Extracting and Visualizing Frequent Medical Instruction Patterns with Statistical Insights from Multi-Institutional Electronic Medical Record Data

Despite the widespread adoption of electronic medical records (EMR), the variations in format and terminology across institutions hinder inter-institutional comparisons and feature extraction. This paper proposes a demonstration of extracting frequent disease-specific instruction sequences and efficiently visualizing them with statistical insights, e.g. statistical trends, and abnormal inspection result rates from real multi-institutional EMR data. The utility of the developed visualization tool was presented for decision support and improving clinical processes.