A basic model for assessing primary health care electronic medical record data quality

Heather Maddocks,Vijaya Chevendra,Moira Stewart,J Neil Marshall,Fred Burge,Sonny Cejic,Bert M Chesworth,Simon De Lusignan,Amanda L Terry,Joshua Shadd,Amardeep Thind

doi:10.1186/s12911-019-0740-0

Abstract

BackgroundThe increased use of electronic medical records (EMRs) in Canadian primary health care practice has resulted in an expansion of the availability of EMR data. Potential users of these data need to understand their quality in relation to the uses to which they are applied. Herein, we propose a basic model for assessing primary health care EMR data quality, comprising a set of data quality measures within four domains. We describe the process of developing and testing this set of measures, share the results of applying these measures in three EMR-derived datasets, and discuss what this reveals about the measures and EMR data quality. The model is offered as a starting point from which data users can refine their own approach, based on their own needs.MethodsUsing an iterative process, measures of EMR data quality were created within four domains: comparability; completeness; correctness; and currency. We used a series of process steps to develop the measures. The measures were then operationalized, and tested within three datasets created from different EMR software products.ResultsA set of eleven final measures were created. We were not able to calculate results for several measures in one dataset because of the way the data were collected in that specific EMR. Overall, we found variability in the results of testing the measures (e.g. sensitivity values were highest for diabetes, and lowest for obesity), among datasets (e.g. recording of height), and by patient age and sex (e.g. recording of blood pressure, height and weight).ConclusionsThis paper proposes a basic model for assessing primary health care EMR data quality. We developed and tested multiple measures of data quality, within four domains, in three different EMR-derived primary health care datasets. The results of testing these measures indicated that not all measures could be utilized in all datasets, and illustrated variability in data quality. This is one step forward in creating a standard set of measures of data quality. Nonetheless, each project has unique challenges, and therefore requires its own data quality assessment before proceeding.

Highlights

The increased use of electronic medical records (EMRs) in Canadian primary health care practice has resulted in an expansion of the availability of Electronic Medical Record (EMR) data
This builds on previous EMR data quality work, but differs because we developed and tested multiple measures of data quality, within four domains, in three different EMR-derived primary health care datasets
Conceptualizing data quality domains Focusing on the assessment of EMR data quality from the research perspective, we conceptualized the measurement of EMR data quality within four domains

Summary

Introduction

The increased use of electronic medical records (EMRs) in Canadian primary health care practice has resulted in an expansion of the availability of EMR data. The increased use of electronic medical records (EMRs) in Canadian primary health care practice [1,2,3] has resulted in an expansion of the availability of EMR data These data are being put to uses such as quality improvement activities related to patient care, and secondary purposes such as research and disease surveillance [4, 5]. Issues have been identified in the completeness of risk factor information [13, 14] chronic disease documentation [15], recording of weight and family history [14], and socio-demographic data quality [16] This echoes the evidence from other countries [17,18,19], from studies conducted in the past [20,21,22] and in other health care settings [23]. These results reinforce that EMR data quality is an ongoing issue, for researchers

Methods

Results

Discussion

Conclusion