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digital health competence, healthcare provider performance, work engagement, workload management, PLS-SEM, digital transformation
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Albeah AM and Hassan HC. Digital Health Competence, Workload Management, and Healthcare Provider Performance: The Mediating Role of Work Engagement [version 1; peer review: awaiting peer review]. F1000Research 2026, 15:1133 (https://doi.org/10.12688/f1000research.186050.1)
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Research Article
Digital Health Competence, Workload Management, and Healthcare Provider Performance: The Mediating Role of Work Engagement
[version 1; peer review: awaiting peer review]
PUBLISHED 11 Jul 2026
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This article is included in the Health Services gateway.
Abstract
Corresponding author:
Ali Mohammed Albeah
Competing interests:
No competing interests were disclosed.
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The author(s) declared that no grants were involved in supporting this work.
Copyright:
© 2026 Albeah AM and Hassan HC.
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
How to cite: Albeah AM and Hassan HC. Digital Health Competence, Workload Management, and Healthcare Provider Performance: The Mediating Role of Work Engagement [version 1; peer review: awaiting peer review]. F1000Research 2026, 15:1133 (https://doi.org/10.12688/f1000research.186050.1)
First published: 11 Jul 2026, 15:1133 (https://doi.org/10.12688/f1000research.186050.1)
Latest published: 11 Jul 2026, 15:1133 (https://doi.org/10.12688/f1000research.186050.1)
Introduction
Healthcare systems worldwide are undergoing rapid digital transformation driven by the widespread implementation of electronic health records, telemedicine, artificial intelligence, clinical decision-support systems, and other digital health technologies. These innovations have fundamentally changed the way healthcare services are delivered by improving access to care, facilitating clinical decision-making, enhancing communication among healthcare professionals, and supporting patient safety. At the same time, digital transformation has increased expectations for healthcare organizations to maintain high-quality, efficient, and patient-centered services while adapting to increasingly complex clinical and organizational environments. Consequently, healthcare provider performance has become a strategic priority for healthcare organizations seeking to achieve sustainable health system improvement. Performance in healthcare is no longer determined solely by professional knowledge and clinical skills but also by healthcare providers’ ability to adapt to technological change and increasingly demanding workplace conditions (WHO, 2021; Westbrook et al., 2020; Wirtz et al., 2021).
One important factor supporting healthcare workforce performance is digital health competence. Digital health competence refers to healthcare professionals’ ability to effectively and responsibly use digital technologies, interpret health information, communicate through digital platforms, and integrate technology into clinical practice. As healthcare organizations increasingly rely on digital systems, healthcare providers require competencies that extend beyond technical proficiency to include information literacy, digital communication, critical thinking, and continuous learning. Previous studies have shown that higher levels of digital competence facilitate technology adoption, improve workflow efficiency, reduce clinical errors, and enhance organizational performance (Kruse et al., 2020; Kane et al., 2021; Venkatesh et al., 2022; Zhang et al., 2022). Furthermore, organizations with digitally competent healthcare professionals are more capable of sustaining digital transformation initiatives while maintaining high-quality healthcare delivery (Meijerink et al., 2022; Yu et al., 2025).
Despite advances in healthcare technology, workload remains one of the most significant challenges affecting healthcare providers’ performance. Increasing patient complexity, workforce shortages, administrative responsibilities, and digital documentation requirements have substantially increased physical, cognitive, and emotional job demands. According to the Job Demands–Resources (JD-R) theory, excessive job demands without adequate organizational resources may reduce motivation, contribute to burnout, and ultimately impair work performance (Bakker & Demerouti, 2020). Conversely, effective workload management enables healthcare organizations to balance job demands with available resources, thereby improving employee well-being and organizational effectiveness. Previous research has consistently demonstrated that well-managed workloads are associated with lower occupational stress, greater job satisfaction, improved patient safety, and better healthcare outcomes (Dall’Ora et al., 2020; Marufu et al., 2021; Montgomery et al., 2021).
