Using Prevalence Patterns to Discover Un-Mapped Flowsheet Data in an Electronic Health Record Data Warehouse
We have developed a data summarization tool called Chi2notype which leverages the star schema of the Integrating Informatics from Bench to Bedside (i2b2) vendor-neutral data-warehouse platform to characterize a patient-cohort of interest. Chi2notype calculates a chi-squared statistic for every one of the tens of thousands of facts in an Electronic Medical Record System (EMR) and uses it to rank them from most over-represented in the cohort to most under-represented. This can be used for many purposes, including detection of adverse events, studies of socioeconomic disparities in health outcomes, and quality control. Here we demonstrate the use of Chi2notype to find un-mapped elements from nursing flowsheets commonly used for measuring the progress of ALS patients, thus making it possible to link them to their respective parent flowsheets in the i2b2 ontology. This, in turn, makes these flowsheets accessible to researchers performing preparatory queries or retrospective analysis on de-identified electronic health record (EHR) data.