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Achieving quality primary care data: a description of the Canadian Primary Care Sentinel Surveillance Network data capture, extraction, and processing in Alberta

Authors :
Stephanie Garies
Michael Cummings
Brian Forst
Kerry McBrien
Boglarka Soos
Matt Taylor
Neil Drummond
Donna Manca
Kimberley Duerksen
Hude Quan
Tyler Williamson
Source :
International Journal of Population Data Science, Vol 4, Iss 2 (2019)
Publication Year :
2019
Publisher :
Swansea University, 2019.

Abstract

Electronic medical record (EMR) databases have become increasingly popular for secondary purposes, such as health research. The Canadian Primary Care Sentinel Surveillance Network (CPCSSN) is the country’s first and only national primary care EMR data repository, with de-identified health information for almost two million Canadians. Comprehensive and freely available documentation describing the data ‘lifecycle’ is important for assessing potential data quality issues and appropriate interpretation of research findings. Here, we describe the flow of CPCSSN data in the province of Alberta. The data originate from 54 publicly-funded primary care settings, including one community pediatric clinic, with 318 providers contributing de-identified EMR data for 410,951 patients. Data extraction methods have been developed for five different EMR systems, and include both backend and automated frontend extractions. The raw EMR data are transformed according to specific rules, including trimming implausible values, converting values and free text to standard terminologies or classification systems, and structuring the data into a common CPCSSN format. Regional networks across Canada are responsible for their local data extraction and processing, before the data are transferred to a central repository, and made available for research and disease surveillance. This paper aims to provide important contextual information to future CPCSSN data users.

Details

Language :
English
ISSN :
23994908
Volume :
4
Issue :
2
Database :
Directory of Open Access Journals
Journal :
International Journal of Population Data Science
Publication Type :
Academic Journal
Accession number :
edsdoj.5d33696672634b45879362ab15c7fec5
Document Type :
article
Full Text :
https://doi.org/10.23889/ijpds.v4i2.1132