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埃塞俄比亚电子病历系统的使用及其决定因素:系统评价与荟萃分析

Electronic Medical Record System Use and Determinants in Ethiopia: Systematic Review and Meta-Analysis.

作者信息

Tegegne Masresha Derese, Wubante Sisay Maru, Kalayou Mulugeta Hayelom, Melaku Mequannent Sharew, Tilahun Binyam, Yilma Tesfahun Melese, Dessie Hiwote Simane

机构信息

Department of Health Informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.

Department of Health Informatics, School of Public Health, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia.

出版信息

Interact J Med Res. 2023 Jan 11;12:e40721. doi: 10.2196/40721.

DOI:10.2196/40721
PMID:36630161
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9878362/
Abstract

BACKGROUND

The strategic plan of the Ethiopian Ministry of Health recommends an electronic medical record (EMR) system to enhance health care delivery and streamline data systems. However, only a few exhaustive systematic reviews and meta-analyses have been conducted on the degree of EMR use in Ethiopia and the factors influencing success. This will emphasize the factors that make EMR effective and increase awareness of its widespread use among future implementers in Ethiopia.

OBJECTIVE

This study aims to determine the pooled estimate of EMR use and success determinants among health professionals in Ethiopia.

METHODS

We developed a protocol and searched PubMed, Web of Sciences, African Journals OnLine, Embase, MEDLINE, and Scopus to identify relevant studies. To assess the quality of each included study, we used the Joanna Briggs Institute quality assessment tool using 9 criteria. The applicable data were extracted using Microsoft Excel 2019, and the data were then analyzed using Stata software (version 11; StataCorp). The presence of total heterogeneity across included studies was calculated using the index of heterogeneity I statistics. The pooled size of EMR use was estimated using a random effect model with a 95% CI.

RESULTS

After reviewing 11,026 research papers, 5 papers with a combined total of 2439 health workers were included in the evaluation and meta-analysis. The pooled estimate of EMR usage in Ethiopia was 51.85% (95% CI 37.14%-66.55%). The subgroup study found that the northern Ethiopian region had the greatest EMR utilization rate (58.75%) and that higher (54.99%) utilization was also seen in publications published after 2016. Age groups <30 years, access to an EMR manual, EMR-related training, and managerial support were identified factors associated with EMR use among health workers.

CONCLUSIONS

The use of EMR systems in Ethiopia is relatively low. Belonging to a young age group, accessing an EMR manual, receiving EMR-related training, and managerial support were identified as factors associated with EMR use among health workers. As a result, to increase the use of EMRs by health care providers, it is essential to provide management support and an EMR training program and make the EMR manual accessible to health professionals.

摘要

背景

埃塞俄比亚卫生部的战略计划建议采用电子病历(EMR)系统,以改善医疗服务提供并简化数据系统。然而,关于埃塞俄比亚电子病历的使用程度及其成功的影响因素,仅进行了少数详尽的系统评价和荟萃分析。这将强调使电子病历有效的因素,并提高埃塞俄比亚未来实施者对其广泛使用的认识。

目的

本研究旨在确定埃塞俄比亚卫生专业人员中电子病历使用情况及成功决定因素的合并估计值。

方法

我们制定了一项方案,并检索了PubMed、科学网、非洲在线期刊、Embase、MEDLINE和Scopus,以识别相关研究。为评估每项纳入研究的质量,我们使用了乔安娜·布里格斯研究所质量评估工具的9项标准。适用数据使用Microsoft Excel 2019提取,然后使用Stata软件(版本11;StataCorp)进行分析。使用异质性指数I统计量计算纳入研究中总体异质性的存在情况。使用随机效应模型和95%置信区间估计电子病历使用的合并规模。

结果

在审查了11026篇研究论文后,5篇论文共涉及2439名卫生工作者被纳入评估和荟萃分析。埃塞俄比亚电子病历使用情况的合并估计值为51.85%(95%置信区间37.14%-66.55%)。亚组研究发现,埃塞俄比亚北部地区的电子病历利用率最高(58.75%),2016年后发表的文献中利用率也较高(54.99%)。年龄小于30岁、可获取电子病历手册、接受电子病历相关培训以及获得管理支持是卫生工作者中与电子病历使用相关的因素。

结论

埃塞俄比亚电子病历系统的使用相对较低。年龄较小、可获取电子病历手册、接受电子病历相关培训以及获得管理支持被确定为卫生工作者中与电子病历使用相关的因素。因此,为提高医疗服务提供者对电子病历的使用,提供管理支持和电子病历培训计划并使卫生专业人员能够获取电子病历手册至关重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b8a/9878362/8ae3a09dce0a/ijmr_v12i1e40721_fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b8a/9878362/5b4bbcbbb376/ijmr_v12i1e40721_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b8a/9878362/705fb0e60830/ijmr_v12i1e40721_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b8a/9878362/fe31acff24c9/ijmr_v12i1e40721_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b8a/9878362/85dd7f3bd69e/ijmr_v12i1e40721_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b8a/9878362/b87b2a991908/ijmr_v12i1e40721_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b8a/9878362/5ad292d88fda/ijmr_v12i1e40721_fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b8a/9878362/8ae3a09dce0a/ijmr_v12i1e40721_fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b8a/9878362/5b4bbcbbb376/ijmr_v12i1e40721_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b8a/9878362/705fb0e60830/ijmr_v12i1e40721_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b8a/9878362/fe31acff24c9/ijmr_v12i1e40721_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b8a/9878362/85dd7f3bd69e/ijmr_v12i1e40721_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b8a/9878362/b87b2a991908/ijmr_v12i1e40721_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b8a/9878362/5ad292d88fda/ijmr_v12i1e40721_fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b8a/9878362/8ae3a09dce0a/ijmr_v12i1e40721_fig7.jpg

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