Data visualization for truth maintenance in clinical decision support systems

BACKGROUND AND OBJECTIVES: The goal is to inform proactive initiatives to expand the knowledge base of clinical decision support systems. DESIGN AND SETTING: We describe an initiative in which research informationists and health services researchers employ visualization tools to map logic models for clinical decision support within an electronic health record. MATERIALS AND METHODS: We mapped relationships using software for social network analysis: NodeXL and CMAP. We defined relationships by shared observations, such as two Arden rules within medical logic modules that consider the same clinical observation, or by the presence of common keywords that were used to label rules according to standardized vocabularies. RESULTS: We studied the Child Health Improvement through Computer Automation (CHICA) system, an electronic medical record that contains 170 unique variables representing discrete clinical observations. These variables were used in 300 medical logic modules (MLM's) that prompted health care providers to deliver preventive counseling or otherwise served as clinical decision support. Using data visualization tools, we generated maps that illustrate connections, or lack thereof, between clinical topics within CHICA's MLMs. CONCLUSIONS: The development of such maps may allow multiple disciplines commonly interacting over EMR platforms, and various perspectives (clinicians, programmers, informationists) to work more effectively as teams to refine the EMR by programming logic routines to address co-morbidities or other instances where domains of medical knowledge should be connected.