The relationship between nursing informatics competencies and clinical decision-making among registered nurses working at tertiary hospitals

Abstract

Aim

This study aims to explore the relationship between nursing informatics competencies and clinical decision-making among nurses in Jordan.

Design

A cross-sectional, descriptive-correlational design was used with a sample of 249 registered nurses from three tertiary governmental hospitals in Jordan, utilizing the Self-Assessment of Nursing Informatics Competencies Scale and the Clinical Decision-Making in Nursing Scale.

Results

Nurses reported moderate-to-high informatics competencies (M = 3.17, SD = 0.76) and decision-making abilities (M = 3.30, SD = 0.94). Total nursing informatics competencies score showed a significant positive correlation with all clinical decision-making domains, with the strongest correlation observed for critical thinking (Pearson r = 0.57, P < 0.05), followed by clinical judgment (Pearson r = 0.52, P < 0.05).

Conclusion

The findings underscore a significant link between nursing informatics competencies and clinical decision-making, with professional experience, system usage frequency, and informatics training serving as key predictors. These results highlight the importance of targeted interventions to enhance informatics skills and support effective clinical decisions.

Keywords

Nursing informatics clinical decision-making health information technology nurses competencies

Introduction

Healthcare systems worldwide are undergoing rapid transformations due to technological advancements.^1,2 Globally, the adoption of health information technology (HIT) in clinical settings is rapidly expanding.³ It is well known that nurses represent a large proportion of healthcare professionals. They play a pivotal role in delivering patient care and making critical decisions that directly impact health outcomes.⁴

Nursing informatics (NI) is a cornerstone of modern healthcare delivery. It was defined as the integration of nursing, computer, and information sciences,^5,6 in which real-time patient data access enhances decision-making and reduces medical errors.⁶ Also, NI was also linked to improvements in patient safety and the efficiency of care delivery systems.⁷ However, ensuring that nurses possess sufficient informatics competencies remains a significant challenge. It was documented that up to 40% of nurses reported moderate-to-low health informatics proficiency.⁸ For instance, 35% of nurses felt they lacked adequate training to navigate electronic health record (EHR) systems efficiently in acute care settings in the USA,⁹ hindering their ability to make evidence-based decisions and compromising quality of care.

Clinical decision-making (CDM) in nursing is a complex and multifaceted process that requires synthesizing knowledge, experience, critical thinking, and evidence-based practices.¹⁰ It was documented that integrating NI tools, such as using EHRs in clinical workflows, can significantly enhance CDM capabilities.¹¹ However, the adoption of NI in the Middle East has been slower compared to other regions, with significant disparities in training and infrastructure.^12,13 In Jordan, a Middle Eastern country, 43% of nurses felt confident in their ability to use NI systems to support CDM.¹⁴ This could be further complicated by the high patient-to-nurse ratio, which exceeds the global standard of 4:1 in many healthcare settings in the country.¹⁵ The adoption of HIT in the country has outpaced the training of healthcare professionals.¹⁴ However, a few international studies have explored this connection, indicating a positive relationship. For example, research has shown that nurses with higher informatics competencies are better able to interpret electronic data and use decision support tools, which enhances the quality of clinical decisions.¹⁶ A study shows that students with better informatics competencies felt more confident in using clinical systems and making evidence-informed decisions.¹⁷

Nurses in Jordan primarily use Hospital Information Systems (HISs) such as Hakeem, which is a national EHR system implemented across many public healthcare facilities. In private hospitals, systems like Cerner, Epic, or Medisys may be used, depending on the institution.¹⁸ These systems support functions such as electronic documentation, medication administration records, and clinical decision support tools. Nevertheless, the impact of NI competencies on CDM among nurses in Jordan remains underexplored, creating a significant gap in the literature. Therefore, this study aimed to explore the relationship between NI competencies and CDM among nurses in Jordan, with particular attention to identifying predictors of CDM performance.

Materials and methods

Study design

A cross-sectional, descriptive-correlational design was used in this study.

Study settings

The study was conducted in three major governmental hospitals in Jordan, all of which utilize modern HISs. These are integrated platforms that encompass multiple subsystems, including EHRs, Clinical Decision Support Systems (CDSS), and Electronic Medication Administration Records (eMARs). In practice, these components often function as interconnected modules within a single HIS, rather than as standalone entities.

In the context of this study, nurses in Jordan primarily engage with the following HIS platforms:

Hakeem: A national EHR system implemented across public healthcare facilities. Hakeem integrates various functionalities such as electronic documentation, medication tracking through eMAR modules, laboratory and imaging orders, and basic clinical decision support features.

