Using Prevalence Patterns to Discover Un-mapped Flowsheet Data in an Electronic Health Record Data Warehouse

Alex F. Bokov, Angela B. Bos, Laura S. Manuel, Alfredo Tirado-Ramos, Pamela Kittrell, Carlayne E Jackson, Gail P. Olin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 hundreds of thousands of variables in an Electronic Medical Record (EMR) system 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 used for monitoring 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 eligibility queries or retrospective analysis on de-identified electronic health record (EHR) data.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE 30th International Symposium on Computer-Based Medical Systems, CBMS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages324-327
Number of pages4
Volume2017-June
ISBN (Electronic)9781538617106
DOIs
StatePublished - Nov 10 2017
Event30th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2017 - Thessaloniki, Greece
Duration: Jun 22 2017Jun 24 2017

Other

Other30th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2017
CountryGreece
CityThessaloniki
Period6/22/176/24/17

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

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