Development and validation of an instrument to evaluate the perspective of using the electronic health record in a hospital setting

Abstract

Background

Evaluating healthcare information systems, such as the Electronic Health Records (EHR), is both challenging and essential, especially in resource-limited countries. This study aims to psychometrically develop and validate an instrument (questionnaire) to assess the factors influencing the successful adoption of the EHR system by healthcare professionals in Moroccan university hospitals.

Methods

The questionnaire validation process occurred in two main stages. Initially, data collected from a pilot sample of 164 participants underwent analysis using exploratory factor analysis (EFA) to evaluate the validity and reliability of the retained factor structure. Subsequently, the validity of the overall measurement model was confirmed using confirmatory factor analysis (CFA) in a sample of 368 healthcare professionals.

Results

The structure of the modified HOT-fit model, comprising seven constructs (System Quality, Information Quality, Information technology Service Quality, User Satisfaction, Organization, Environment, and Clinical Performance), was confirmed through confirmatory factor analysis. Absolute, incremental, and parsimonious fit indices all indicated an appropriate level of acceptability, affirming the robustness of the measurement model. Additionally, the instrument demonstrated adequate reliability and convergent validity, with composite reliability values ranging from 0.75 to 0.89 and average variance extracted (AVE) values ranging from 0.51 to 0.63. Furthermore, the square roots of AVE values exceeded the correlations between different pairs of constructs, and the heterotrait-monotrait ratio of correlations (HTMT) was below 0.85, confirming suitable discriminant validity.

Conclusions

The resulting instrument, due to its rigorous development and validation process, can serve as a reliable and valid tool for assessing the success of information technologies in similar contexts.

Introduction

The emergence of the concept of the “Electronic Health Records (EHR)” centered on the patient dates back to the early 1990s when the idea of an interorganizational medical record was established [1]. Over time, this basic concept has gradually evolved in terms of the scope, purpose, and technology used for its implementation, although the fundamental idea is to provide a comprehensive, primarily transinstitutional record that supports the activities of healthcare facilities [2].

According to the International Organization for Standardization, the Electronic Health Records is defined as “a digital repository of patient data, that is securely stored and exchanged, and accessible to multiple authorized users. It contains historical, current, and prospective information, with its primary aim being to promote continuous, effective, and quality healthcare” [3].

Globally, the implementation of EHR is on a remarkable rise [4, 5]. The implementation and adoption of EHR are high in developed countries [6], such as the United States, where over 90% of hospitals use government-certified EHR [7]. Similarly, in 2018, nearly all healthcare facilities in France reported having a completed or ongoing development project for an “Electronic Health Records”, with modular functionality integrating various aspects of medical information input and retrieval [8]. Conversely, expansion of EHR in developing countries remains low and does not progress as extensively as the penetration of Information Technology (IT) in other sectors such as banking, commerce, education, and more [6]. However, the current literature presents several examples of successful EHR implementations in resource-limited hospitals, including in Kenya [9], Haiti [10], Malawi [11], and South Africa [12]. In a more general sense, the presence of an EHR in a hospital has gradually become a global standard to support the healthcare process [13].

Nevertheless, many studies report a mixed record of EHR utilization [14, 15]. The first, highly positive aspect is evident in a wide range of measurable benefits, including continuous, anytime, and anywhere access to patient information, promoting care continuity, informed clinical decision-making, and reducing medical errors, especially related to medication prescribing [16]. Moreover, EHR facilitates the accumulation of potentially exploitable digital clinical data for research and medical practice improvement [17]. Additionally, other research has shown that EHR significantly increased net monetary benefits per provider, led to cumulative net savings in hospitals, increased provider productivity, and improved efficiency [15, 18]. Furthermore, some studies have highlighted improved clinical outcomes following EHR adoption in hospitals, including reduced length of hospitalization, 30-day mortality reduction, decreased relative probability of myocardial infarction-related deaths, and improved outcomes for patients with renal diseases [19, 20].

