Open AccessUsing the Canadian Institute for Health Information’s Information Quality Framework to Support Integration and Utilization of Complex, Multi-Jurisdictional Data
Author(s) -
Chrissy Willemse
Publication year - 2020
Publication title -
international journal of population data science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.602
H-Index - 7
ISSN - 2399-4908
DOI - 10.23889/ijpds.v5i5.1556
Subject(s) - data quality , information quality , computer science , documentation , operationalization , data science , quality (philosophy) , health informatics , information system , data governance , knowledge management , health care , business , engineering , metric (unit) , philosophy , epistemology , marketing , economic growth , economics , electrical engineering , programming language
The Canadian Institute for Health Information (CIHI) provides essential information on Canada’s health systems and the health of Canadians. This presentation discusses information quality’s role in the integration and utilization of CIHI’s complex, multi-sector and multi-jurisdictional data.
IntroductionCIHI’s Data and Information Quality Program is recognized internationally for its comprehensiveness and high standards. As the need for linked data research increases, the requirements on quality continue to grow. CIHI’s multi-sector, multi-jurisdictional healthcare system and the varying health policies, care delivery models, and data collection practices that go with it pose challenges for researchers as they try to pull the data together in a comprehensive way. CIHI’s Information Quality Framework forms the foundation for addressing these challenges and ensuring data are fit for integration and are properly utilized.
Objectives and ApproachIn 2019, a connected data quality project was initiated to improve the usability of CIHI’s analytical data. Information quality framework concepts were applied across CIHI data sources to better understand data linkage challenges, measure inconsistencies across data sources, identify opportunities to improve data and standards, and develop resources to support users.
ResultsFindings from the project identified key connected data quality activities for the organization to operationalize. These focus on quality assessment and reporting; harmonization of data standards; expanded documentation and analytical resources; data classification and profiling tools to support descriptive analysis; and new source of truth and pre-linked datasets. Quality activities were prioritized based on need and complexity, and “connected data teams” were established to carry out the work.
Conclusion / ImplicationsExpansion of CIHI’s quality framework across data sources facilitates its data linkage capabilities and “connected data” use. It enables the evolution of CIHI’s analytical environments and information products from being database specific to integrated-data driven, and facilitates the use of CIHI’s analytical data for research.