This study adopted a framework development research design that combined empirical analysis with theoretical integration to develop a digital competence–based framework for evaluating EHR utilization. The research was conducted in hospitals within the Greater Bushenyi region of Uganda. The framework development process followed four main stages. First, empirical analysis was conducted to examine digital competence levels and factors influencing EHR utilization among healthcare workers. Second, relevant theoretical perspectives and digital competence frameworks were integrated to provide a conceptual foundation for the study. Third, a conceptual framework was developed based on the empirical findings and theoretical insights. Finally, the proposed framework was validated through expert consultation to ensure its clarity, relevance, and practical applicability in hospital environments.

Ethical approval for this study was obtained from the Kampala International University Research Ethics Committee (KIU-REC) and the Uganda National Council for Science and Technology (UNCST) prior to the commencement of data collection.

Participation in the study was entirely voluntary. Written informed consent was obtained from all participants before their involvement in the research. Participants were provided with adequate information regarding the purpose of the study, the procedures involved, the expected benefits and potential risks, and their right to decline participation or withdraw from the study at any time without any adverse consequences.

The framework integrates insights from multiple theoretical perspectives; Self-Determination Theory (SDT) explains the motivational factors influencing technology adoption. The theory emphasizes competence, autonomy, and relatedness as key drivers of human behavior ( Gagné et al., 2022). Digital competence models conceptualize digital capability as consisting of technical, cognitive, and socio-emotional skills required to interact with digital systems ( Vuorikari et al., 2025). The Digital Competence Framework for Citizens (DigComp) provides structured domains of digital competence including information literacy, communication, safety, and problem-solving skills. Competency framework models translate digital skills into measurable professional performance indicators ( Bouwmans et al., 2024). Together, these perspectives explain how both motivation and capability development influence EHR utilization as shown in Table 1.

According to Figure 1. The framework combines several theories to explain how healthcare workers develop the skills needed to use EHR systems effectively. SDT explains the motivational factors that influence healthcare workers’ willingness to use digital technologies, focusing on competence, autonomy, and support in the workplace. The Digital Competence Model describes the types of skills required to use digital systems, including technical, cognitive, and socio-emotional skills. The DigComp Framework further defines key digital skill areas such as information literacy, communication, safety, and problem solving. Together, these theories guide the proposed framework for digital competence development, which emphasizes improving health workers’ digital competence and providing workplace support through training and feedback. These elements help promote effective EHR utilization in hospital settings.

The proposed digital competence–based framework conceptualizes EHR utilization as a multidimensional process shaped by the interaction of four key determinants: human factors, organizational factors, technical factors, and system factors. These determinants do not operate in isolation; rather, they interact dynamically to influence healthcare workers’ digital competence, which in turn determines the effectiveness of EHR utilization and subsequent healthcare service delivery outcomes.

At the foundational level, human factors, including knowledge, skills, attitudes, and motivation, directly influence the ability of healthcare workers to engage with EHR systems. However, the development and expression of these human capabilities are strongly moderated by organizational factors, such as training opportunities, leadership support, workflow design, and institutional policies. For instance, even highly skilled healthcare workers may underutilize EHR systems in environments where training is insufficient or where organizational support is weak.

Technical factors, including system usability, accessibility of devices, and availability of technical support, further shape how effectively healthcare workers can translate their competence into practice. Poor system design or frequent technical failures can hinder even competent users, reducing efficiency and increasing the likelihood of errors.

In addition, system factors, such as infrastructure reliability, system integration, data quality, and interoperability, create the broader technological environment within which EHR utilization occurs. These factors determine whether digital tools can support seamless clinical workflows and data-driven decision-making.

The interaction among these four determinants is therefore synergistic: improvements in one domain can enhance or constrain outcomes in another. For example, investments in infrastructure (system factors) must be complemented by training (organizational factors) to produce meaningful improvements in competence and utilization.

Within this framework, digital competence functions as the central mediating mechanism that links EHR utilization determinants to healthcare service delivery outcomes. Digital competence is conceptualized as a composite of technical skills, cognitive abilities, and socio-behavioral capabilities required to effectively use digital systems in clinical practice.

The centrality of digital competence is grounded in both empirical findings and theoretical perspectives. From a theoretical standpoint, Self-Determination Theory emphasizes competence as a key driver of intrinsic motivation and effective performance. Similarly, digital competence frameworks such as DigComp highlight the multidimensional nature of digital skills required for meaningful technology use.

Empirically, the findings of this study demonstrate that human factors, closely aligned with competence are the strongest predictors of healthcare service delivery outcomes. This indicates that the presence of digital infrastructure alone is insufficient; rather, it is the ability of healthcare workers to effectively utilize these systems that ultimately determines their impact.

Digital competence therefore acts as a conversion mechanism, transforming available resources (technology, organizational support, infrastructure) into effective EHR utilization practices. Without sufficient competence, the potential benefits of digital health systems remain unrealized.

