AI Article Synopsis
- A common data model (CDM) was developed for a multisite study on virtual visits across three Kaiser Permanente regions to standardize data and enhance distributed processing.
- The study involved reviewing the design of the CDM based on past virtual visit practices and existing electronic health record data, covering a time frame from 2017 to June 2021.
- The final CDM successfully integrated data from over 7 million person-years, achieving high accuracy in identifying visit types and diagnoses, thereby streamlining analysis despite initial resource demands.
Article Abstract
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.
Methods: 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).
Results: 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.
Conclusions: 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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9994571 | PMC |
| http://dx.doi.org/10.1097/MLR.0000000000001834 | DOI Listing |
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