Barriers to Data Quality Resulting from the Process of Coding Health Information to Administrative Data: a Qualitative Study

Background: Administrative health data are increasingly used for research and surveillance to inform decision-making because of its large sample sizes, geographic coverage, comprehensivity, and possibility for longitudinal follow-up. Within Canadian provinces, individuals are assigned unique personal health numbers that allow for linkage of administrative health records in that jurisdiction. It is therefore necessary to ensure that these data are of high quality, and that chart information is accurately coded to meet this end. Our objective is to explore the potential barriers that exist for high quality data coding through qualitative inquiry into the roles and responsibilities of medical chart coders.

Methods: We conducted semi-structured interviews with 28 medical chart coders from Alberta, Canada. We used thematic analysis and open-coded each transcript to understand the process of administrative health data generation and identify barriers to its quality.

Results: The process of generating administrative health data is highly complex and involves a diverse workforce. As such, there are multiple points in this process that introduce challenges for high quality data. For coders, the main barriers to data quality occurred around chart documentation, variability in the interpretation of chart information, and high quota expectations.

Conclusions: This study illustrates the complex nature of barriers to high quality coding, in the context of administrative data generation. The findings from this study may be of use to data users, researchers, and decision-makers who wish to better understand the limitations of their data or pursue interventions to improve data quality.