The framework further introduces digital competence levels (low, moderate, high) as a practical tool for assessing workforce readiness and guiding targeted interventions. These competence levels enable healthcare institutions to identify specific gaps and tailor interventions accordingly.

At the low competence level, interventions focus on foundational digital literacy, basic system navigation, and confidence-building measures. At the moderate level, interventions emphasize advanced system functionalities, data interpretation, and problem-solving skills. At the high competence level, interventions shift towards optimization, including clinical decision support utilization, data analytics, and leadership in digital health practices.

Interventions are implemented through multiple pathways, including structured training programs, continuous professional development, mentorship, and system usability improvements. Importantly, the framework recognizes that interventions must be aligned with both organizational and technical contexts to be effective.

The framework also supports continuous feedback and iterative improvement, where competence assessment informs intervention design, and intervention outcomes are evaluated to refine strategies over time. This creates a dynamic cycle of competence development and performance enhancement.

The ultimate outcome of this framework is improved healthcare service delivery, reflected in enhanced documentation accuracy, better clinical decision-making, improved coordination of care, and increased efficiency. By positioning digital competence at the center of this pathway, the framework provides a structured approach for transforming digital health investments into measurable healthcare improvements. Overall, Figure 6 shows the proposed model offers a comprehensive and practical tool for understanding and improving EHR utilization by integrating human capability development with organizational and technological systems.

The results of this study are consistent with prior research highlighting the critical role of human and organizational factors in the successful adoption and utilization of digital health systems ( Melkas et al., 2020). Previous studies have shown that despite significant investments in EHR infrastructure, actual system use remains suboptimal due to gaps in user competence and inadequate training ( Liu et al., 2020). For example, Tsai et al. (2020) identified limited user skills and resistance to technology as key barriers to effective EHR utilization ( Milenkovic et al., 2020), while ( Mwogosi, 2025) emphasized usability challenges and provider dissatisfaction as major constraints in low-resource settings.

However, this study extends existing literature by explicitly positioning digital competence as a central mediating construct that links EHR utilization determinants to healthcare service delivery outcomes ( McMullan, 2018). Unlike many previous studies that examine adoption factors in isolation, the proposed framework integrates human, organizational, technical, and system factors into a unified model, thereby providing a more comprehensive understanding of EHR utilization dynamics.

One of the most significant findings of this study is that human factors emerged as the strongest predictor of healthcare service delivery, accounting for a substantial proportion of the observed variance. This finding underscores the fundamental role of healthcare workers’ knowledge, skills, and motivation in determining the effectiveness of digital health systems.

The dominance of human factors can be explained through both theoretical and practical perspectives. From a theoretical standpoint, Self-Determination Theory suggests that competence is a key driver of performance and engagement ( Ryan & Deci, 2000). Healthcare workers who feel confident in their digital abilities are more likely to actively engage with EHR systems, explore advanced functionalities, and integrate digital tools into their clinical workflows.

From a practical perspective, EHR systems are inherently user-dependent technologies ( Tutty et al., 2019). Unlike fully automated systems, their effectiveness relies on accurate data entry, proper navigation, and informed interpretation by users. Even in environments with well-developed infrastructure, low levels of digital competence can lead to underutilization, data quality issues, and inefficiencies. Conversely, highly competent users can maximize system functionality even in resource-constrained settings.

This finding reinforces the argument that investments in digital health infrastructure must be complemented by deliberate efforts to build human capacity. Without such investments, the potential benefits of EHR systems may not be fully realized.

The findings of this study have important implications for healthcare systems in low- and middle-income countries, including Uganda. In many LMIC contexts, digital health initiatives are often driven by technology acquisition, with less emphasis placed on workforce readiness and competence development. This imbalance can result in a gap between system availability and effective utilization.

The proposed framework addresses this gap by providing a structured approach to assessing and improving digital competence among healthcare workers. By identifying competence levels and linking them to targeted interventions, the framework enables healthcare institutions to adopt a more strategic and sustainable approach to digital health implementation.

In the Ugandan context, where healthcare systems face challenges related to limited resources, workforce shortages, and infrastructural constraints, strengthening digital competence offers a cost-effective strategy for improving service delivery. Rather than focusing solely on expanding technological infrastructure, policymakers and healthcare managers can achieve significant gains by investing in training, mentorship, and continuous professional development.

Furthermore, the framework aligns with broader digital health strategies aimed at improving data quality, enhancing clinical decision-making, and promoting integrated care.

This study makes both theoretical and practical contributions. Theoretically, it advances understanding of EHR utilization by integrating multiple perspectives, Self-Determination Theory, digital competence models, and competency-based frameworks into a unified conceptual model. Practically, it provides a validated tool that healthcare institutions can use to assess workforce readiness, identify competence gaps, and design targeted interventions.

Overall, the findings emphasize that effective EHR utilization is not solely a technological issue but a human-centered process that requires the alignment of skills, motivation, organizational support, and system design. By placing digital competence at the center of this process, the proposed framework offers a robust and actionable approach for improving healthcare service delivery through digital health systems.