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Learning health system linchpins: information exchange and a common data model.

Aaron S Eisman1,2,3, Elizabeth S Chen1,2,4, Wen-Chih Wu2,4,5

  • 1Center for Biomedical Informatics, Brown University, Providence, RI 02912, United States.

Journal of the American Medical Informatics Association : JAMIA
|November 14, 2024
PubMed
Summary

A health information exchange standardized to a common data model (HIE-CDM) can improve data flow for learning health systems (LHS). This approach enables better population health research and interventions.

Keywords:
atherosclerotic cardiovascular diseasecommon data modelhealth information exchangelearning health system

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Area of Science:

  • Health Informatics
  • Population Health Management
  • Learning Health Systems

Background:

  • Centrally managed health information exchanges (HIEs) aggregate data from multiple partners.
  • Standardizing HIE data to a common data model (CDM) is crucial for interoperability.
  • Learning health systems (LHS) require seamless semantic data flow for continuous improvement.

Purpose of the Study:

  • To demonstrate how a health information exchange common data model (HIE-CDM) facilitates semantic data flow for a learning health system (LHS).
  • To showcase the potential of standardized HIE data for observational population health research.
  • To illustrate leveraging existing health IT infrastructure for LHS advancement.

Main Methods:

  • Operated a statewide HIE aggregating data from over half the state's population across 47 partners.
  • Standardized HIE data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM).
  • Analyzed Atherosclerotic Cardiovascular Disease (ASCVD) risk and primary prevention practices from 2013-2023.

Main Results:

  • Calculated longitudinal 10-year ASCVD risk for 62,999 individuals, revealing risk factors from multiple data partners for many.
  • Enabled granular tracking of individual ASCVD risk, primary prevention (statin therapy), and disease incidence.
  • Found suboptimal statin adherence and identified disparities in ASCVD risk profiles and statin use among Federally Qualified Health Center patients. CDM transformation unified data and reduced heterogeneity.

Conclusions:

  • Demonstrated the potential of HIE-CDM to support observational population health research.
  • Showcased the ability to overcome LHS barriers by utilizing existing health IT infrastructure and data best practices.
  • Confirmed that HIE-CDM facilitates knowledge curation and intervention development at multiple levels (individual, health system, population).