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      Validation of asthma recording in electronic health records: a systematic review

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          Abstract

          Objective

          To describe the methods used to validate asthma diagnoses in electronic health records and summarize the results of the validation studies.

          Background

          Electronic health records are increasingly being used for research on asthma to inform health services and health policy. Validation of the recording of asthma diagnoses in electronic health records is essential to use these databases for credible epidemiological asthma research.

          Methods

          We searched EMBASE and MEDLINE databases for studies that validated asthma diagnoses detected in electronic health records up to October 2016. Two reviewers independently assessed the full text against the predetermined inclusion criteria. Key data including author, year, data source, case definitions, reference standard, and validation statistics (including sensitivity, specificity, positive predictive value [PPV], and negative predictive value [NPV]) were summarized in two tables.

          Results

          Thirteen studies met the inclusion criteria. Most studies demonstrated a high validity using at least one case definition (PPV >80%). Ten studies used a manual validation as the reference standard; each had at least one case definition with a PPV of at least 63%, up to 100%. We also found two studies using a second independent database to validate asthma diagnoses. The PPVs of the best performing case definitions ranged from 46% to 58%. We found one study which used a questionnaire as the reference standard to validate a database case definition; the PPV of the case definition algorithm in this study was 89%.

          Conclusion

          Attaining high PPVs (>80%) is possible using each of the discussed validation methods. Identifying asthma cases in electronic health records is possible with high sensitivity, specificity or PPV, by combining multiple data sources, or by focusing on specific test measures. Studies testing a range of case definitions show wide variation in the validity of each definition, suggesting this may be important for obtaining asthma definitions with optimal validity.

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          Most cited references32

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          Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research

          Objective To review the methods and dimensions of data quality assessment in the context of electronic health record (EHR) data reuse for research. Materials and methods A review of the clinical research literature discussing data quality assessment methodology for EHR data was performed. Using an iterative process, the aspects of data quality being measured were abstracted and categorized, as well as the methods of assessment used. Results Five dimensions of data quality were identified, which are completeness, correctness, concordance, plausibility, and currency, and seven broad categories of data quality assessment methods: comparison with gold standards, data element agreement, data source agreement, distribution comparison, validity checks, log review, and element presence. Discussion Examination of the methods by which clinical researchers have investigated the quality and suitability of EHR data for research shows that there are fundamental features of data quality, which may be difficult to measure, as well as proxy dimensions. Researchers interested in the reuse of EHR data for clinical research are recommended to consider the adoption of a consistent taxonomy of EHR data quality, to remain aware of the task-dependence of data quality, to integrate work on data quality assessment from other fields, and to adopt systematic, empirically driven, statistically based methods of data quality assessment. Conclusion There is currently little consistency or potential generalizability in the methods used to assess EHR data quality. If the reuse of EHR data for clinical research is to become accepted, researchers should adopt validated, systematic methods of EHR data quality assessment.
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            Update on asthma control in five European countries: results of a 2008 survey.

            The 2006 European National Health and Wellness Survey (NHWS) showed that a large proportion of asthmatics had uncontrolled asthma. The current analysis estimated the prevalence of asthma and asthma control (Asthma Control Test™ (ACT); QualityMetric Inc., Lincoln, RI, USA) in five European countries using the 2008 NHWS. Health-related quality of life (HRQoL), using the Short Form-12 (SF-12) health survey, and work productivity/activity impairment were assessed. Of 3,619 respondents aged ≥18 yrs, the prevalence of self-reported physician diagnosis of asthma was 6.1% (15 million people); 56.6% of treated asthmatics were not well-controlled (NWC; ACT score ≤19). Individual components of the ACT showed that, compared with at least well-controlled patients (ALWC; ACT score ≥20), NWC patients had activity limitations at least some of the time (40.8% versus 1.5%, p<0.001), were breathless ≥3 times per week (72.5% versus 5.4%, p<0.001), suffered sleep difficulties due to asthma at least once per week (60.3% versus 4.6%, p<0.001) and required rescue medication ≥2-3 times per week (77.4% versus 15.9%, p<0.001). NWC patients had also received more healthcare contact in the past 6 ;months, including hospitalisation (17.4% versus 9.9%, p<0.001). The SF-12 physical and mental summary scores were 7.46 and 4.73 points higher, respectively, for ALWC patients compared with NWC patients (p<0.001). ALWC patients reported less absenteeism (5.5% versus 12.2%) and work impairment (15.4% versus 30.0%) than NWC patients (both p<0.001). The proportion of asthmatics with NWC asthma has not improved since 2006. ALWC asthma is associated with a significant positive impact on healthcare resource use, HRQoL and work productivity.
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              Comparison of methodologies for calculating quality measures based on administrative data versus clinical data from an electronic health record system: implications for performance measures.

              New reimbursement policies and pay-for-performance programs to reward providers for producing better outcomes are proliferating. Although electronic health record (EHR) systems could provide essential clinical data upon which to base quality measures, most metrics in use were derived from administrative claims data. We compared commonly used quality measures calculated from administrative data to those derived from clinical data in an EHR based on a random sample of 125 charts of Medicare patients with diabetes. Using standard definitions based on administrative data (which require two visits with an encounter diagnosis of diabetes during the measurement period), only 75% of diabetics determined by manually reviewing the EHR (the gold standard) were identified. In contrast, 97% of diabetics were identified using coded information in the EHR. The discrepancies in identified patients resulted in statistically significant differences in the quality measures for frequency of HbA1c testing, control of blood pressure, frequency of testing for urine protein, and frequency of eye exams for diabetic patients. New development of standardized quality measures should shift from claims-based measures to clinically based measures that can be derived from coded information in an EHR. Using data from EHRs will also leverage their clinical content without adding burden to the care process.
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                Author and article information

                Journal
                Clin Epidemiol
                Clin Epidemiol
                Clinical Epidemiology
                Clinical Epidemiology
                Dove Medical Press
                1179-1349
                2017
                01 December 2017
                : 9
                : 643-656
                Affiliations
                [1 ]Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
                [2 ]National Heart and Lung Institute, Imperial College, London, UK
                [3 ]RWD & Epidemiology, GSK R&D, Uxbridge, UK
                Author notes
                Correspondence: Francis Nissen, Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK, Email francis.nissen@ 123456lshtm.ac.uk
                Article
                clep-9-643
                10.2147/CLEP.S143718
                5716672
                29238227
                58d75e56-8f3e-4280-8561-55db178ea5b1
                © 2017 Nissen et al. This work is published by Dove Medical Press Limited, and licensed under a Creative Commons Attribution License

                The full terms of the License are available at http://creativecommons.org/licenses/by/4.0/. The license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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                Categories
                Review

                Public health
                sensitivity,specificity,ppv,npv,database,validity,epidemiology
                Public health
                sensitivity, specificity, ppv, npv, database, validity, epidemiology

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