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Health professionals’ readiness to implement electronic medical record system in Gamo zone public hospitals, southern Ethiopia: an institution based cross-sectional study

2023·12 Zitationen·BMC Health Services ResearchOpen Access
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12

Zitationen

4

Autoren

2023

Jahr

Abstract

BACKGROUND: The adoption of Electronic Medical Records (EMR) by the healthcare sector can improve patient care and safety, facilitate structured research, and effectively plan, monitor, and assess disease. EMR adoptions in low-income countries like Ethiopia were delayed and failing more frequently, despite their critical necessity. The most popular way to solve the issue is to evaluate user preparedness prior to the adoption of EMR. However, little is known regarding the EMR readiness of healthcare professionals in this study setting. Therefore, the objective of this study was to assess the readiness and factors associated with health professional readiness toward EMR in Gamo Zone, Ethiopia. METHODS: An institution-based cross-sectional survey was conducted by using a pretested self-administered questionnaire on 416 study participants at public hospital hospitals in southern Ethiopia. STAT version 14 software was used to conduct the analysis after the data was entered using Epi-data version 3.2. A binary logistic regression model was fitted to identify factors associated with readiness. Finally, the results were interpreted using an adjusted odds ratio (AOR) with a 95% confidence interval (CI) and p-value less than 0.05. RESULTS: A total of 400 participants enrolled in the study, with a response rate of 97.1%. A total of 65.25% (n = 261) [95% CI: 0.60, 0.69] participants had overall readiness, 68.75% (n = 275) [95% CI: 0.64, 0.73] had engagement readiness, and (69.75%) (n = 279) [95% CI: 0.65, 0.74] had core EMR readiness. Computer skills (AOR: 3.06; 95% CI: 1.49-6.29), EMR training (AOR: 2.00; 95% CI: 1.06-3.67), good EMR knowledge (AOR: 2.021; 95% CI: 1.19-3.39), and favorable attitude (AOR: 3.00; 95% CI: 1.76-4.97) were factors significantly associated with EMR readiness. CONCLUSION: Although it was deemed insufficient, more than half of the respondents indicated a satisfactory level of overall readiness for the adoption of EMR. Moreover, having computer skills, having EMR training, good EMR knowledge, and favorable EMR attitude were all significantly and positively related to EMR readiness.

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Institutionen

Themen

Electronic Health Records SystemsArtificial Intelligence in Healthcare and EducationMachine Learning in Healthcare
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