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A Comparison of Electronic Medical Record Data to Paper Records in Antiretroviral Therapy Clinic in Ethiopia: What is affecting the Quality of the Data?

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dc.contributor.author Abiy, Rahel
dc.contributor.author Gashu, Kassahun
dc.contributor.author Asemaw, Tarekegn
dc.contributor.author Mitiku, Mebratu
dc.contributor.author Fekadie, Berhanu
dc.contributor.author Abebaw, Zeleke
dc.contributor.author Mamuye, Adane
dc.contributor.author Tazebew, Ashenafi
dc.contributor.author Teklu, Alemayehu
dc.contributor.author Nurhussien, Fedilu
dc.contributor.author Kebede, Mihiretu
dc.contributor.author Fritz, Fleur
dc.contributor.author Tilahun, Binyam
dc.date.accessioned 2021-05-17T12:26:17Z
dc.date.available 2021-05-17T12:26:17Z
dc.date.issued 2018
dc.identifier.uri http://hdl.handle.net/123456789/3550
dc.description.abstract Background: Anti-Retroviral Therapy (ART) care is a lifelong treatment, which needs accurate and reliable data collected for long period of time. Poor quality of medical records data remains a challenge and is directly related to the quality of care of patients. To improve this, there is an increasing trend to implement electronic medical record (EMR) in hospitals. However, there is little evidence on the impact of EMR on the quality of health data in low- resource setting hospitals like Ethiopia. This comparative study aims to fill this evidence gap by assessing the completeness and reliability of paper-based and electronic medical records and explore the challenges of ensuring data quality at the Anti-Retroviral Therapy (ART) clinic at the University of Gondar Referral Hospital in Northwest Ethiopia. Methods: An institution-based comparative cross-sectional study, supplemented with a qualitative approach was conducted from February 1 to March 30, 2017 at the ART clinic of the University of Gondar Hospital. A total of 250 medical records having both electronic and paper-based versions were collected and assessed. A national ART registration form which consists of 40 ART data elements was used as a checklist to assess completeness and reliability dimensions of data quality on medical records of patients on HIV care. Kappa statistics were computed to describe the level of data agreement between paper-based and electronic records across patient characteristics. In-depth interviews were conducted using semi-structured questionnaires with ten key informants to explore the challenges related with the quality of medical records. Responses of the key informant interviews were analyzed using thematic analysis. Results: The overall completeness of medical records was 78% with 95% CI (70.8% - 85.1%) in paper-based and 76% with 95%CI (67.8% - 83.2%) EMR. The data reliability measured in Kappa statistics shows strong agreements on the socio-demographic data such as educational status 0.93 (0.891, 0.963), WHO staging 0.86 (0.808, 0.906); general appearance 0.83 (0.755, 0.892) and patient referral record 0.87 (0.795, 0.932). The major challenges hindering good data quality was the current side by side dual data documentation practice (the need to document both on the paper and the EMR for a single record), patient overload and low data documentation practice of health workers. Conclusion: The overall completeness of ART medical records was still slightly better in paper-based records than EMR. The main reason affecting the EMR data quality was the current dual documentation practice both on the paper and electronic for each patient in the hospital and the high load of patients in the clinic. The hospital management need to decide to use either the paper or the electronic system and build the capacity of health workers to improve data quality in the hospital. en_US
dc.description.sponsorship UOG en_US
dc.language.iso en en_US
dc.publisher Online Journal of Public Health Informatics en_US
dc.relation.ispartofseries Jornal;
dc.subject ART data, EMR, data quality, Ethiopia, developing countries en_US
dc.title A Comparison of Electronic Medical Record Data to Paper Records in Antiretroviral Therapy Clinic in Ethiopia: What is affecting the Quality of the Data? en_US
dc.type Article en_US


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