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

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.

Keywords: data accuracy; health services research; healthcare quality improvement; quality improvement; quality improvement methodologies.

Figure 1

The Physician Learning Program’s non-linear process of quality improvement using routinely collected health data. The key elements are: (1) cocreating clinical questions and identifying whether secondary data are available or if primary data collection is necessary; (2) gathering data from databases or completing primary data collection; (3) deep cleaning of the data; (4) conducting analyses and further data cleaning; and (5) effectively communicating findings that serve as the basis for quality improvement.