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Data visualization for truth maintenance in clinical decision support systems.

Gilbert Chien Liu1, Jere D Odell2, Elizabeth C Whipple3

  • 1Department of Pediatrics, University of Louisville, Louisville, KY, USA.

International Journal of Pediatrics & Adolescent Medicine
|February 27, 2019
PubMed
Summary
This summary is machine-generated.

Visualizing clinical decision support logic models in electronic health records helps teams connect medical knowledge. This enhances proactive initiatives to improve clinical decision support systems.

Keywords:
Decision support systemsElectronic health recordsInformation scienceManagement

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Area of Science:

  • Health Informatics
  • Clinical Decision Support
  • Medical Knowledge Representation

Background:

  • Clinical decision support systems (CDSS) are crucial for healthcare.
  • Expanding the knowledge base of CDSS requires understanding their underlying logic.
  • Current methods for mapping CDSS logic can be complex and fragmented.

Purpose of the Study:

  • To inform proactive initiatives for expanding the knowledge base of CDSS.
  • To demonstrate a method for visualizing CDSS logic models within electronic health records (EHRs).

Main Methods:

  • Employed visualization tools and social network analysis software (NodeXL, CMAP).
  • Mapped logic models for clinical decision support within an EHR.
  • Defined relationships based on shared observations or common keywords in medical logic modules (MLMs).

Main Results:

  • Studied the Child Health Improvement through Computer Automation (CHICA) system's EHR.
  • Generated data visualizations illustrating connections between clinical topics within 300 MLMs.
  • Identified connections and gaps in the logic of the CDSS.

Conclusions:

  • Visualization of CDSS logic models facilitates interdisciplinary collaboration.
  • Maps can help refine EHRs by connecting related medical knowledge domains.
  • This approach supports teams in addressing comorbidities and improving CDSS functionality.