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/Risk_assessment_/23287163
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Wada, Shinsuke; Tsuda, Satoru; Abe, Maiko; Nakazawa, Toru; Urushihara, Hisashi (2023). Risk assessment.. PLOS ONE. Dataset
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：Risk assessment.
- DOI：10.1371/journal.pone.0286669.t002
- 发布日期：2023-06-02
- 文件数：1 个
- 文件总体积：9.73 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Risk_assessment_/23287163
命令行下载
curl -L -o "Table 2.xls" "https://ndownloader.figshare.com/files/41042771"
文件清单
Table 2.xls（9.73 KB）
文件总体积 9.73 KB（源站 API 实测）。
数据说明
Background
Disease/patient registries are underutilized despite their multiple advantages over clinical trials in the clinical evaluation of drugs, such as the capacity for long-term curation, provision of patient outcome data in routine clinical practice, and provision of benchmark data for comparison. Ensuring the fit-for-purpose quality of data generated from such registries is important to informing regulatory decision making. Here, we report the construction of a quality management system aiming to ensure regulatory-grade data quality for a registry of Japanese patients with glaucoma to evaluate long-term patient outcomes.
Methods
The quality management system was established by reference to the risk-based approach in the ICH-E6 (R2) recommendations. The following three-component approach was taken: establishment of governance, computerized system validation (CSV), and implementation of risk assessment and control. Compliance of the system with the recommendations of regulatory guidelines relevant to use of the registry was assessed.
Results
Governance by academic collaboration was established. This was followed by the development of a total of 15 standard operating procedures, including CSV, data management, monitoring, audit, and management of imaging data. The data management system was constructed based on a data management plan, which specified data/paper flow and data management procedures. The electronic data capture (EDC) system was audited by an external vendor, and configured and validated using the V-model framework as recommended in the GAMP5 guideline. Informed consent, eligibility assessment and major ophthalmology measurements were determined as Critical to Quality (CTQ) factors. A total of 22 risk items were identified and classified into three categories, and operationalized in the form of a risk control plan, which included training sessions and risk-based monitoring. The glaucoma registry addressed most quality recommendations in official guidelines issued by multiple health authorities, although two recommendations were not met.
Conclusions
We established and configured a quality management system for a glaucoma registry to ensure fit-fo
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0286669.t002（source: https://plos.figshare.com/articles/dataset/Risk_assessment_/23287163）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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