In addition to organizational and technological factors, work engagement has emerged as an important psychological mechanism explaining variations in healthcare provider performance. Work engagement is characterized by vigor, dedication, and absorption, reflecting a positive motivational state that encourages employees to invest cognitive, emotional, and physical resources in their work. Highly engaged healthcare professionals demonstrate greater commitment to patient care, stronger organizational attachment, and higher levels of productivity. Previous evidence indicates that work engagement not only directly improves individual performance but also mediates the relationship between workplace resources and performance outcomes (Saks, 2019; Barello et al., 2021; Salanova et al., 2021). Within digitally transforming healthcare organizations, employees who possess sufficient resources, including digital competence and supportive workload management, are more likely to develop stronger work engagement, which subsequently enhances their performance.
Although previous studies have independently examined digital competence, workload management, and work engagement, limited evidence has integrated these constructs into a single conceptual framework to explain healthcare provider performance, particularly within tertiary hospitals undergoing rapid digital transformation in Saudi Arabia. Existing research has primarily focused on direct relationships between technological capabilities and organizational outcomes, with relatively less attention given to the psychological mechanisms through which digital competence and workload management influence performance. Addressing this gap may provide a more comprehensive understanding of how technological, organizational, and psychological resources jointly contribute to healthcare workforce performance. Therefore, this study aimed to examine the effects of digital health competence and workload management on healthcare provider performance, with work engagement as a mediating variable among healthcare providers at King Abdullah Medical City, Saudi Arabia. By integrating these variables within a single structural model, this study contributes to the growing literature on healthcare workforce management and digital transformation while providing evidence to support organizational strategies for improving healthcare service quality.
Methods
Study design
This study employed a quantitative cross-sectional design to examine the relationships among digital health competence, workload management, work engagement, and healthcare provider performance. A cross-sectional approach was considered appropriate because it enabled the simultaneous assessment of these variables within a real-world healthcare setting. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to evaluate both the direct and indirect relationships proposed in the conceptual model. Data analysis was performed using IBM SPSS Statistics version 26 for descriptive analyses and SmartPLS version 4 for measurement and structural model evaluation.
Study setting
The study was conducted at King Abdullah Medical City (KAMC), a tertiary referral hospital within the Makkah Health Cluster, Saudi Arabia. KAMC is one of the country’s leading specialist hospitals, providing highly specialized healthcare services across multiple clinical disciplines. As part of Saudi Vision 2030 and the national healthcare transformation programme, the hospital has extensively implemented digital health technologies, including electronic health records, digital clinical decision-support systems, and other health information technologies. These ongoing digital transformation initiatives make KAMC an appropriate setting for examining the relationships among digital health competence, workload management, work engagement, and healthcare provider performance.
Participants and sampling
The target population comprised all clinical healthcare providers employed at KAMC who were directly involved in patient care or clinical support services. According to institutional workforce records obtained in September 2025, KAMC employed 2,638 healthcare professionals, including physicians, nurses, and health technicians.
Participants were selected using stratified random sampling based on professional categories, including physicians, nurses, and health technicians. This sampling approach was intended to ensure representation from the major professional groups involved in clinical service delivery at King Abdullah Medical City.
Sample size
The minimum required sample size was calculated using Cochran’s formula for finite populations, resulting in a minimum sample of 336 participants. To increase statistical power and compensate for potential incomplete responses, a total of 450 eligible healthcare providers participated in the study and were included in the final analysis.
Eligibility criteria
Inclusion criteria
Participants were eligible if they: were physicians, nurses, or health technicians actively employed at KAMC; had at least six months of working experience at the hospital; and were directly involved in patient care or clinical support services.
Exclusion criteria
Healthcare providers were excluded if they: were interns or trainees; had worked at the hospital for less than six months; or were employed exclusively in non-clinical administrative positions.
Study variables and measurements
Four constructs were evaluated in this study using standardized self-administered questionnaires.
Digital health competence
Digital health competence was measured using a 12-item instrument adapted from Jarva et al. (2023), which demonstrated satisfactory psychometric properties in previous validation studies. The questionnaire assessed healthcare providers’ competencies in electronic medical record utilization, digital communication, information evaluation, data security, and adaptation to healthcare technologies. Responses were recorded using a four-point Likert scale, with higher scores indicating greater digital health competence.
Workload management
Workload management was assessed using a 12-item instrument adapted from the Copenhagen Psychosocial Questionnaire III (COPSOQ III) and the Practice Environment Scale. The instrument evaluated job demands, staffing adequacy, workload distribution, and resource availability. Responses were rated on a five-point Likert scale, with higher scores representing more effective workload management.
