Department of Health Informatics, School of Public Health, College of Medicine and Health Sciences, Woldia University, Woldia, P.O.Box: 400, Ethiopia.
Department of Health Informatics, School of Public Health, College of Medicine and Health Sciences, Hawassa University, Hawassa, Ethiopia.
BMC Health Serv Res. 2024 Aug 3;24(1):889. doi: 10.1186/s12913-024-11378-1.
BACKGROUND: The implementation of Electronic Health Record (EHR) systems is a critical challenge, particularly in low-income countries, where behavioral intention plays a crucial role. To address this issue, we conducted a study to extend and apply the Unified Theory of Acceptance and Use of Technology 3 (UTAUT3) model in predicting health professionals' behavioral intention to use EHR systems. METHODS: A quantitative research approach was employed among 423 health professionals in Southwest Ethiopia. We assessed the validity of the proposed model through measurement and structural model statistics. Analysis was done using SPSS AMOS version 23. Hypotheses were tested using structural equation modeling (SEM) analysis, and mediation and moderation effects were evaluated. The associations between exogenous and endogenous variables were examined using standardized regression coefficients (β), 95% confidence intervals, and p-values, with a significance level of p-value < 0.05. RESULTS: The proposed model outperformed previous UTAUT models, explaining 84.5% (squared multiple correlations (R) = 0.845) of the variance in behavioral intention to use EHR systems. Personal innovativeness (β = 0.215, p-value < 0.018), performance expectancy (β = 0.245, p-value < 0.001), and attitude (β = 0.611, p-value < 0.001) showed significant associations to use EHR systems. Mediation analysis revealed that performance expectancy, hedonic motivation, and technology anxiety had significant indirect effects on behavioral intention. Furthermore, moderation analysis indicated that gender moderated the association between social influence, personal innovativeness, and behavioral intention. CONCLUSION: The extended UTAUT3 model accurately predicts health professionals' intention to use EHR systems and provides a valuable framework for understanding technology acceptance in healthcare. We recommend that digital health implementers and concerned bodies consider the comprehensive range of direct, indirect, and moderating effects. By addressing personal innovativeness, performance expectancy, attitude, hedonic motivation, technology anxiety, and the gender-specific impact of social influence, interventions can effectively enhance behavioral intention toward EHR systems. It is crucial to design gender-specific interventions that address the differences in social influence and personal innovativeness between males and females.
背景：电子健康记录（EHR）系统的实施是一个关键挑战，特别是在低收入国家，行为意向起着至关重要的作用。为了解决这个问题，我们进行了一项研究，旨在扩展和应用统一技术接受和使用理论 3（UTAUT3）模型，以预测卫生专业人员使用 EHR 系统的行为意向。
方法：在埃塞俄比亚西南部的 423 名卫生专业人员中采用了定量研究方法。我们通过测量和结构模型统计评估了所提出模型的有效性。使用 SPSS AMOS 版本 23 进行分析。使用结构方程建模（SEM）分析测试假设，并评估中介和调节效应。使用标准化回归系数（β）、95%置信区间和 p 值检验外生和内生变量之间的关联，显著性水平为 p 值 < 0.05。
结果：所提出的模型优于以前的 UTAUT 模型，解释了 EHR 系统使用行为意向的 84.5%（平方多重相关系数（R）= 0.845）的方差。个人创新性（β=0.215，p 值 < 0.018）、绩效预期（β=0.245，p 值 < 0.001）和态度（β=0.611，p 值 < 0.001）与使用 EHR 系统有显著关联。中介分析表明，绩效预期、享乐动机和技术焦虑对行为意向有显著的间接影响。此外，调节分析表明，性别调节了社会影响、个人创新性和行为意向之间的关联。
结论：扩展的 UTAUT3 模型准确预测了卫生专业人员使用 EHR 系统的意图，并为理解医疗保健中的技术接受提供了有价值的框架。我们建议数字健康实施者和有关机构考虑直接、间接和调节效应的综合范围。通过解决个人创新性、绩效预期、态度、享乐动机、技术焦虑以及社会影响的性别特定影响，可以有效地增强对 EHR 系统的行为意向。设计针对男性和女性之间社会影响和个人创新性差异的性别特定干预措施至关重要。
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