Factors influencing clinician performance post-electronic health record implementation: an empirical analysis in Moroccan hospitals

Abstract

Background

In recent years, the Moroccan Ministry of Health and Social Protection has invested considerable resources in implementing electronic health record (EHR) systems to provide citizens with quality healthcare services through efficient structures. However, the rhythm of EHR deployment across the country is very slow, requiring urgent evaluation to remove barriers to successful EHR adoption.

Objective

This study aims to investigate the critical factors affecting healthcare providers’ performance post-EHR implementation in Moroccan public hospitals.

Methods

A cross-sectional study was conducted in three hospitals affiliated with Hassan II University Hospital Center in Fez. Data were collected using a questionnaire survey administered to a sample of 368 healthcare providers from March 2021 to July 2021. Clinician performance was assessed using a proposed research model that integrates the Information System Success Model and the Technology-Organization-Environment framework. The final model was analyzed and tested by using structural equation modeling. Statistical analyses were conducted using SPSS version 25 and Amos version 26.

Results

The findings highlighted that the most critical factors influencing clinician performance are clinician satisfaction (β = 0.5, p < 0.001), followed by organization (β = 0.28, p < 0.001), and system quality (β = 0.17, p = 0.01). Additionally, information quality indirectly affects clinician performance (β = 0.19, p < 0.001). However, the environmental factor does not appear to have a significant impact (β = -0.004, p = 0.94).

Conclusion

This study, performed for the first time in Morocco, identifies key factors for policymakers and healthcare organizations to enhance the successful implementation of EHR systems. Additionally, it serves as a valuable framework for future studies in this area.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-025-12438-w.

Background

The implementation of information technologies (IT) in healthcare has accelerated worldwide over the past decade [1]. Among these initiatives, electronic health record (EHR) systems have become pivotal tools, fundamentally transforming healthcare practices [2] and serving as the backbone for various e-health solutions [3]. Consequently, developed and developing countries are increasingly investing in health information technology (HIT), particularly in EHR implementation, to improve the quality and efficiency of healthcare services [4, 5].

An EHR is defined as a digital repository containing all clinical and administrative data of a patient that can be securely stored, exchanged, and accessed by authorized healthcare professionals [6–8]. The successful implementation of EHR systems offers several advantages, such as reducing healthcare costs, improving work performance, enhancing care quality, reducing medical errors, supporting clinical decisions, and improving care coordination [2, 4, 9–11]. However, EHR adoption also presents several challenges, including high implementation costs, workflow disruption, temporary productivity decreases, and data privacy concerns [12]. Additionally, healthcare professionals report decreased satisfaction due to the increased administrative burden [13, 14], which detracts from patient care time and raises levels of fatigue and burnout [15–17].

In Morocco, over the past decade, strategies to digitalize public services, including healthcare, have been launched through the “Maroc Numeric 2013” project [18], which aims to integrate information and communication technologies to meet the needs of citizens and businesses. In the healthcare sector, University Hospital Centers (CHU) have implemented EHR systems comprising multiple patient-focused modules [19], operated by authorized healthcare providers within specific facilities. Additionally, the Ministry of Health and Social Protection (MoHSP) has implemented a national health management information system (HMIS) to support Morocco’s health system activities across all levels of the organizational structure [20]. Similarly, telemedicine services have been launched to provide specialized healthcare services to patients in remote rural regions [21]. More recently, the MoHSP announced plans to expand EHR implementation across primary and secondary healthcare facilities as part of a broader national healthcare system overhaul, with an estimated budget of 120 million dollars [22].

However, several obstacles continue to hinder the successful deployment of EHR systems in Morocco due to the increasing complexity of EHR implementation strategies [14, 23]. A documentary analysis by Oufkir et al. [24] revealed that, despite growing awareness and efforts to implement a nationwide EHR system in Morocco, significant organizational, technical, and human barriers persist. Additionally, Parks et al. [19] identified specific challenges in Moroccan university medical centers, including limited bargaining power in EHR selection, recurring errors, interoperability challenges, and inadequate human and financial resources. Similarly, Derecho et al. [25] highlighted common barriers to EHR adoption in developing countries, such as insufficient training, resistance to change, and high implementation costs, contributing to lower adoption rates.

These challenges underscore the urgent need for comprehensive evaluation studies to address factors that may influence successful EHR adoption in Moroccan hospitals, particularly in terms of enhancing healthcare providers’ performance. Despite growing interest in EHR implementation, a significant gap in the literature remains regarding the empirical evaluation of EHR experiences across diverse Moroccan healthcare settings [24], and scarce information on their usage and intended usage scenarios [19]. To our knowledge, no studies have specifically assessed EHR implementation success with a focus on clinician performance in Morocco.

To address these gaps, the present study aims to examine key factors influencing clinician performance in the post-implementation phase of EHR adoption across Moroccan public hospitals. This research provides a valuable framework for healthcare managers, policymakers, and IT professionals to develop more effective strategies for successful EHR implementation, particularly in the context of developing countries.