The second aspect concerns a digital project with highly variable usage rates, generally underutilized and often poorly accepted [15], leading to new forms of medical errors [19]. This negative aspect reveals new risks, notably related to workarounds [21, 22], and increased time needed for data entry [23], and contributes to healthcare professionals’ workplace stress [24]. Similarly, Van Gemert-Pijnen et al. [25] described information technology (IT) used in healthcare, including EHR and hospital information systems, as “cutting-edge technology with low impact”.

Morocco’s experience toward healthcare digitalization began in the last decade with the implementation of various EHR systems from different vendors in most university hospital centers (CHU) [26], and more recently, in some primary healthcare facilities, aiming to develop an integrated national health information system. Since 2009, the CHU of Fez, where our study was conducted, has implemented an EHR system named HOSIX, developed by the Spanish vendor SIVSA. This system was deployed in several stages and encompasses several modules for clinical and administrative functions (i.e., medical records, pharmacy, laboratory, radiology, operating unit, emergency room, billing & recovery, appointment scheduling.). However, this system cannot be accessed by unauthorized users or taken outside the hospital [27].

Healthcare institutions invest heavily in IT projects to increase productivity, reduce costs, and enhance product and process quality [28,29,30]. The Scientific literature shows that 50 to 95% of IT projects are not successfully implemented, and 20 to 30% of EHR implementations fail within the first year [31, 32]. Indeed, the failure of such projects results in significant costs for the institution in terms of financial losses, time, and organizational resources [32, 33].

Since this work focused on the opinions of healthcare professionals, especially doctors and nurses, regarding the factors related to successful EHR adoption, it should help healthcare facilities minimize or remove implementation failures, potentially resulting in significant financial savings. Additionally, very few validated questionnaires have assessed the clinical use of EHR systems in hospitals [34, 35]. Similarly, to our knowledge, assessments of healthcare professionals’ experiences with EHR in the context of healthcare in Morocco have not been subject to in-depth study and analysis. Therefore, this study represents an original research endeavor with the primary objective of developing and validating a measurement instrument designed to assess the factors influencing the success of EHR system adoption in a public hospital in Morocco.

Methods

Study design

A two-tier cross-sectional survey design was adopted to design and validate the tool for assessing the success of EHR adoption in Moroccan university hospital settings. The first phase involved item identification, content validation, and establishing reliability and validity. The second phase aimed to confirm the factorial model of the instrument developed during the initial phase using another sample.

Measurement tool

The measurement instrument in our study was primarily developed based on HOT-fit model [36], with some modifications. First, in addition to the three dimensions (technological, human, and organizational) confirmed in Yusof’s study [37] and based on DiMaggio & Powell’s institutional theory [38], we added the environmental dimension as a potentially significant variable for evaluating the adoption of healthcare technology. This addition is supported by evidence from previous studies [39,40,41,42]. Second, the human dimension is mainly explained by “User Satisfaction”. Third, the concept of “Net Benefits” would be substituted with the concept of “Clinical Performance”, which serves as our dependent variable. Similarly, we selected items from a literature review focusing on evaluating EHR usage experiences in hospital settings [40, 42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66]. These items were then adapted based on various criteria, including coverage of the functional domain (health information technologies), item relevance to the dimension, psychometric properties (validity and reliability), and measurement scales. The initial version of the questionnaire comprised 37 items evaluated on a 7-point Likert scale ranging from (1) “strongly disagree” to (7) “strongly agree.” This scale provides respondents with sufficient options to accurately express their opinions [67], which enhances the reliability and validity of the measurement instrument [43]. The items were distributed across the different variables of the research model as follows: System Quality (SQ: 5 items), Information Quality (IQ: 5 items), IT Service Quality (ISQ: 5 items), Organization (ORG: 5 items), Environment (ENV: 4 items), User Satisfaction (US: 5 items), and Clinical Performance (PC: 8 items). After a validation process primarily divided into two phases (exploratory and confirmatory), the final version of the questionnaire for this study comprised 28 items (Additional fil 1: Survey questionnaire).

