在英国国家卫生服务机构的多个电子记录系统中对卵巢癌数据进行系统的选和分析:一个第三级推中心的经验
A Samani1,2, G Giannone1,2, L Mercuri3
1Department of Oncology, Imperial College Healthcare NHS Trust, London, UK.
概括
对卵巢癌患者的真实世界数据 (RWD) 的自动处理是可行的,能够及时进行研究和操作洞察. 这种方法提供了可靠的数据,反映了不断变化的治疗实践,并支持临床决策.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
背景情况:
- 手动治疗卵巢癌真实世界数据 (RWD) 是资源密集型的.
- 信息学和临床团队之间建立了新的合作,以自动化数据策划.
- 该倡议旨在为卵巢癌患者提供综合及及时的RWD接入.
研究的目的:
- 开发和实施一个自动化系统,用于策划和分析卵巢癌中的真实数据.
- 评估用于研究和操作用途的大规模自动化数据处理的可行性和稳定性.
- 随着时间的推移,确定卵巢癌患者护理和治疗的趋势.
主要方法:
- 在临床领域定义了高产率,可访问的数据点.
- 在iCARE安全数据环境中集成,清理和分析数据.
- 利用多变量生存建模来验证数据的稳定性.
主要成果:
- 在2014-2022年期间诊断出1581名卵巢癌患者的数据进行了策划.
- 转诊专家瘤委员会增加,而基线特征保持稳定.
- 2020年观察到新疗法线的减少,与SARS-CoV-2爆发相吻合.
结论:
- 自动化的RWD策划和分析可以在规模上和实时实现.
- 自动化方法产生了与现有文献相一致的临床相关发现.
- 关键的现实世界变量的自动策划是可行的,为研究和操作应用提供了可靠的数据.
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