Theoretical foundation and proposed research model

The DeLone and McLean Information System Success Model (ISSM) [26] is widely recognized as a comprehensive framework for evaluating the effectiveness of information systems across various contexts [27], particularly for assessing EHR implementations in developing countries [28], as it provides more relevant criteria compared to other IT acceptance models [11]. Several studies [27–31] have employed the ISSM framework, as its key dimensions—system quality, information quality, IT service quality, system use, user satisfaction, and net benefits— provide a thorough evaluation of EHR implementation’s impact on healthcare professionals. However, not all variables from the ISSM may be applicable, given the mandatory nature of EHR system use [32, 33]. Additionally, the ISSM has been critiqued for not fully addressing the role of environmental and organizational factors in successful adoption. To address this, we propose modifying the ISSM by removing the “system use” variable and incorporating organizational and environmental factors, as outlined in the Technology-Organization-Environment (TOE) framework [34]. The TOE framework has been widely applied and tested across various stages of innovation assimilation (initiation, adoption, routinization) in diverse settings, including both developed and developing countries [35, 36]. Moreover, studies by Abdekhoda et al. [37] and Hsiao et al. [38] have demonstrated the TOE framework’s relevance in understanding the factors that influence health information technology adoption.

By integrating ISSM with the TOE framework, this research offers a comprehensive view of factors affecting clinician performance in the post-implementation phase of EHR systems in hospitals (Fig. 1). The study constructs and hypotheses are outlined below.

Fig. 1.

Proposed research model

Technology characteristics include system quality, information quality, and IT service quality [26]. In our study, system quality focuses on the ease of use and ease of learning of EHR systems [37], which are crucial factors for enhancing system usability that directly contribute to increasing clinical productivity, reducing error rates, minimizing user fatigue, and improving user satisfaction [39]. Chang et al. [40], found that system quality positively influences user satisfaction and job performance of healthcare professionals. Additionally, information quality refers to the relevance, completeness, comprehensibility, accuracy, and update of the information generated by the EHR system [11, 28, 30, 41]. Previous research has supported a significant positive association between information quality and user satisfaction [28, 29, 42–44]. Also, Salleh et al. [11] found that record quality positively affects the performance of healthcare providers. Furthermore, IT service quality was evaluated based on the responsiveness, accuracy, reliability, technical competence, and empathy demonstrated by the IT support staff [11, 41]. Previous studies have maintained the positive influence of IT service quality on user satisfaction [28, 42–45], and improvement of work performance [46, 47].

The organizational context is recognized as crucial for the successful implementation of HIS [4, 37, 38, 48–51], particularly through key factors such as top management support, supervisor assistance, and peer influence. Chen & Hsiao [52] demonstrated that adequate management support positively influences the perceived usefulness of HIS, which is reflected in improved work efficiency, reduced hospital care costs, and enhanced patient care quality. Similarly, Maillet et al. [53] found that favorable organizational conditions significantly influence nurse satisfaction.

According to the TOE framework [34], the environmental context includes external pressures such as competition, vendor support, and government policies, which can significantly influence the implementation of IT innovation. In this regard, Jianxun et al. [54] confirmed that institutional pressures (e.g., competitors, government, and partners) strongly influence health practitioners’ attitudes toward the effectiveness of EHR systems in achieving health sector outcomes. Additionally, Kalankesh et al. [55] highlighted the role of vendor support quality in influencing user satisfaction with information systems. Similarly, Erlirianto et al. [43] found that environmental factors positively impact net benefits, such as enhancing the effectiveness and efficiency of EMR users in hospitals.

Clinician satisfaction is a critical factor in successfully implementing information systems (IS), particularly in the post-implementation phase [30]. In this study, clinician satisfaction is defined as “the clinician’s overall level of satisfaction with the EMR system” [29], and it was assessed using factors such as satisfaction with system accuracy, information quality, attitude toward the system, confirmation of expectations, and overall system satisfaction [28, 31, 52, 54, 56]. Tilahun & Fritz [28] demonstrated a significant influence of user satisfaction on the perceived net benefits of EMR systems, including faster care delivery, increased effectiveness, and improved service quality for healthcare practitioners. Additionally, Garcia-Smith & Effken [29] found that nurse satisfaction with clinical information systems strongly influenced job performance, particularly regarding effective and efficient nursing documentation outcomes.

Clinician performance is defined as “the degree to which a clinician believes that using a particular EHR system impacts job performance” [29, 57]. In this study, it was assessed by examining key indicators commonly reported in previous studies [11, 27, 58–60], including effectiveness, efficiency, speed of task execution, error reduction, coordination of care, and quality of care. Healthcare professionals are more likely to adopt a technology when they understand and perceive its usefulness and potential benefits [61]. Conversely, an information system that fails to enhance its users’ professional performance is unlikely to be valuable [62].

In summary, eleven hypotheses were developed to examine the causal relationships between the different variables.

• H 1a. System quality has a positive effect on clinician satisfaction.

• H 1b. System quality has a positive effect on clinician performance.

• H 2a. Information quality has a positive effect on clinician satisfaction.

• H 2b. Information quality has a positive effect on clinician performance.