Study participants

The targeted respondents were healthcare professionals, including physicians, nurses, and healthcare technicians, who use EHR in the process of healthcare delivery. The inclusion criteria were as follows: working at the study sites (three hospitals of CHU Hassan II of Fez, namely, Specialties Hospital, Oncology Hospital, and Mother and Child Hospital); being part-time or full-time employees; and declaring the use of at least one EHR functionality to support their practice. Similarly, we selected all participants by convenience, and each received the questionnaire either in paper form or electronically via email. To ensure the independence of the samples, individuals who participated in the exploratory phase were excluded from the confirmatory phase, as recommended by Hair et al. [68]. In total, we received 164 responses for the exploratory phase between February and March 2021 and 368 responses between April and July 2021 for the confirmatory phase.

Content validity

After translating the initial version of the questionnaire into French and back-translating it into English by two independent translators, we sought input from a panel of seven experts specialized in the medical and informatics fields to assess content validity. The experts were tasked with determining whether the selected items were essential (to keep) or unnecessary (to remove), while examining the following criteria: relevance, clarity, completeness, and the suitability of each item individually and in relation to its factor, as well as the suitability of the item for the overall scale. They also had the option to suggest additional items they deemed essential but were not present in the initial version of the questionnaire. Moreover, they could suggest rephrasing items. During direct and individual interviews with our panel of experts, all their suggestions were individually analyzed. The main suggested modifications were to eliminate two items (SQ5) and (ORG5) as they were not within the knowledge scope of end-users of EHR. However, no additional items were recommended by the experts.

Research ethics approval

Ethical approval for our research was obtained from the hospital-university ethics committee of Fez, assigned the reference number “22/20”. Additionally, we obtained permission to conduct research from the administrators of the participating hospital facilities. Concerning the participants in our study, they will provide voluntary and informed consent after being informed of the study’s objectives, benefits, and risks. They will also have the freedom to withdraw at any time without the need to inform us.

Data analysis

Statistical data analysis was conducted using SPSS version 25 and AMOS version 26. First, the sociodemographic data of the samples were analyzed using descriptive statistics, including means and percentages. Next, the factorial structure and internal reliability of the questionnaire were assessed on the first sample (N = 164) using exploratory factor analysis (EFA). The Kaiser-Meyer-Olkin (KMO) index and Bartlett’s sphericity test were used to assess the suitability of the sample data for factor analysis [68]. Additionally, Mardia’s test was used to assess the violation of the multivariate normality assumption of the variables. To find the adequate number of factors for extraction, two tests were used: The minimum average partial (MAP) test [69] and the scree-plot test [70].

Exploratory Factor Analysis was performed using the Principal Axis Factoring method as the extraction method and Promax rotation to obtain the most parsimonious factor structure. An initial loading threshold of at least 0.30 was used. Items that did not exceed this threshold or loaded significantly on multiple factors were excluded from all factors [71]. The measurement instrument’s reliability was evaluated through an assessment of its internal consistency, determined by calculating Cronbach’s alpha coefficient. Finally, the theoretical model of the instrument (32 items) was assessed using confirmatory factor analysis (CFA) on a sample of 368. CFA was performed using the maximum likelihood (ML) estimation method of the normal theory with Bollen-Stine bootstrap based on 5000 samples [72]. Internal consistency was assessed by calculating the composite reliability (CR), convergent validity was evaluated using the average variance extracted (AVE), and discriminant validity was evaluated using the Fornell-Larcker criterion and heterotrait-monotrait ratio (HTMT) [73]. The assessment of the measurement model’s fit was conducted using three categories of indices: incremental fit (CFI, TLI, NFI); absolute fit (RMSEA, GFI, AGFI); and parsimonious fit (Chisq/df).

