RT Journal Article SR Electronic T1 Extending an open-source tool to measure data quality: case report on Observational Health Data Science and Informatics (OHDSI) JF BMJ Health & Care Informatics JO BMJ Health Care Inform FD BMJ Publishing Group Ltd SP e100054 DO 10.1136/bmjhci-2019-100054 VO 27 IS 1 A1 Brian E Dixon A1 Chen Wen A1 Tony French A1 Jennifer L Williams A1 Jon D Duke A1 Shaun J Grannis YR 2020 UL http://informatics.bmj.com/content/27/1/e100054.abstract AB 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.