• H 3a. IT Service quality positively affects clinician satisfaction.

• H 3b. IT Service quality positively affects clinician performance.

• H 4a. Organization has a positive effect on clinician satisfaction.

• H 4b. Organization has a positive effect on clinician performance.

• H 5a. Environment positively influences clinician satisfaction.

• H 5b. Environment positively influences clinician performance.

• H 6: Clinician satisfaction has a positive effect on clinician performance

Furthermore, six hypotheses were proposed to examine the mediation relationships between the independent variables (system quality, information quality, IT service quality, organization, environment) and the dependent variable (clinician performance) through clinician satisfaction:

• H 7. System quality indirectly affects clinician performance via the mediating roles of clinician satisfaction.

• H 8. Information quality indirectly affects clinician performance via the mediating roles of clinician satisfaction.

• H 9. IT Service quality indirectly affects clinician performance via the mediating roles of clinician satisfaction.

• H 10. Organization indirectly affects clinician performance via the mediating roles of clinician satisfaction.

• H 11. Environment indirectly affects clinician performance via the mediating roles of clinician satisfaction.

Research methodology

Research design and setting

This cross-sectional analytical study was carried out in three hospitals within Hassan II University Hospital in Fez, Morocco (Specialties Hospital, Oncology Hospital, Mother and Child Hospital) over five months from March 2021 to July 2021. These three hospitals were selected for their combined capacity of 681 beds and their management of 80% of total admissions to Hassan II University Hospital, supported by approximately 2304 healthcare professionals [63]. Since 2009, they have actively used the EHR system (named HOSIX), developed by the Spanish supplier SIVSA. HOSIX is one of the leading EHR systems in Morocco, implemented in several stages, and integrates various modules for clinical and administrative functions, including medical records, pharmacy, laboratory, radiology, operating rooms, emergency services, billing, collections, appointment scheduling, etc. [19].

Sampling and procedures

The participants in this research were healthcare providers from the three hospitals under investigation, specifically comprising medical staff and the nursing and health technician staff who use the EHR system for patient care. Participants were selected using a convenience sampling technique, and several measures were taken to minimize potential response bias: participation was entirely voluntary, no incentives were provided, and the confidentiality of respondents was rigorously maintained throughout the study.

The sample size should be at least ten times the number of items in the study for data analysis using structural equation modeling (SEM) [64]. Similarly, Hair et al. [65] recommended a sample size between 200 and 400 for adequate statistical analysis using SEM. Based on this approach, 400 paper-based questionnaires were distributed after obtaining permission from each department head. By the end of the data collection period, 380 questionnaires were returned, yielding a response rate of 95%. After excluding incomplete responses, 368 valid questionnaires were retained for analysis.

Measurement tool

The survey instrument, divided into two sections, was designed by selecting appropriate questions from previous studies. The first section includes questions about general sociodemographic information, such as age, gender, clinical position, hospital, years of EHR system use, and training in the use of the EHR system. The second section comprises 29 items addressing the 7 constructs in the proposed model of the current study: system quality (2 items), information quality (5 items), IT service quality (4 items), organization (3 items), environment (3 items), clinician satisfaction (5 items), and clinician performance (6 items). Participants were asked to indicate their level of agreement with each statement using a seven-point Likert scale, where 1 represents “strongly disagree” and 7 represents “strongly agree”.

After translating the questionnaire from English to French and performing a back-translation by an English language expert, the instrument was reviewed for content validity by a panel of seven experts. Their feedback helped refine the questionnaire, enhancing its validity. Consequently, two items were removed, and some wording adjustments were made to improve clarity. The revised questionnaire was then pre-tested by asking 20 healthcare professionals, who actively use the EHR system in their daily work, to complete it and provide their feedback. Consequently, none of the questions were deemed difficult to understand, except for items ENV2 and ENV4, for which we added examples in parentheses to clarify them.

To assess the reliability of the revised questionnaire, both Cronbach’s alpha and composite reliability (CR) should exceed 0.7 [66]. As shown in Table 6 in Appendix 1, Cronbach’s alpha values ranged from 0.72 to 0.89, and CR values ranged from 0.75 to 0.89, indicating acceptable construct reliability.

To validate the measurement items, both convergent and discriminant validity were assessed. First, convergent validity was evaluated using the average variance extracted (AVE) and item loadings, both of which must exceed the recommended threshold of 0.50 [66]. In our study, the calculated AVE values ranged from 0.51 to 0.60, and all construct loadings ranged from 0.54 to 0.95 (Table 6 in Appendix 1), which surpasses the recommended levels. Second, discriminant validity was assessed using the square root of the AVE and the cross-loading matrix. For acceptable discriminant validity, the square root of a construct’s AVE should be greater than its correlation with other constructs [67]. As shown in Table 1, the square root of the AVE for each latent construct (in bold) exceeded its correlations with other constructs, indicating strong discriminant validity of the measurement items. Therefore, both convergent and discriminant validity of the questionnaire were successfully confirmed. In total, 37 items were finalized for the field survey (Table 6 in Appendix 1).

Table 1.