Findings

Participant characteristics

The research was conducted on two separate samples of healthcare professionals from different departments at the Hassan II University Hospital in Fez, all of whom use the EHR system in their daily activities. Participants were invited voluntarily to take part in the survey after securing permission from hospital management and their department heads. The first sample (N = 164) underwent exploratory factor analysis, while the second (N = 368) was examined through confirmatory factor analysis. Both samples had similar sociodemographic characteristics, as detailed in Table 1. The average age was 28.34 ± 4.53 for the first sample and 29.21 ± 5.33 for the second sample. The gender distribution showed that 61% of participants in the first sample were female, compared to 62.5% in the second sample. Regarding their clinical positions, the majority of EHR users in both samples were physicians (92.7% vs. 86.1%). Similarly, a significant portion of respondents in both samples worked at the Specialties Hospital of Hassan II University Hospital in Fez (75.6% vs. 78%). Furthermore, nearly half of the healthcare professionals in both samples (53.7% vs. 47.3%) had 1 to 4 years of experience using the EHR at the Hassan II University Hospital in Fez. Moreover, the majority of respondents in both samples had not received training on the use of the EHR (86.6% vs. 85.1%).

Table 1 Sociodemographic characteristics of the participants

Exploratory factor analysis

Before proceeding with the factor analysis, we assessed the adequacy of the sampling using the Kaiser–Meyer–Olkin (KMO) test. The overall KMO value was 0.866, which is above the threshold of 0.7 suggested by Kaiser [74] and Carricano et al. [75]. Furthermore, the determinant of the correlation matrix was 4.673E-1, exceeding the threshold of 1E-05, which allows us to reject the presence of multicollinearity among the variables according to Field [76]. Additionally, Bartlett’s test of sphericity (χ2 = 3413.379, df = 595, p < 0.001) showed that the correlations between the items were statistically significant, supporting the appropriateness of conducting exploratory factor analysis.

The scree plot test curve cuts off at seven factors before starting the straight line (Fig. 1), and Velicer’s MAP test confirmed the relevance of a seven-factor solution, with eigenvalues exceeding 1, explaining 65.80% of the total variance, which is acceptable. According to Gorsuch [77] and Hair et al. [68], a factor solution explaining at least 60% of the total variance is typically sufficient for exploratory factor analysis (EFA) in the social sciences, where information is often less precise.

Fig. 1

Scree plot for factors of the instrument

Factor analysis, with principal axis factoring as the extraction method and Promax rotation, yielded an initial structure of the factor matrix (Table 2). Based on Hair et al. [71] and Tabachnik & Fidell [78], an initial loading threshold of at least 0.30 was used. Subsequently, items that did not surpass this threshold or showed significant loadings on more than one factor were rejected to maintain simple structure [79]. After each iteration, the examination of the rotated factor matrix revealed significant factor loadings and changes in the communalities’ values. If an item was not significant, it was removed from the measurement model, and the analysis was rerun [80]. This process was repeated multiple times until a structured rotated factor matrix was achieved, with all communalities of the remaining items exceeding 0.20, as recommended by Child [81].

Therefore, items SQ3 (with a communality below 0.2), SQ4 (exhibiting too low factor loading, i.e., below 0.30), and CP1 (having significant cross-loading on two different factors) were excluded from this model. Despite losing three items in the factor composition, the refined model retained the seven-factor structure from our research model version (System Quality, Information Quality, IT Service Quality, Organization, Environment, User Satisfaction, and Clinical Performance). Thus, the factor names were retained, and the seven-factor model explained 68% of the variance (Table 2), which is considered acceptable according to Child [81].

Internal consistency

The questionnaire’s reliability was assessed in terms of internal consistency by calculating Cronbach’s alpha coefficient for each dimension and item-total (corrected) correlations for each item (Table 2). Overall, the values are satisfactory, with all alpha coefficients exceeding the threshold of 0.7, thus reinforcing the psychometric qualities of the internal reliability of the questionnaire [82]. Furthermore, the minimum item-total correlation calculated was 0.416 (Table 2), surpassing the 0.30 threshold as suggested by Hair et al. [68]. According to both methods of assessing internal consistency, all constructs and items in our questionnaire demonstrated sufficient and acceptable internal consistency.

