From patient care to research: a validation study examining the factors contributing to data quality in a primary care electronic medical record database

BMC Family Practice

Research Article

Tyler Williamson¹  Natalie Casaclang²  Nathan Coleman²  Gayle Halas²  William Peeler²  Alan Katz³ 

[1] Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada;Department of Family Medicine, University of Manitoba, Winnipeg, MB, Canada;Department of Family Medicine, University of Manitoba, Winnipeg, MB, Canada;Department of Community Health Sciences, Manitoba Centre for Health Policy, University of Manitoba, 408-727 McDermot Ave, R3E 3P5, Winnipeg, MB, Canada;

关键词: Electronic Medical Records;    Primary Care;    Chronic Disease;    Health Information Systems;   

DOI  :  10.1186/s12875-015-0223-z

 received in 2014-09-29, accepted in 2015-01-09,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundElectronic Medical Records (EMRs) are increasingly used in the provision of primary care and have been compiled into databases which can be utilized for surveillance, research and informing practice. The primary purpose of these records is for the provision of individual patient care; validation and examination of underlying limitations is crucial for use for research and data quality improvement. This study examines and describes the validity of chronic disease case definition algorithms and factors affecting data quality in a primary care EMR database.MethodsA retrospective chart audit of an age stratified random sample was used to validate and examine diagnostic algorithms applied to EMR data from the Manitoba Primary Care Research Network (MaPCReN), part of the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). The presence of diabetes, hypertension, depression, osteoarthritis and chronic obstructive pulmonary disease (COPD) was determined by review of the medical record and compared to algorithm identified cases to identify discrepancies and describe the underlying contributing factors.ResultsThe algorithm for diabetes had high sensitivity, specificity and positive predictive value (PPV) with all scores being over 90%. Specificities of the algorithms were greater than 90% for all conditions except for hypertension at 79.2%. The largest deficits in algorithm performance included poor PPV for COPD at 36.7% and limited sensitivity for COPD, depression and osteoarthritis at 72.0%, 73.3% and 63.2% respectively. Main sources of discrepancy included missing coding, alternative coding, inappropriate diagnosis detection based on medications used for alternate indications, inappropriate exclusion due to comorbidity and loss of data.ConclusionsComparison to medical chart review shows that at MaPCReN the CPCSSN case finding algorithms are valid with a few limitations. This study provides the basis for the validated data to be utilized for research and informs users of its limitations. Analysis of underlying discrepancies provides the ability to improve algorithm performance and facilitate improved data quality.

【 授权许可】

Unknown   
© Coleman et al.; licensee BioMed Central. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.