Discriminant validity results

Latent construct	System quality	Information quality	IT service quality	Organization	Environment	Clinician satisfaction	Clinician performance
System quality	0.77
Information quality	0.47***	0.79
IT service quality	0.41***	0.61***	0.76
Organization	0.27***	0.49***	0.46***	0.71
Environment	0.21**	0.24***	0.27***	0.312***	0.72
Clinician satisfaction	0.50***	0.69***	0.59***	0.587***	0.28***	0.76
Clinician performance	0.43***	0.46***	0.34***	0.551***	0.22***	0.66***	0.77

The bolded diagonal represents the AVE values, while the non-bolded values represent the correlations between the constructs

^** p < 0.01

^*** p < 0.001

Data analysis

Statistical analyses were conducted using SPSS version 25 and Amos version 26. Initially, a descriptive analysis was conducted to summarize respondents’ demographic profiles and the characteristics of the model constructs (means and standard deviation (SD) for continuous variables, and frequencies and percentages for categorical variables). Next, the measurement model was evaluated by examining internal reliability (Cronbach’s alpha and composite reliability), convergent validity (average variance extracted and item loadings), and discriminant validity (Fornell & Larcker criterion).

Additionally, seven model-fit indices were employed to assess the overall fitness of the structural model, encompassing the comparative fit index (CFI), adjusted goodness-of-fit index (AGFI), normal fit index (NFI), Tucker-Lewis index (TLI), goodness of fit index (GFI), root mean square of standardized residual (RMSEA), and Chi-square ratio (Chisq/df). Finally, the structural equation modeling (SEM) method, was employed to examine the relationships between variables, and mediation analyses were conducted using a bootstrap method with a 95% confidence interval. The significance level was set at p < 0.05, representing the standard threshold for statistical significance.

Data analysis and results

Respondent profiles and descriptive statistics of model constructs

Table 2 presents the sociodemographic characteristics of the respondents. Among the 368 healthcare professionals surveyed, the average age of respondents was 29.21 (± SD = 5.33), with ages ranging from 22 to 60 years. More than half 62.5% (n = 230) were female, and the majority were physicians 86.1% (n = 317). Additionally, 78% (n = 287) of respondents worked in specialty hospitals, and nearly half of the participants 47.3% (n = 174) had used EHRs for one to four years when the survey took place. Finally, a smaller percentage of healthcare providers 12.2% (n = 45) had received training in EHR use.

Table 2.

Demographic profile of respondents (n = 368)

Variables	Mean ± SD	Frequency	Percentage (%)
Age a	29.21 ± 5.33 Range (22–60)
Gender b
Female		230	62.5
Male		137	37.2
Hospital
Specialties Hospital		287	78
Oncology Hospital		35	9.5
Mother and Child Hospital		46	12.5
Professional category
Physicians		317	86.1
Nurses & Health Technicians		51	13.9
Year of EHR system use
< 1 year		63	17.1
1–4 years		174	47.3
5–9 years		87	23.6
> 10 years		44	12
Training in the use of EHR system c
Yes		45	12.2
No		313	85.1

SD Standard Deviation

^aEight missing values

^bOne missing value

^cTen missing values

Table 3 provides descriptive statistics for the model variables, measured on a 7-point scale. The highest means were recorded for clinician performance (mean = 5.30, SD = 1.08), system quality (mean = 5.26, SD = 1.30), organization (mean = 5.23, SD = 1.09), and information quality (mean = 4.96, SD = 1.15), indicating strong positive opinions in these areas. In contrast, environment (mean = 4.17, SD = 0.93) and IT service quality (mean = 4.48, SD = 1.26) received the lowest means, reflecting comparatively lower perceptions of these aspects. Furthermore, information quality (mean = 4.96, SD = 1.15) and clinician satisfaction (mean = 4.79, SD = 1.09) presented moderate means, suggesting a balanced view regarding these variables.

Table 3.

Descriptive statistics of constructs

Construct	Mean	Standard Deviation
System Quality (SQ)	5.26	1.30
Information Quality (IQ)	4.96	1.15
IT Service Quality (ISQ)	4.48	1.26
Organization (ORG)	5.23	1.09
Environment (ENV)	4.17	0.93
Clinician Satisfaction (CS)	4.79	1.09
Clinician Performance (CP)	5.30	1.08

Structural model analysis results

As illustrated in Fig. 2, all the model-fit measures (CFI, NFI, TLI, AGFI, GFI, RMSEA, Chisq/df) were within acceptable threshold values recommended by previous studies [66, 68, 69], indicating that the proposed model demonstrated a satisfactory fit with the observed data, and structural model analysis can be performed [70].

Fig. 2.