Confirmatory factor analysis

Convergent validity

The results of the confirmatory factor analysis of the first-order measurement model showed that the lowest factor loadings were 0.54, observed for items ORG1 and CP8. Furthermore, all standardized regression coefficients (Fig. 2) exceeded the recommended threshold of 0.50, as indicated by Hair et al. [68].

Table 2 Factor loadings, communalities (h2), item-total correlation, and Cronbach’s alpha of the measurement tool

Fig. 2

CFA measurement model (final version M5). Abbreviations. SQ: System Quality; IQ: Information Quality; ISQ: Information technology Service Quality; US: User Satisfaction; ORG: Organization; ENV: Environment. CP: Clinical Performance;

Furthermore, the reliability and convergent validity of the questionnaire were also confirmed, with high and acceptable values for composite reliability (CR) (0.75–0.89) and average variance extracted (AVE) (0.51–0.63), respectively (Table 3). As a result, the entire factor analysis process was validated, and the measurement tool adapted satisfactorily to the data.

Table 3 Construct validity and reliability

Discriminant validity

Discriminant validity was evaluated following the Fornell and Larcker [73] criterion, which involves checking if the average variance extracted (AVE) is greater than the intercorrelations values between latent variables. In Table 3, the values in bold represent the square root of the AVE for each factor, while the other values correspond to inter-correlations between latent variables. The most important correlation observed among factors was 0.66 (between US and CP), while the lowest value among the square roots of the AVE values was 0.71. Therefore, we can affirm the discriminant validity of all factors in the model because the diagonal values of the matrix exceeded the values located outside the diagonal in the corresponding rows and columns.

Moreover, the maximum shared variance (MSV) was lower than the average variance extracted (AVE). Discriminant validity can also be assessed using the HTMT test. Thus, a value of the HTMT test below 0.85 or 0.90 indicates good discriminant validity [72]. As indicated in Table 4, all values in the matrix are below 0.85, confirming the discriminant validity between all factors in the proposed model.

In conclusion, all conditions required to ensure the validity of the constructs in the overall measurement model have been met. Reliability (measured by Cronbach’s alpha and Joreskog’s Rho) is very satisfactory. Convergent validity (assessed by average variance extracted) as well as discriminant validity (evaluated according to the Fornell and Larcker criterion and the HTMT test) of the constructs are all acceptable.

Table 4 Discriminant validity: Heterotrait-Monotrait (HTMT) Criterion results

Assessment of the overall measurement model’s fitness

To assess the quality of the current model, we will use three categories of fit indices, namely absolute fit, parsimony, and incremental fit indices. These fit indices are considered acceptable if their values meet the following statistical fit thresholds: the chi-square to degrees of freedom ratio (χ2/df) should be less than 3, the comparative fit index (CFI) should be greater than 0.95, the values of the normed fit index (NFI), Tucker Lewis index (TLI), and goodness-of-fit Index (GFI) should be equal to or greater than 0.90, the adjusted goodness-of-fit index (AGFI) should be greater than 0.85, and the root mean square error of approximation (RMSEA) should be less than 0.05 [68, 83].

Based on these acceptance thresholds, some values in the initial model M1 did not conform to acceptable levels (Table 5). To improve the quality of these fit indices, we made several modifications. Specifically, we deleted items with poor factor loadings (less than 0.5), as recommended by Hair et al. [68]. Additionally, we correlated error terms within the same construct that exhibited high modification indices (MI) (greater than 10), as suggested by Collier [84] and Byrne [85]. Accordingly, four items (ISQ5 = 0.41; ORG4 = 0.44; ENV1 = 0.43; CP7 = 0.42) were removed. Furthermore, we introduced covariances of error terms associated with items CP6 and CP8 (err27 ↔ err28; MI = 48.92), items ISQ1 and ISQ2 (err5 ↔ err6; MI = 20.41), and items IQ2 and IQ5 (err3 ↔ err17; MI = 13.77). Table 5 summarizes the transition from model M1 to model M5 with the modifications made. Ultimately, the overall measurement model (M5) met the required thresholds and thus provided a better fit to the empirical data than the other models.