Work engagement
Work engagement was measured using the Utrecht Work Engagement Scale (UWES-9), which evaluates three dimensions: vigor, dedication, and absorption. Participants responded using a seven-point frequency scale ranging from “never” to “always,” with higher scores indicating greater work engagement.
Healthcare provider performance
Healthcare provider performance was assessed using the Individual Work Performance Questionnaire (IWPQ), which measures task performance, contextual performance, and counterproductive work behaviour. Higher scores reflected better overall work performance.
Data collection
Data were collected using an anonymous online questionnaire administered through Google Forms. Prior to participation, eligible healthcare providers received an electronic participant information sheet describing the study objectives, study procedures, voluntary participation, confidentiality, and their right to withdraw from the study at any time without any consequences. Electronic written informed consent was obtained from all participants before they were able to access and complete the questionnaire by requiring them to indicate their agreement electronically.
The questionnaire consisted of validated instruments measuring digital health competence, workload management, work engagement, and healthcare provider performance. In addition, demographic information, including participants’ sex, age, profession, educational level, and years of work experience, was collected.
To minimize information bias, all participants completed the questionnaire anonymously using standardized self-administered instruments. Questionnaires with incomplete responses were excluded from the analysis. All completed questionnaires were stored securely and used exclusively for research purposes.
Statistical analysis
Descriptive statistics were calculated to summarize participants’ demographic characteristics and study variables. The measurement model was evaluated by examining indicator reliability, internal consistency reliability (Cronbach’s alpha and composite reliability), convergent validity using the average variance extracted (AVE), and discriminant validity using the heterotrait–monotrait (HTMT) ratio.
The structural model was subsequently evaluated by assessing multicollinearity using variance inflation factors (VIF), path coefficients (β), coefficients of determination (R2), effect sizes (f2), and predictive relevance (Q2). Statistical significance was determined using a bootstrapping procedure with 5,000 resamples. The mediating effect of work engagement was evaluated by examining indirect effects and the variance accounted for (VAF) to determine the extent of mediation.
Results
Participant characteristics
A total of 450 healthcare providers were included in the final analysis. As shown in Table 1, 238 (52.9%) participants were female and 212 (47.1%) were male. Nearly half of the respondents were aged 26–35 years (49.6%), followed by those aged 36–45 years (36.4%) and 46 years or older (14.0%). Nurses represented the largest professional group (65.6%), followed by physicians (25.3%) and health technicians (9.1%). Most participants were Saudi nationals (70.0%) and held a bachelor’s degree (54.4%). Regarding clinical experience, the largest proportion had 4–5 years of experience (25.1%), followed by those with 6–7 years (23.3%) and 8 years or more (23.3%).
Table 1. Demographic and professional characteristics of the participants.
Descriptive statistics
The descriptive statistics indicated moderate levels across all study constructs. Digital health competence had a mean score of 29.68 (SD = 10.31), workload management had a mean of 35.59 (SD = 13.05), work engagement had a mean of 26.84 (SD = 13.40), and healthcare provider performance had a mean of 35.43 (SD = 19.88). Overall, these findings suggest moderate perceptions of digital competence, workload management, work engagement, and healthcare provider performance among healthcare professionals at King Abdullah Medical City.
Measurement model assessment
The measurement model demonstrated satisfactory psychometric properties. All indicator outer loadings exceeded the recommended threshold of 0.70, ranging from 0.703 to 0.813. Internal consistency reliability was established, with Cronbach’s alpha ranging from 0.902 to 0.963 and composite reliability ranging from 0.920 to 0.967. Convergent validity was supported by average variance extracted (AVE) values ranging from 0.560 to 0.616. Furthermore, discriminant validity was confirmed using both the Fornell–Larcker criterion and the heterotrait–monotrait (HTMT) ratio, with all HTMT values below the recommended threshold of 0.85. These findings indicate that the measurement model possessed adequate reliability and validity for structural model evaluation ( Table 2).
