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

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

doi:10.1186/s12911-019-0740-0

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

BMC Med Inform Decis Mak. 2019 Feb 12;19(1):30. doi: 10.1186/s12911-019-0740-0.

Authors

Affiliations

¹ Department of Family Medicine, Department of Epidemiology & Biostatistics, Schulich Interfaculty Program in Public Health, Schulich School of Medicine & Dentistry, The University of Western Ontario, 1151 Richmond Street, London, Ontario, N6A 3K7, Canada. aterry4@uwo.ca.
² Department of Family Medicine, Department of Epidemiology & Biostatistics, Schulich School of Medicine & Dentistry, The University of Western Ontario, 1151 Richmond Street, London, Ontario, N6A 3K7, Canada.
³ Department of Clinical and Experimental Medicine, University of Surrey, Guildford, Surrey, GU2 7XH, UK.
⁴ School of Physical Therapy, Faculty of Health Sciences, Department of Epidemiology & Biostatistics, Schulich School of Medicine & Dentistry, The University of Western Ontario, 1151 Richmond Street, London, Ontario, N6A 3K7, Canada.
⁵ Science and Software Educator and Consultant, 58 Moraine Walk, London, Ontario, N6G 4Y8, Canada.
⁶ Department of Family Medicine, Schulich School of Medicine & Dentistry, The University of Western Ontario, 1151 Richmond Street, London, Ontario, N6A 3K7, Canada.
⁷ Department of Family Medicine, McMaster University, 100 Main Street West, 6th Floor, Hamilton, Ontario, L8P 1H6, Canada.
⁸ Department of Family Medicine, Dalhousie University, 5909 Veterans Memorial Lane, Abbie J Lane Building, Room 8101B, Halifax, Nova Scotia, B3H 2E2, Canada.
⁹ Department of Family Medicine, Department of Epidemiology & Biostatistics, Schulich Interfaculty Program in Public Health, Schulich School of Medicine and Dentistry, The University of Western Ontario, 1151 Richmond Street, London, Ontario, N6A 3K7, Canada.

Abstract

Background: 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. 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.

Methods: Using 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.

Results: A 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).

Conclusions: This 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.

Keywords: Computerized medical records; Data quality; Electronic medical records; Primary health care.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adult
Canada
Data Accuracy
Datasets as Topic
Electronic Health Records*
Female
Humans
Male
Middle Aged
Models, Theoretical*
Primary Health Care*
Quality Assurance, Health Care*

Grants and funding

105431/CIHR/Canada