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

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          Abstract

          Background

          Electronic 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.

          Methods

          A 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.

          Results

          The 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.

          Conclusions

          Comparison 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.

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          Most cited references26

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          Validity of diagnostic coding within the General Practice Research Database: a systematic review.

          The UK-based General Practice Research Database (GPRD) is a valuable source of longitudinal primary care records and is increasingly used for epidemiological research. To conduct a systematic review of the literature on accuracy and completeness of diagnostic coding in the GPRD. Systematic review. Six electronic databases were searched using search terms relating to the GPRD, in association with terms synonymous with validity, accuracy, concordance, and recording. A positive predictive value was calculated for each diagnosis that considered a comparison with a gold standard. Studies were also considered that compared the GPRD with other databases and national statistics. A total of 49 papers are included in this review. Forty papers conducted validation of a clinical diagnosis in the GPRD. When assessed against a gold standard (validation using GP questionnaire, primary care medical records, or hospital correspondence), most of the diagnoses were accurately recorded in the patient electronic record. Acute conditions were not as well recorded, with positive predictive values lower than 50%. Twelve papers compared prevalence or consultation rates in the GPRD against other primary care databases or national statistics. Generally, there was good agreement between disease prevalence and consultation rates between the GPRD and other datasets; however, rates of diabetes and musculoskeletal conditions were underestimated in the GPRD. Most of the diagnoses coded in the GPRD are well recorded. Researchers using the GPRD may want to consider how well the disease of interest is recorded before planning research, and consider how to optimise the identification of clinical events.
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            Validating the 8 CPCSSN case definitions for chronic disease surveillance in a primary care database of electronic health records.

            The Canadian Primary Care Sentinel Surveillance Network (CPCSSN) is Canada's first national chronic disease surveillance system based on electronic health record (EHR) data. The purpose of this study was to develop and validate case definitions and case-finding algorithms used to identify 8 common chronic conditions in primary care: chronic obstructive pulmonary disease (COPD), dementia, depression, diabetes, hypertension, osteoarthritis, parkinsonism, and epilepsy. Using a cross-sectional data validation study design, regional and local CPCSSN networks from British Columbia, Alberta (2), Ontario, Nova Scotia, and Newfoundland participated in validating EHR case-finding algorithms. A random sample of EHR charts were reviewed, oversampling for patients older than 60 years and for those with epilepsy or parkinsonism. Charts were reviewed by trained research assistants and residents who were blinded to the algorithmic diagnosis. Sensitivity, specificity, and positive and negative predictive values (PPVs, NPVs) were calculated. We obtained data from 1,920 charts from 4 different EHR systems (Wolf, Med Access, Nightingale, and PS Suite). For the total sample, sensitivity ranged from 78% (osteoarthritis) to more than 95% (diabetes, epilepsy, and parkinsonism); specificity was greater than 94% for all diseases; PPV ranged from 72% (dementia) to 93% (hypertension); NPV ranged from 86% (hypertension) to greater than 99% (diabetes, dementia, epilepsy, and parkinsonism). The CPCSSN diagnostic algorithms showed excellent sensitivity and specificity for hypertension, diabetes, epilepsy, and parkinsonism and acceptable values for the other conditions. CPCSSN data are appropriate for use in public health surveillance, primary care, and health services research, as well as to inform policy for these diseases. © 2014 Annals of Family Medicine, Inc.
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              Systematic review of scope and quality of electronic patient record data in primary care.

              To systematically review measures of data quality in electronic patient records (EPRs) in primary care. Systematic review of English language publications, 1980-2001. Bibliographic searches of medical databases, specialist medical informatics databases, conference proceedings, and institutional contacts. Studies selected according to a predefined framework for categorising review papers. Reference standards and measurements used to judge quality. Bibliographic searches identified 4589 publications. After primary exclusions 174 articles were classified, 52 of which met the inclusion criteria for review. Selected studies were primarily descriptive surveys. Variability in methods prevented meta-analysis of results. Forty eight publications were concerned with diagnostic data, 37 studies measured data quality, and 15 scoped EPR quality. Reliability of data was assessed with rate comparison. Measures of sensitivity were highly dependent on the element of EPR data being investigated, while the positive predictive value was consistently high, indicating good validity. Prescribing data were generally of better quality than diagnostic or lifestyle data. The lack of standardised methods for assessment of quality of data in electronic patient records makes it difficult to compare results between studies. Studies should present data quality measures with clear numerators, denominators, and confidence intervals. Ambiguous terms such as "accuracy" should be avoided unless precisely defined.
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                Author and article information

                Contributors
                umcoleman@myumanitoba.ca
                Gayle.Halas@med.umanitoba.ca
                William.Peeler@med.umanitoba.ca
                nats_oh@gmail.com
                tyler.williamson@ucalgary.ca
                Alan_Katz@cpe.umanitoba.ca
                Journal
                BMC Fam Pract
                BMC Fam Pract
                BMC Family Practice
                BioMed Central (London )
                1471-2296
                5 February 2015
                5 February 2015
                2015
                : 16
                : 11
                Affiliations
                [ ]Department of Family Medicine, University of Manitoba, Winnipeg, MB Canada
                [ ]Department of Community Health Sciences, University of Calgary, Calgary, AB Canada
                [ ]Department of Community Health Sciences, Manitoba Centre for Health Policy, University of Manitoba, 408-727 McDermot Ave, Winnipeg, MB R3E 3P5 Canada
                Article
                223
                10.1186/s12875-015-0223-z
                4324413
                25649201
                8c5c6e05-f1b0-4b39-91e7-1772f261b4ef
                © Coleman et al.; licensee BioMed Central. 2015

                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.

                History
                : 29 September 2014
                : 9 January 2015
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2015

                Medicine
                electronic medical records,primary care,chronic disease,health information systems
                Medicine
                electronic medical records, primary care, chronic disease, health information systems

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