Structural model assessment
The structural model demonstrated moderate explanatory power and satisfactory predictive capability. The model explained 49.5% of the variance in work engagement (R2 = 0.495) and 57.9% of the variance in healthcare provider performance (R2 = 0.579). Effect size analysis indicated that digital health competence and workload management exerted substantial effects on work engagement (f2 = 0.386 and 0.372, respectively), whereas work engagement showed a moderate effect on healthcare provider performance (f2 = 0.169). Predictive relevance analysis further demonstrated positive Q2 values for work engagement (Q2 = 0.488) and healthcare provider performance (Q2 = 0.502), indicating satisfactory predictive capability of the proposed model.
Hypothesis testing
The bootstrapping results demonstrated that all proposed hypotheses were supported ( Table 3). Digital health competence significantly influenced healthcare provider performance (β = 0.285, t = 7.976, p < 0.001) and work engagement (β = 0.453, t = 13.121, p < 0.001). Likewise, workload management significantly affected healthcare provider performance (β = 0.289, t = 7.434, p < 0.001) and work engagement (β = 0.445, t = 13.522, p < 0.001). Furthermore, work engagement exerted a significant positive effect on healthcare provider performance (β = 0.375, t = 8.471, p < 0.001). Accordingly, all five direct hypotheses were supported.
Table 3. Results of hypothesis testing.
Mediation analysis
The mediation analysis demonstrated that work engagement significantly mediated the relationships between digital health competence and healthcare provider performance (β = 0.170, t = 7.081, p < 0.001) and between workload management and healthcare provider performance (β = 0.167, t = 6.723, p < 0.001). Because both direct and indirect effects remained statistically significant, the mediation observed in both pathways was classified as complementary (partial) mediation. Approximately 37.4% of the total effect of digital health competence on healthcare provider performance and 36.6% of the total effect of workload management on healthcare provider performance were explained through work engagement.
Discussion
This study investigated the relationships among digital health competence, workload management, work engagement, and healthcare provider performance among healthcare professionals at King Abdullah Medical City, Saudi Arabia. The findings demonstrated that digital health competence and workload management positively influenced healthcare provider performance both directly and indirectly through work engagement. These results support the proposed conceptual framework and reinforce the importance of integrating technological capability, organizational resources, and psychological engagement to improve workforce performance within digitally transforming healthcare organizations.
The positive influence of digital health competence on work engagement and healthcare provider performance is consistent with previous studies demonstrating that healthcare professionals with higher digital competence are better able to integrate digital technologies into clinical practice, improve communication, enhance clinical decision-making, and deliver more efficient patient care (Kruse et al., 2020; Kane et al., 2021; Venkatesh et al., 2022). Digital competence extends beyond technical proficiency to include digital communication, information management, data security, and critical appraisal of health information, all of which are increasingly essential in modern healthcare systems (Shachak et al., 2019). Furthermore, digital transformation has fundamentally reshaped healthcare delivery by integrating artificial intelligence, electronic health records, and data-driven clinical decision support into routine practice (Topol, 2019; Nguyen et al., 2022). Consequently, continuous investment in digital competency development represents an important organizational strategy for improving workforce capability and healthcare quality (WHO, 2021; Meijerink et al., 2022; Yu et al., 2025; Starke et al., 2025).
The study also found that workload management positively influenced both work engagement and healthcare provider performance. These findings are consistent with the Job Demands–Resources (JD-R) theory, which proposes that balancing job demands with adequate organizational resources promotes employee motivation and work performance (Bakker & Demerouti, 2020). Effective workload management reduces excessive physical and psychological demands while allowing healthcare professionals to allocate greater attention to patient care. In digitally transforming healthcare organizations, effective workload management may also reduce technostress arising from the increasing use of digital health technologies, thereby supporting employee well-being and work performance (Tarafdar et al., 2020). Previous studies have similarly reported that appropriate workload distribution is associated with lower burnout, improved job satisfaction, enhanced patient safety, and better organizational outcomes (Dall’Ora et al., 2020; Marufu et al., 2021; Montgomery et al., 2021). Consequently, healthcare organizations should consider workload optimization as an important organizational strategy for maintaining employee well-being, reducing technology-related work strain, and improving healthcare service quality.