Structural model analysis results

Hypothesis testing

The structural model identifies relationships among dependent and independent variables using path coefficients (β) and critical ratios (C.R). The results for the structural model, presented in Table 4 using AMOS v.26, indicate that system quality significantly influences both clinician satisfaction (β = 0.19, p < 0.001) and clinician performance (β = 0.17, p = 0.01). Information quality has a significant effect on clinician satisfaction (β = 0.37, p < 0.001), but its influence on clinician performance is not significant (β = 0.004, p = 0.96). IT service quality has a significant positive effect on clinician satisfaction (β = 0.13, p = 0.04) and a significant negative effect on clinician performance (β = −0.14, p = 0.03). Organizational factors significantly influence both clinician satisfaction (β = 0.29, p < 0.001) and clinician performance (β = 0.28, p < 0.001). Clinician satisfaction strongly influences clinician performance (β = 0.5, p < 0.001). However, the environmental factors do not significantly influence either clinician satisfaction (β = 0.02, p = 0.62) or clinician performance (β = −0.004, p = 0.94).

Table 4.

Results of the hypothesis tests for the proposed research model using SEM

Hypothesis	Path			β	C.R	p	Decision
H1a	SQ	→	CS	0.19	3.31	< .001***	Accepted
H1b	SQ	→	CP	0.17	2.74	0.01**	Accepted
H2a	IQ	→	CS	0.37	5.55	< .001***	Accepted
H2b	IQ	→	CP	0.004	−0.05	0.96	Rejected
H3a	ISQ	→	CS	0.13	2.08	0.04*	Accepted
H3b	ISQ	→	CP	−0.14	−2.19	0.03*	Rejected
H4a	ORG	→	CS	0.29	4.74	< .001***	Accepted
H4b	ORG	→	CP	0.28	4.24	< .001***	Accepted
H5a	ENV	→	CS	0.02	0.5	0.62	Rejected
H5b	ENV	→	CP	−0.004	−0.08	0.94	Rejected
H6	CS	→	CP	0.50	6.08	< .001***	Accepted

β Standardized regression weights, C.R Critical Ratio, p Signification value, SQ system quality, IQ Information quality, ISQ IT service quality, ORG Organization, ENV Environment, CS Clinician satisfaction, CP Clinician performance

^*P < 0.05

^** P < 0.01

^*** P < 0.001 level

Further, our proposed model investigated five indirect relationships among our research variables, as suggested in H7, H8, H9, H10, and H11. Based on the findings of the mediation tests (Table 5), we can first demonstrate that the impact of information quality on clinician performance is fully mediated by clinician satisfaction (H8). Furthermore, clinician satisfaction acts as a partial mediator in the effect of system quality, IT service quality, as well as organization on clinician performance (H7, H9, H10). However, no mediation relationship is observed between the environment and clinician performance through clinician satisfaction (H11).

Table 5.

Mediating roles of clinician satisfaction using a bootstrap analysis with a 95% Confidence Interval

Mediation pathway	Direct effect		Indirect effect		Confidence Interval		Conclusion
	Estimate	p-value	Estimate	p-value	Low	High
H7: System Quality →	0.177	0.006	0.1	0.001	0.039	0.2	Partial mediation
Clinician Satisfaction →
Clinician Performance
H8: Information Quality →	−0.004	0.957	0.197	0.000	0.108	0.316	Full mediation
Clinician Satisfaction →
Clinician Performance
H9: IT Service Quality →	−0.159	0.028	0.072	0.043	0.002	0.171	Partial mediation
Clinician Satisfaction →
Clinician Performance
H10: Organization →	0.362	0.001	0.182	0.000	0.094	0.306	Partial mediation
Clinician Satisfaction →
Clinician Performance
H11: Environment →	−0.005	0.935	0.015	0.65	−0.058	0.109	No mediation
Clinician Satisfaction →
Clinician Performance

Bootstrap sample = 5000 with replacement

Discussion

This study aims to investigate the factors significantly influencing clinician performance in the post-implementation phase of EHR adoption in three Moroccan hospitals. Misra and Mondal [71] highlight that understanding the influence of IT innovation on various user categories is essential and adds a significant contribution to research. In line with this perspective, we developed an integrated model combining the ISSM and the TOE framework to provide valuable insights into potential determinants influencing clinician performance in using EHR systems in a hospital setting (Fig. 1).

Based on structural equation modeling results, clinician satisfaction emerged as the strongest predictor of clinician performance during EHR adoption (H6). This indicates that clinicians who are generally satisfied with the accuracy, quality, and functionality of the EHR system, as well as how well it meets their expectations, are more likely to integrate the system effectively into their daily workflows and subsequently achieve their objectives, particularly in enhancing clinical efficiency and productivity [39]. This result aligns with the research by Yu and Qian [31], which showed that user satisfaction accounts for 63% of the direct effect on net benefits, such as improved job performance resulting from EHR use. Additionally, Tilahun and Fritz [28] found that user satisfaction has the greatest influence on perceived net benefits among healthcare professionals during the implementation of EMR in low-resource settings. Thus, maintaining high levels of user satisfaction becomes a key determinant for achieving desired performance improvements and realizing the full potential of the EHR system.

