Objectives Personal health record systems allow users to manage their health information in a confidential manner. However, there is little evidence about healthcare providers' intentions to use such technologies in resource-limited settings. Therefore, this study aimed to assess predicting healthcare providers' acceptance of electronic personal health record systems. Methods An institutional-based cross-sectional study was conducted from 19 July to 23 August 2022 at teaching hospitals in the Amhara regional state of Ethiopia. A total of 638 health professionals participated in the study. Simple random sampling techniques were used to select the study participants. Structural equation modelling analysis was employed using AMOS V.26 software. Result Perceived ease of use had a significant effect on the intention to use electronic personal health records (β=0. 377, p<0.01), perceived usefulness (β=0.104, p<0.05) and attitude (β=0.204, p<0.01); perceived ease of use and information technology experience had a significant effect on perceived usefulness (β=0.077, p<0.05); and digital literacy (β=0.087, p<0.05) and attitude had also a strong effect on intention to use electronic personal health records (β=0.361, p<0.01). The relationship between perceived ease of use and the intention to use was mediated by attitude (β=0.076, p<0.01). Conclusion Perceived ease of use, attitude and digital literacy had a significant effect on the intention to use electronic personal health records. The perceived ease of use had a greater influence on the intention to use electronic personal health record systems. Thus, capacity building and technical support could enhance health providers' acceptance of using electronic personal health records in Ethiopia.
CITATION STYLE
Walle, A. D., Ferede, T. A., Baykemagn, N. D., Shimie, A. W., Kebede, S. D., Tegegne, M. D., … Mengistie, M. B. (2023). Predicting healthcare professionals’ acceptance towards electronic personal health record systems in a resource-limited setting: using modified technology acceptance model. BMJ Health and Care Informatics, 30(1). https://doi.org/10.1136/bmjhci-2022-100707
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