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A common data model (CDM) standardized virtual visit data across three Kaiser Permanente regions, improving data consistency for research. The CDM accurately identified visit modes and diagnoses, demonstrating its effectiveness.

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Area of Science:

  • Health Informatics
  • Clinical Research Data Management
  • Telehealth Implementation

Background:

  • Multisite studies benefit from a common data model (CDM) for standardizing data.
  • A CDM harmonizes dataset organization, variable definitions, and code structures.
  • This study focused on developing a CDM for virtual visit implementation across three Kaiser Permanente (KP) regions.

Purpose of the Study:

  • To describe the development of a common data model (CDM) for a multisite study on virtual visit implementation.
  • To address and harmonize differences in virtual visit programs across three KP regions for consistent research analyses.
  • To ensure data integrity and accuracy for virtual and in-person visit data within the CDM.

Main Methods:

  • Conducted scoping reviews to inform CDM design, focusing on virtual visit characteristics and electronic health record data sources.
  • Defined patient-level, provider-level, and system-level measures for the CDM.
  • Assessed CDM integrity through chart review of random samples of virtual and in-person visits.

Main Results:

  • The final CDM included data from 7,476,604 person-years for KP members aged 19+.
  • The CDM encompassed 2,966,112 virtual visits and 10,004,195 in-person visits.
  • Chart review confirmed the CDM's accuracy in identifying visit mode (>96%) and presenting diagnosis (>91%).

Conclusions:

  • Developing a common data model (CDM) can be resource-intensive upfront.
  • Once implemented, CDMs offer significant downstream programming and analytic efficiencies.
  • CDMs harmonize site-specific data differences into a consistent framework for research.