Table 5  Goodness-of-fit statistics for the modified primary-order CFA models

Discussion

The primary aim of this study was to design and validate a tool for evaluating the success of EHR adoption in the Moroccan context. To achieve this, two separate samples consisting of 164 and 368 healthcare professionals from the University Hospital Hassan II of Fez were studied combing both EFA et CFA.

The majority of healthcare professionals in the sample were physicians (89.4%, combining 86.1% and 92.7%). This can be explained by the specificities of the hospitals in our study, which generally restrict access to EHR to healthcare professionals directly involved in patient care, particularly doctors and nurses. However, even access for the latter category remains limited, as the nursing care module incorporating EHR has not yet been fully implemented in all departments of the targeted hospitals.

Through exploratory and confirmatory factor analyses, the final version of our instrument (28 items) was validated with an overall structure comprising seven constructs: System Quality, Information Quality, IT Service Quality, User Satisfaction, Organization, Environment, and Clinical Performance. Indeed, the reliability and validity values for all constructs of our instrument were in line with the needed standards [39]. These results align with a study conducted by Ayuni et al. [86], who used a questionnaire consisting of 37 items based on the HOT-fit model to evaluate the success of an e-learning system in an educational institution in Indonesia. The authors found that the composite reliability value and Cronbach’s alpha for each construct (User Satisfaction, System Use, System Quality, Information Quality, Service Quality, Organizational Structure, Environment, and Net Benefit) exceeded 0.6, and the Average Variance Extracted (AVE) value was greater than 0.5, surpassing the recommended thresholds [87,88,89].

Furthermore, the overall fit quality, as indicated by the Goodness-of-Fit Index (GFI), demonstrated that the constructs in this study effectively support the research model. This finding is consistent with the study conducted by Erlirianto et al. [90], who validated a conceptual model aimed at evaluating EMR system adoption in an Indonesian hospital through a questionnaire comprising 57 items distributed across four aspects (technology, human, organization, and net benefits). Their model, developed based on the HOT-fit evaluation framework by Yusof et al. [36] achieved a GFI value of 0.943, with all items meeting the thresholds for reliability and validity.

Similarly, in the study by Lian et al. [39], who used a questionnaire consisting of 38 items to examine critical factors affecting the decision to adopt cloud computing technology in Taiwan, the authors concluded on the importance of four factors (technological, human, organizational, and environmental) in the decision to adopt cloud computing. However, a recent study by Salleh et al. [43], which focused on evaluating the effect of adopting an EHR system on the performance of Malaysian healthcare professionals, only emphasized five constructs (system quality, records quality, service quality, knowledge quality, and effective use) that have a significant impact on healthcare providers’ performance.

Overall, the construct structure of the instrument proposed for our study has shown similar psychometric properties to the results of other studies [86, 90]. The difference lies in the degree of reliability of the factors and the loading factors for each indicator. Additionally, all the factors in our instrument are positively and significantly correlated with each other, and the order of the correlation strength of the independent variables (SQ, IQ, ISQ, ORG, ENV, US) with the dependent variable (CP) was the human factor: user satisfaction (US), the organizational factor (ORG), the technological factor (SQ, IQ, ISQ), and the environmental factor (ENV).

These findings highlight the importance of the user satisfaction (r = 0.66, p < 0.001) in the successful adoption of the EHR system in Morocco hospitals, which corroborates the conclusion of Salleh et al. [43], who showed that improving the quality of healthcare professionals’ knowledge positively impacts their performance when using EHR systems. Similarly, studies by Garcia-Smith & Effken [91], Yu & Qian [44], and Tilahun & Fritz [45] concluded that healthcare staff satisfaction with clinical information system use is the best predictor of perceived net benefits from EHR adoption. However, Erlirianto et al. [90], and Ojo [46] did not find an association between user satisfaction and the perceived net benefits for healthcare professionals.

