Kaiser Permanente, Mid-Atlantic Permanente Research Institute, Rockville, MD.
Kaiser Permanente, Institute for Health Research, Denver, CO.
Med Care. 2023 Apr 1;61(Suppl 1):S54-S61. doi: 10.1097/MLR.0000000000001834. Epub 2023 Mar 9.
BACKGROUND/OBJECTIVE: In multisite studies, a common data model (CDM) standardizes dataset organization, variable definitions, and variable code structures and can support distributed data processing. We describe the development of a CDM for a study of virtual visit implementation in 3 Kaiser Permanente (KP) regions.
We conducted several scoping reviews to inform our study's CDM design: (1) virtual visit mode, implementation timing, and scope (targeted clinical conditions and departments); and (2) extant sources of electronic health record data to specify study measures. Our study covered the period from 2017 through June 2021. Integrity of the CDM was assessed by a chart review of random samples of virtual and in-person visits, overall and by specific conditions of interest (neck or back pain, urinary tract infection, major depression).
The scoping reviews identified a need to address differences in virtual visit programs across the 3 KP regionsto harmonize measurement specifications for our research analyses. The final CDM contained patient-level, provider-level, and system-level measures on 7,476,604 person-years for KP members aged 19 years and above. Utilization included 2,966,112 virtual visits (synchronous chats, telephone visits, video visits) and 10,004,195 in-person visits. Chart review indicated the CDM correctly identified visit mode on>96% (n=444) of visits, and presenting diagnosis on >91% (n=482) of visits.
Upfront design and implementation of CDMs may be resource intensive. Once implemented, CDMs, like the one we developed for our study, provide downstream programming and analytic efficiencies by harmonizing, in a consistent framework, otherwise idiosyncratic temporal and study site differences in source data.
背景/目的：在多站点研究中，通用数据模型 (CDM) 标准化了数据集组织、变量定义和变量代码结构，并能够支持分布式数据处理。我们描述了为凯萨医疗机构 (KP) 的 3 个地区的虚拟就诊实施研究开发一个 CDM 的过程。
我们进行了几次范围界定审查，以告知我们研究的 CDM 设计：（1）虚拟就诊模式、实施时间和范围（目标临床条件和科室）；（2）电子健康记录数据的现有来源，以指定研究措施。我们的研究涵盖了 2017 年至 2021 年 6 月的时间段。通过对虚拟和面对面就诊的随机样本进行图表审查，评估 CDM 的完整性，总体上并按特定感兴趣的条件（颈部或背部疼痛、尿路感染、重度抑郁症）进行评估。
范围界定审查确定需要解决 3 个 KP 地区的虚拟就诊项目之间的差异，以协调我们研究分析的测量规范。最终的 CDM 包含了患者层面、提供者层面和系统层面的措施，涵盖了年龄在 19 岁及以上的 KP 成员的 7476604 人年。利用率包括 2966112 次虚拟就诊（同步聊天、电话就诊、视频就诊）和 10004195 次面对面就诊。图表审查表明，CDM 正确地识别了超过 96%（n=444）的就诊模式，以及超过 91%（n=482）的就诊的主要诊断。
CDM 的前期设计和实施可能需要大量资源。一旦实施，CDM 就像我们为研究开发的那样，通过在一致的框架中协调源数据中特有的时间和研究地点差异，为下游编程和分析提供效率。
