Extending an open-source tool to measure data quality: Case report on Observational Health Data Science and Informatics (OHDSI)

Brian E. Dixon, Chen Wen, Tony French, Jennifer L. Williams, Jon D. Duke, Shaun J. Grannis

Research output: Contribution to journalArticle

Abstract

Introduction As the health system seeks to leverage large-scale data to inform population outcomes, the informatics community is developing tools for analysing these data. To support data quality assessment within such a tool, we extended the open-source software Observational Health Data Sciences and Informatics (OHDSI) to incorporate new functions useful for population health. Methods We developed and tested methods to measure the completeness, timeliness and entropy of information. The new data quality methods were applied to over 100 million clinical messages received from emergency department information systems for use in public health syndromic surveillance systems. Discussion While completeness and entropy methods were implemented by the OHDSI community, timeliness was not adopted as its context did not fit with the existing OHDSI domains. The case report examines the process and reasons for acceptance and rejection of ideas proposed to an open-source community like OHDSI.

Original languageEnglish (US)
Article numbere100054
JournalBMJ Health and Care Informatics
Volume27
Issue number1
DOIs
StatePublished - Mar 29 2020

Keywords

  • information systems
  • medical informatics
  • public health

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics
  • Health Information Management

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