Regarding the organizational factor, our study identified it as the second most powerful predictor influencing clinical performance (r = 0.555, p < 0.001). This can be attributed to the importance of its indicators, such as elements related to senior management and supervisor support, in the EHR system adoption process. These findings align with those of Abdelkhoda et al. [47], Chen & Hsiao [48] and Aldosari et al. [49], who reported the significant influence of organizational factors on health practitioners’ attitudes towards EMR adoption in hospitals.

Furthermore, our findings revealed a positive and significant association between the technological dimension, through its three constructs (Information Quality, r = 0.46, p < 0.001; System Quality, r = 0.43, p < 0.001; IT Service Quality, r = 0.34, p < 0.001), and clinical. This result aligns with studies conducted by Salleh et al. [43] and Mijin et al. [50], which demonstrated the positive effect of data quality and EHR feature quality on the performance of healthcare providers. Similarly, Bossen et al. [51], and Hung et al. [52] found that service quality had a stronger effect on physicians’ perceived usefulness of EHR.

Finally, the correlation between the environmental dimension and clinical performance was positive and significant (r = 0.22, p < 0.001). This finding aligns with studies conducted in hospitals by Jianxun et al. [40], Ahmadi et al. [41], Abdekhoda et al. [42], and Kuo & Wen [53] which found a significant influence of environmental factors, i.e., government policy and vendor support, on healthcare providers’ attitudes towards EHR.

Overall, these interpretations should consider certain methodological considerations, such as the type of factor analysis employed, sample size, and the study’s context. To ensure robust conclusions about the reliability and validity of our measurement tool for future applications, both exploratory and confirmatory analyses were conducted in this study. Additionally, the seven-construct measurement model displayed excellent fit according to the predefined threshold values for absolute, incremental, and parsimonious fit indices as supported by robust references in the literature [68, 83, 92,93,94,95].

The findings presented in this paper suggest that this questionnaire will provide invaluable insights for policy-makers and health information managers, enabling them to evaluate the success of EHR adoption in hospitals and to identify effective strategies for EHR system implementation, particularly in developing countries. Moreover, this instrument is recommended for use by researchers conducting evaluative studies in the field of Health Information Systems. Additionally, this study offers a methodologically rigorous process for the validation of future instruments.

Conclusions

This study provides a validation of a measurement tool designed to assess the factors influencing the successful adoption of electronic health records by healthcare professionals based on a modified HOT-fit model. We examined its psychometric properties with two samples of 164 and 368 healthcare professionals using EHR at the Hassan II University Hospital in Fez. Similarly, the adequacy indices obtained using confirmatory factor analysis showed a reasonable fit of the model for our study questionnaire.

However, it is important to note that this research has some limitations. Sampling was carried out at a single university hospital, focusing on a convenience sample primarily composed of physicians. Therefore, the results cannot be extrapolated to all healthcare professionals across the Moroccan territory. It is necessary to conduct further studies, whether longitudinal or cross-sectional, on larger samples comprising different profiles of healthcare professionals working in public or private healthcare institutions to enhance the understanding of determinants of EHR adoption success. Additionally, it is necessary to develop the instrument to encompass various other facets related to the use of EHR systems, such as medical research, clinical activity management and audit, financial benefits, staff workload resistance to change, and more.

In conclusion, although Moroccan hospitals have various hospital information systems with varying levels of deployment for EHR systems [27], our results (a 28-item questionnaire) provide a reference basis for decision-makers, and managers to improve, and optimize the impact of EHR systems on hospital management, healthcare quality, and healthcare professional satisfaction. However, it should be noted that “the development of a questionnaire should be considered an ongoing process, where each revision is guided by new validation studies“ [35].