An important finding of this study is the mediating role of work engagement. The results suggest that digital health competence and effective workload management not only improve healthcare provider performance directly but also strengthen employees’ engagement with their work, which subsequently contributes to improved performance. These findings support previous evidence indicating that engaged healthcare professionals demonstrate greater dedication, higher motivation, and stronger commitment to organizational goals (Barello et al., 2021; Salanova et al., 2021). The mediating role of work engagement further supports the Job Demands–Resources (JD-R) theory, which proposes that organizational and personal resources stimulate motivational processes that ultimately enhance employee performance (Bakker & Demerouti, 2020). Within digitally transforming healthcare environments, employees who possess sufficient technological competencies and experience supportive working conditions are more likely to remain engaged and perform effectively. This finding further emphasizes the importance of integrating organizational, technological, and psychological factors when designing strategies to improve healthcare workforce performance. The present study contributes to the growing literature on digital transformation in healthcare by integrating digital health competence, workload management, work engagement, and healthcare provider performance into a single conceptual model. While previous studies have frequently examined these variables independently, this study provides a more comprehensive understanding of how technological capability and organizational resources interact through psychological mechanisms to influence workforce performance. These findings extend the application of the Job Demands–Resources theory within digitally transforming healthcare organizations and provide empirical evidence supporting integrated workforce management strategies.
Strengths and limitations
This study has several strengths. It examined multiple organizational and psychological factors simultaneously using Structural Equation Modeling–Partial Least Squares (PLS-SEM), enabling the assessment of both direct and indirect relationships among the study variables. Furthermore, the study was conducted in a large tertiary referral hospital actively implementing digital health technologies, providing valuable insights into workforce performance within a rapidly evolving healthcare environment. Second, the study employed validated measurement instruments with satisfactory psychometric properties, thereby strengthening the reliability and validity of the findings.
However, several limitations should be acknowledged. First, the cross-sectional design prevents causal inference between the study variables. Second, the study was conducted at a single tertiary healthcare institution, which may limit the generalizability of the findings to other healthcare settings. Third, all variables were measured using self-administered questionnaires, which may introduce response bias, social desirability bias, and common method variance despite the use of validated instruments.
Implications for practice
The findings have important practical implications for healthcare managers and policymakers. Healthcare organizations should prioritize continuous digital competency development through structured education and training programmes while simultaneously implementing effective workload management strategies to reduce occupational stress and improve employee well-being. Strengthening work engagement through supportive leadership, professional development opportunities, and positive organizational culture may further enhance healthcare provider performance. Collectively, these strategies may support healthcare organizations in achieving sustainable digital transformation while maintaining high-quality patient care.
Future research
Future studies should employ longitudinal or prospective research designs to better establish causal relationships among digital health competence, workload management, work engagement, and healthcare provider performance. Multi-centre studies involving different healthcare organizations and healthcare systems are also recommended to improve the generalizability of the findings across diverse healthcare settings. Additionally, future research should investigate other organizational and psychological factors, including digital leadership, organizational culture, innovation climate, employee resilience, organizational learning capability, and technostress, which may further explain healthcare workforce performance in digitally transforming healthcare environments (Khan et al., 2025; Tarafdar et al., 2020; Starke et al., 2025). Qualitative or mixed-methods approaches may also provide a deeper understanding of how healthcare professionals experience digital transformation and how organizational support facilitates successful technology adoption in clinical practice.
Conclusion
This study demonstrates that digital health competence and workload management are important determinants of healthcare provider performance within digitally transforming healthcare organizations. Both factors not only exert direct positive effects on healthcare provider performance but also enhance work engagement, which partially mediates these relationships. These findings highlight the importance of integrating technological capability, organizational support, and employee engagement to improve workforce performance.
From a practical perspective, healthcare organizations should prioritize continuous digital competency development, optimize workload management, and foster work engagement through supportive organizational policies and leadership practices. Collectively, these strategies may strengthen workforce performance and support the successful implementation of digital transformation while maintaining high-quality and patient-centered healthcare services.
Ethical considerations
Ethical approval for this study was obtained from the Research Ethics Committee (Institutional Review Board) of Lincoln University College, Malaysia (Approval No. LUC/MKT/SP/012/223). Written informed consent was obtained electronically from all participants before questionnaire completion. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki.
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Albeah AM and Hassan HC. Digital Health Competence, Workload Management, and Healthcare Provider Performance: The Mediating Role of Work Engagement [version 1; peer review: awaiting peer review]. F1000Research 2026, 15:1133 (https://doi.org/10.12688/f1000research.186050.1)
NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.
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