This study also revealed that organizational factors have a positive effect on both clinician satisfaction (H4a) and clinician performance (H4b). This can be explained by the fact that healthcare professionals’ attitudes toward EHR usage are significantly influenced by the availability of resources, the positive influence of colleagues (particularly champions and super users of EHR systems), and the commitment of departmental leadership [48]. Additionally, when top management actively participates in EHR implementation, it facilitates the appropriate allocation of human and financial resources, enabling clinicians to better meet the demands of their practice and enhance their overall performance [52]. These findings are consistent with previous empirical research. For example, Maillet et al. [53] reported that nurse satisfaction is directly influenced by improvements in organizational conditions, particularly the availability of essential resources for the effective functioning of the EHR system. Similarly, studies by Shiferaw & Mehari [72] and Luyten & Marneffe [73] confirmed that the attitude of EHR users is significantly impacted by peer influence. Moreover, Lambooij et al. [74] revealed a significant relationship between the organizational factor and the perceived added value of EMR among physicians and nurses working in hospitals. Therefore, hospital decision-makers need to develop organizational strategies that reinforce management commitment and promote a culture of collaboration among clinicians to increase healthcare professional motivation and support the effective adoption of EHR systems, ultimately leading to improved clinical performance.

Regarding the technological aspects, this study confirmed that system quality, particularly in terms of ease of use and ease of learning the functionalities of the EHR, has a significant positive effect on clinician satisfaction (H1a) and performance (H1b). This indicates that a well-designed and intuitive EHR system allows clinicians to quickly familiarize themselves with its features, reducing the time needed for learning and integrating this tool into their daily practice. Additionally, utilizing a system that is intuitive and aligned with clinicians’ working patterns will reduce their workload by decreasing the time spent on data management, enabling them to concentrate more on direct patient care, which would enhance clinical productivity [11]. These findings are consistent with several studies, including those by Tubaishat [30] and Tilahun and Fritz [28], who found that system quality significantly influences user satisfaction with the EHR system. Additionally, Salleh et al. [27] and Mijin et al. [75] highlighted the positive impact of EHR feature quality on healthcare provider performance. However, a study by Erlirianto et al. [43], conducted with 87 healthcare professionals in an Indonesian hospital, found no significant association between EMR system quality and user satisfaction. Thus, designing a user-oriented system interface allows judicious use through easy data entry, automatic information verification, and timely access to the system [76], thereby enhancing the efficiency and effectiveness of healthcare practitioners in delivering care.

This study also found that information quality is the most significant variable positively influencing clinician satisfaction (H2a). This effect might be attributed to the user-friendly format of the EHR, which facilitates quick and accurate data entry, continuous information updates, and error reduction, enabling healthcare providers to make timely and accurate diagnoses and treatment decisions [11]. Additionally, the comprehensiveness and relevance of patient data enable clinicians to make informed decisions regarding patient care or referrals to other services or hospitals without the risk of diagnostic errors, redundant tests, resource wastage, or incorrect medications, thereby enhancing their satisfaction and performance [11]. This finding aligns with previous studies [31, 42, 43] which also identified a significant relationship between EHR information quality and user satisfaction. However, our study found that information quality did not have a direct influence on clinician performance (H2b). This result aligns with the study conducted by Chen and Hsiao [52], which found no significant effect of information quality on the perceived usefulness of EHR systems among physicians in Taiwanese hospitals. Nevertheless, our research revealed that information quality has an indirect positive effect on clinician performance through its influence on clinician satisfaction, supporting the mediating relationships in our research model. This suggests that while high-quality information is an essential requirement, it is not enough on its own to enhance clinician performance. It is crucial to consider information quality within a broader framework that encompasses organizational processes and their adaptability to the specific needs of each department. These findings align with previous research on EHR adoption, which indicates that the use of high-quality information from EHR systems significantly enhances care efficiency and user performance [11, 46]. Thus, hospital managers should focus not only on improving the technical aspects of EHR systems but also on ensuring that the information quality is aligned with clinicians' expectations and needs.

The findings of this study also confirmed that IT service quality positively influences clinician satisfaction (H7a). This means that responsive and competent IT support facilitates successful EHR adoption by providing technical assistance, addressing user feedback, and rapidly resolving issues related to the EHR system, computers, and networks. These actions reduce clinician frustration when technical obstacles arise, thereby enhancing their overall satisfaction with the system. This finding aligns with previous studies. For example, Hung et al. [77] found that the quality of EHR service, measured by assurance, empathy, reliability, and tangibility, positively influences user satisfaction and perceived usefulness. Similarly, studies by Tilahun & Fritz [28], Erlirianto et al. [43], and Chang et al. [47], also identified a significant relationship between IT service quality and user satisfaction.

In contrast, IT service quality was found to influence clinician performance negatively, contradicting our initial hypothesis (H7b) and previous relevant research, which found IT service quality to be a strong predictor of clinician performance [11, 46, 47]. This result might be due to several factors: (a) unexpected system downtime, obsolete hardware (e.g., computers and servers), or slow response times that disrupt clinical workflows, forcing clinicians to use paper records in addition to the EHR, which increases their workload [11]; (b) clinicians who frequently rely on IT personnel may become less self-sufficient in navigating the system or solving minor issues, which negatively impacts their performance; and (c) clinicians may not have received adequate training on the EHR system, hindering their ability to use advanced features and affecting their overall efficiency. Therefore, continuous IT support, regular updates to hardware and networks, and comprehensive user training are essential for accelerating successful EHR adoption and improving clinician productivity [4].

