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Understanding challenges of using routinely collected health data to address clinical care gaps: a case study in Alberta, Canada

Authors
  • McGuckin, Taylor1
  • Crick, Katelynn1
  • Myroniuk, Tyler W2
  • Setchell, Brock1
  • Yeung, Roseanne O1, 3
  • Campbell-Scherer, Denise1, 3
  • 1 University of Alberta, Edmonton, Alberta, Canada , Edmonton (Canada)
  • 2 University of Missouri, Columbia, Missouri, USA , Columbia
  • 3 University of Alberta, Edmonton, AB, Canada , Edmonton (Canada)
Type
Published Article
Journal
BMJ Open Quality
Publisher
BMJ Publishing Group
Publication Date
Jan 07, 2022
Volume
11
Issue
1
Identifiers
DOI: 10.1136/bmjoq-2021-001491
PMID: 34996811
PMCID: PMC8744094
Source
PubMed Central
Keywords
Disciplines
  • 1506
License
Unknown

Abstract

High-quality data are fundamental to healthcare research, future applications of artificial intelligence and advancing healthcare delivery and outcomes through a learning health system. Although routinely collected administrative health and electronic medical record data are rich sources of information, they have significant limitations. Through four example projects from the Physician Learning Program in Edmonton, Alberta, Canada, we illustrate barriers to using routinely collected health data to conduct research and engage in clinical quality improvement. These include challenges with data availability for variables of clinical interest, data completeness within a clinical visit, missing and duplicate visits, and variability of data capture systems. We make four recommendations that highlight the need for increased clinical engagement to improve the collection and coding of routinely collected data. Advancing the quality and usability of health systems data will support the continuous quality improvement needed to achieve the quintuple aim.

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