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. 2021 May 4;22(1):325.
doi: 10.1186/s13063-021-05259-9.

The effect of automated audit and feedback on data completeness in the electronic health record of the general physician: protocol for a cluster randomized controlled trial

Steve Van den Bulck #  1 , Tine De Burghgraeve #  2 , Willem Raat  2 , Pavlos Mamouris  2 , Patrick Coursier  2 , Patrik Vankrunkelsven  2 , Geert Goderis  2 , Rosella Hermens  3 , Gijs Van Pottelbergh  2 , Bert Vaes  2
Affiliations

The effect of automated audit and feedback on data completeness in the electronic health record of the general physician: protocol for a cluster randomized controlled trial

Steve Van den Bulck et al. Trials. .

Abstract

Background: The electronic health record (EHR) of the general physician (GP) is an important tool that can be used to assess and improve the quality of healthcare. However, there are some problems when (re) using the data gathered in the EHR for quality assessments. One problem is the lack of data completeness in the EHR. Audit and feedback (A&F) is a well-known quality intervention that can improve the quality of healthcare. We hypothesize that an automated A&F intervention can be adapted to improve the data completeness of the EHR of the GP, more specifically, the number of correctly registered diagnoses of type 2 diabetes and chronic kidney disease.

Methods: This study is a pragmatic cluster randomized controlled trial with an intervention at the level of GP practice. The intervention consists of an audit and extended electronically delivered feedback with multiple components that will be delivered 4 times electronically to general practices over 12 months. The data will be analyzed on an aggregated level (per GP practice). The primary outcome is the percentage of correctly registered diagnoses of type 2 diabetes. The key secondary outcome is the registration of chronic kidney disease. Exploratory secondary outcomes are the registration of heart failure, biometric data and lifestyle habits, and the evolution of 4 different EHR-extractable quality indicators.

Discussion: This cluster randomized controlled trial intends to primarily improve the registration of type 2 diabetes in the EHR of the GP and to secondarily improve the registration of chronic kidney disease. In addition, the registration of heart failure, lifestyle parameters, and biometric data in the EHR of the GP are explored together with 4 EHR-extractable quality indicators. By doing so, this study aims to improve the data completeness of the EHR, paving the way for future quality assessments.

Trial registration: ClinicalTrials.gov NCT04388228 . Registered on May 14, 2020.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Time schedule. DM 2, diabetes mellitus type 2; CKD, chronic kidney disease; EHR, electronic health record
See this image and copyright information in PMC

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References

    1. Campanella P, Lovato E, Marone C, Fallacara L, Mancuso A, Ricciardi W, Specchia ML. The impact of electronic health records on healthcare quality: a systematic review and meta-analysis. Eur J Pub Health. 2016;26(1):60–64. doi: 10.1093/eurpub/ckv122. - DOI - PubMed
    1. Chan KS, Fowles JB, Weiner JP. Review: electronic health records and the reliability and validity of quality measures: a review of the literature. Med Care Res Rev. 2010;67(5):503–527. doi: 10.1177/1077558709359007. - DOI - PubMed
    1. Roth CP, Lim YW, Pevnick JM, Asch SM, McGlynn EA. The challenge of measuring quality of care from the electronic health record. Am J Med Qual. 2009;24(5):385–394. doi: 10.1177/1062860609336627. - DOI - PubMed
    1. Madden JM, Lakoma MD, Rusinak D, Lu CY, Soumerai SB. Missing clinical and behavioral health data in a large electronic health record (EHR) system. J Am Med Inform Assoc. 2016;23(6):1143–1149. doi: 10.1093/jamia/ocw021. - DOI - PMC - PubMed
    1. Weiskopf NG, Hripcsak G, Swaminathan S, Weng C. Defining and measuring completeness of electronic health records for secondary use. J Biomed Inform. 2013;46(5):830–836. doi: 10.1016/j.jbi.2013.06.010. - DOI - PMC - PubMed

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