According to our study results, environmental factors do not influence either clinician satisfaction (H5a) or clinician performance (H5b), indicating that environmental pressures in the Moroccan context, such as government pressure and provider support, do not directly or indirectly affect the success of EHR adoption. These findings contrast with previous studies, such as those by Jianxun et al. [54], which found that institutional pressures (coercive, normative, and mimetic) had significant effects on healthcare professionals’ attitudes toward EHR systems. Additionally, Erlirianto et al. [43] highlighted a strong positive influence of environmental factors on the expected benefits of EMR systems. The divergence between our results and previous research might be due to the specific context of our study. While the Moroccan government has recently initiated reforms to modernize the healthcare system and implement EHR systems across all facilities, these efforts remain in the early stages and have not yet been fully realized. Consequently, clinicians may perceive that current incentives and policies do not adequately address the practical challenges encountered during EHR use. Moreover, the support provided by EHR vendors may not be sufficiently tailored to meet the specific needs of each department, thereby affecting its perceived effectiveness. Similarly, Moroccan University Hospitals (CHUs) do not adhere to competitive principles exercised by other hospitals, as these healthcare structures are regulated by the state to fulfill a social mission focused on providing quality healthcare services. Therefore, while environmental factors may affect the success of EHR adoption in theory, their influence on clinician performance remains limited in this particular setting.

Based on the results of this research, we propose significant theoretical contributions to the field of health IT evaluation. Specifically, our results have statistically validated an integrated model comprising six factors -system quality, information quality, service quality, organization, environment, and clinician satisfaction- that serve as antecedents for evaluating the clinical performance achieved or perceived by healthcare providers following the adoption of an EHR system. Similarly, our research revealed the mediating role played by clinician satisfaction between information quality and clinician performance. Consequently, the success and effectiveness of healthcare providers depend on their levels of satisfaction with the quality and reliability of the data offered by the EHR system.

In the context of managerial implications from this study, the empirical results offer important insights for decision-makers and managers working on digital healthcare projects, particularly in middle and low-resource environments. To ensure user satisfaction, IT departments should conduct regular assessments to identify areas for improvement and confirm that the quality of the data provided by the EHR system meets user expectations. Given the significant impact of EHR system quality on clinician performance, it is recommended that hospital management increase its annual IT budget and strategically plan for the system’s ongoing development based on the specific needs of clinicians. Upgrading IT infrastructure with modern hardware is also crucial for minimizing disruptions that could negatively affect clinician productivity.

In addition, healthcare managers should focus on developing well-trained IT teams capable of providing responsive and ongoing technical support to ensure that clinicians receive timely assistance in resolving technical issues that could disrupt their workflow. Similarly, continuous training for clinicians in medical IT is essential, as it enhances their ability to use the system effectively and reduces their reliance on IT support. Although environmental factors did not significantly influence the participants in our study, it remains important for policymakers to establish a supportive environment where governmental policies (both incentives and regulations), and vendor support (through training sessions and tailored system updates) are directly aligned with clinicians’ specific needs, to enhance both clinician satisfaction and performance.

Limitations and conclusions

This research presents three important limitations. First, it was conducted exclusively in three hospitals affiliated with the Hassan II University Hospital in Fez, all utilizing a single EHR system. Participant recruitment was performed using a convenience sampling method, focusing primarily on two groups of healthcare professionals: medical staff and nursing and health technician staff. This focus may limit the generalizability of the findings to other Moroccan healthcare facilities particularly those with fewer resources or different EHR systems. Consequently, future studies should include a broader range of healthcare institutions, including non-academic hospitals, and other user groups, such as administrative staff and allied health professionals, while employing random sampling to enhance statistical power. Additionally, comparing the perceptions of various professional groups regarding the factors influencing the successful implementation of EHR systems would provide valuable insights. Second, the developed model did not integrate important variables, such as resistance to change and computer literacy, which could have provided a more comprehensive explanation of healthcare providers’ performance, particularly in the context of developing countries. Including these variables could help healthcare organizations in developing more effective strategies for the successful implementation of EHR systems. Additionally, future research could examine the moderating effects of control variables such as age, gender, clinical status, and experience to better understand the influence of EHR adoption on clinician performance. Third, the data were collected at a single point in time using only self-reported questionnaires, which may have introduced response bias and limited the ability to gain a deeper understanding of the causal relationships between the variables in our model over time. To address this limitation, we recommend adopting a longitudinal approach combined with mixed methods. This approach would provide stronger evidence of causality regarding the interactions among key factors in a successful EHR system adoption model and help identify specific barriers contributing to low EHR adoption or system failure within healthcare institutions.

In conclusion, this study examined the predictive factors influencing clinician performance during the adoption of EHR systems in hospital settings, employing an integrated model comprising seven concepts measured through a reliable and valid research instrument. The findings suggest that clinician satisfaction is a significant determinant of clinician performance and contributes to the effective implementation of the EHR system. Furthermore, organizational aspects, such as support from top management, assistance from department heads, and peer influence, were found to be significantly correlated with clinician satisfaction and performance.

