Liyanage Harshana, Liaw Siaw-Teng, Jonnagaddala Jitendra, Hinton William, de Lusignan Simon
Department of Clinical & Experimental Medicine, University of Surrey, UK.
School of Public Health & Community Medicine, UNSW Medicine Australia, Ingham Institute of Applied Medical Research, NSW, Australia.
Stud Health Technol Inform. 2018;255:60-64.
Common data models (CDM) have enabled the simultaneous analysis of disparate and large data sources. A literature review identified three relevant CDMs: The Observational Medical Outcomes Partnership (OMOP) was the most cited; next the Sentinel; and then the Patient Centered Outcomes Research Institute (PCORI). We tested these three CDMs with fifteen pre-defined criteria for a diabetes cohort study use case, assessing the benefit (good diabetes control), risk (hypoglycaemia) and cost effectiveness of recently licenced medications. We found all three CDMs have a useful role in planning collaborative research and enhance analysis of data cross jurisdiction. However, the number of pre-defined criteria achieved by these three CDMs varied. OMOP met 14/15, Sentinel 13/15, and PCORI 10/15. None met the privacy level we specified, and most of the other gaps were clinical and cost outcome related data.
通用数据模型（CDM）能够对不同的大型数据源进行同步分析。一项文献综述确定了三种相关的CDM：观察性医疗结果合作组织（OMOP）被引用次数最多；其次是哨兵系统；然后是患者为中心的结果研究所（PCORI）。我们用15条预定义标准对这三种CDM进行了测试，用于糖尿病队列研究用例，评估近期获批药物的益处（良好的糖尿病控制）、风险（低血糖）和成本效益。我们发现，这三种CDM在规划合作研究以及加强跨辖区数据的分析方面都发挥着有益作用。然而，这三种CDM达到的预定义标准数量各不相同。OMOP满足了14/15条，哨兵系统满足了13/15条，PCORI满足了10/15条。没有一个符合我们规定的隐私级别，其他大多数差距都与临床和成本结果相关数据有关。