Additionally, system quality, its functionalities tailored to clinicians’ specific needs, and the quality of information provided by the EHR were identified as important factors for improving clinician satisfaction and supporting clinician productivity. Our findings also highlight the need for IT departments to provide responsive and continuous technical support. Similarly, regular training for clinicians in health informatics is crucial to reducing their reliance on technical support, promoting better system adoption, and facilitating the smooth integration of the EHR system into clinical workflows. To improve EHR implementation, policymakers and hospital managers should prioritize regular assessments of clinician satisfaction and provide adequate management and technical support. These actions are particularly critical in the Moroccan context and other resource-limited settings, where strategic planning and effective resource allocation are necessary to achieve the successful implementation and sustained use of EHR systems.

Abbreviations

CP

Clinician Performance

CS

Clinician Satisfaction

EHR

Electronic Health Records

EMR

Electronic Medical Records

ENV

Environment

HIT

Health Information Technology

HIS

Health Information Systems

IQ

Information Quality

IT

Information Technology

ISQ

Information technology Service Quality

IS

Information System

ISSM

Information System Success Model

ORG

Organization

SQ

System Quality

TOE

Technology-Organization-Environment

Appendix 1

Table 6.

Evaluation of measurement constructs

Construct / Reference	Code	Items	FL	Alpha	CR	AVE
System Quality (SQ) [37, 58, 73]	SQ1	EHR system is easy to use	0.79	0.74	0.75	0.60
	SQ2	EHR system is easy to learn	0.75
Information Quality (IQ) [11, 28, 30, 45, 75]	IQ1	EHR system provides information that is relevant to my work	0.72	0.89	0.89	0.63
	IQ2	EHR system provides sufficient information	0.79
	IQ3	EHR system provides up-to-date information	0.82
	IQ4	EHR system provides information that is easy to understand	0.83
	IQ5	EHR system provides accurate information	0.79
IT Service Quality (ISQ) [11, 45, 73, 74]	ISQ1	IT personnel provide quick assistance when I encounter problems with the EHR system	0.69	0.85	0.85	0.57
	ISQ2	IT personnel for EHR system understand the specific needs of users	0.73
	ISQ3	IT personnel have the necessary skills and expertise to perform their job well	0.79
	ISQ4	IT personnel provide follow-up services to users	0.81
Organization (ORG) [43, 50, 58, 73]	ORG1	The Top management of this hospital has motivated my use of the EHR system	0.54	0.73	0.75	0.51
	ORG2	My supervisor has been supportive of my use of the EHR system	0.74
	ORG3	The colleagues I value most believe that I should consistently use the EHR system	0.82
Environment (ENV) [45, 50, 54]	ENV2	The Ministry of Health and Social Protection provides incentives (subsidies, expertise, etc.) for implementing the EHR system	0.69	0.72	0.76	0.52
	ENV3	The Ministry of Health and Social Protection establishes clear policies for the implementation of the EHR system	0.68
	ENV4	The provider of the EHR system offers the necessary support (training, software, etc.) to ensure its proper functioning	0.55
Clinician Satisfaction (CS) [30, 43, 45, 54, 73, 77]	CS1	Users have a positive attitude toward the EHR system	0.79	0.87	0.87	0.58
	CS2	I’m satisfied with the quality of the information provided by the EHR system	0.81
	CS3	I am satisfied with the accuracy of the EHR system	0.75
	CS4	The services provided by the EHR system meet my expectations	0.63
	CS5	Overall, I am satisfied with the EHR system	0.80
Clinician Performance (CP) [11, 27, 43, 45, 59, 73–75]	CP1	Using the EHR system enables me to accomplish tasks more quickly	0.84	0.89	0,89	0,59
	CP2	Using the EHR system increases my effectiveness on the job	0.95
	CP3	Using the EHR system improves my work efficiency	0.92
	CP4	Using the EHR system reduces medical errors	0.66
	CP5	Using the EHR system facilitates coordination among departments	0.62
	CP6	Using the EHR system improves the quality of patient care	0.54

AVE Average variance extracted, CR Composite reliability, EHR Electronic health record, FL Factor loading

Authors’ contributions

RR contributed to the conceptualization, research design, data collection, data analysis, and initial manuscript drafting; AEL contributed to data analysis and critically revised the manuscript; HE contributed to the conception and design of the study, and acquisition of data; AL involved in the acquisition of data; JE contributed in the conceptual work and critically reviewed text and analyses; TM involved in the acquisition of data; BB involved in revising the manuscript critically; AAI contributed to the research design and interpretation, revised and approved the final paper. All authors read and approved the manuscript.

Funding

The research did not receive funding from any organizations.

Data availability

The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

Ethical approval was obtained from the “Hospital-University Ethics Committee of Fez” with ethical reference number: 22/20 (Additional file 1). Informed consent was obtained from each participant after explaining the objectives, benefits, and risks of this study. Moreover, this research was conducted following the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.