Cerner, Epic, and Medisys: Predominantly used in private sector hospitals. These systems provide a unified interface that includes EHR functionalities along with embedded CDSS (e.g. drug interaction alerts, clinical guidelines) and eMAR features (e.g. barcode medication administration, real-time documentation of drug administration).

First is the Al-Hussein Hospital, an educational and referral hospital that serves over 500,000 people and has a capacity of 350 beds. Second is the Prince Hamza Hospital, a tertiary hospital with 434 beds, offering medical and surgical specialties. This hospital employs approximately 800 nurses. Third is the Al-Basheer Hospital, the largest governmental hospital in the country, with a capacity of 1925 beds and more than 1500 nurses supporting a wide range of tertiary healthcare services.¹⁹

Study population

The target population for this study included all registered nurses working in governmental hospitals in Jordan who actively utilize NI systems in their daily practice. The accessible population consisted of nurses working at the three selected governmental hospitals. Participants were eligible for inclusion if they were bedside registered nurses, held a bachelor's degree in nursing (as this is the minimum qualification required for professional nursing practice in Jordan), actively used HISs in their daily work, and were willing to participate in the study. Nurses in administrative or leadership roles, informatics nurses, and those not directly involved in patient care were excluded, as the study focused on the experiences of bedside nurses engaged in CDM using HIS.

Sampling and sample size

A convenient sampling technique was employed to select nurses. It was appropriate for the study due to its practicality and ability to target nurses actively using HIS at the selected hospitals. It allowed for efficient data collection within the constraints of the study.²⁰ The sample size was calculated using G*Power software version 3.1.9.7. Using an alpha level of 0.05, a statistical power of 0.80, and a small effect size of 0.15 for the Pearson correlation test, the minimum required sample size was determined to be 273 participants. To account for potential non-response, the sample size was increased by 15%, resulting in a total of 321 participants.²¹

Instruments

The first part of the questionnaire was designed by the researchers to collect sociodemographic and work-related data. The second part of the questionnaire is the Self-Assessment Nursing Informatics Competencies Scale (SANICS).²² It is a self-assessment tool that consists of 30 items, structured to evaluate NI competencies. The scoring system is based on a 5-point Likert scale, with scores ranging from 1 (not competent) to 5 (expert). The SANICS includes five distinct subscales: (1) clinical informatics roles (5 items), which assess participation in system design, implementation, and evaluation; (2) basic computer knowledge and skills (15 items), focusing on foundational technology and system navigation abilities; (3) applied computer skills (4 items), which evaluate practical skills for integrating technology into nursing practice; (4) clinical informatics attitude (4 items), addressing perceptions and attitudes toward informatics use; and (5) wireless device skills (2 items), which measure proficiency in using mobile technology for nursing tasks. A higher score indicates a higher level of competencies in the respective domain. The cutoff points for interpretation classify scores as follows: a mean score of 3 or higher on the total scale or any subscale signifies NI competencies, while scores below 3 indicate areas requiring further development.²² The SANICS has demonstrated strong psychometric properties. Its validity was established through robust content validity methods, confirming that the scale items adequately represent the domains of NI competencies. The reliability of the tool was supported by high internal consistency, with Cronbach's alpha coefficients ranging from 0.89 to 0.94 across its subscales.²²

The third part of the questionnaire was the Clinical Decision-Making Nursing Scale (CDMNS-PT^©).²³ It was translated and validated by Duarte and Dixe,²⁴ consisting of 23 items, each rated on a 5-point Likert scale, ranging from 1 (never) to 5 (always). The CDMNS-PT^© is divided into three subscales: (1) definition of the problem and development of the objectives (12 items), assessing situational factors, patient input, and professional values; (2) search and data processing (5 items), focusing on gathering and analyzing relevant information for decision-making; and (3) assessing alternatives, planning, and implementation of action (6 items), assessing outcomes, planning actions, and executing decisions. The scale's total score ranges from 23 to 115, with higher scores indicating more positive perception of CDM capabilities. The Cronbach's alpha of the total scale was 0.74 and 0.85 as reported in another study,²⁵ indicating very good internal consistency.

Ethical considerations

This research involving human subjects adheres to all relevant national regulations and institutional policies, follows the principles of the Helsinki Declaration, and has received approval from the Institutional Review Boards at Al-Zarqa University and the Jordan Ministry of Health. Written informed consent was obtained from all participants before the study. They were fully informed about the study's objectives, potential risks, and benefits and were made aware that participation was voluntary and they could withdraw at any time. Participants’ confidentiality and anonymity were ensured by assigning each a unique identification number. Data were securely stored on a password-protected computer, accessible only to the researchers.

Data collection process

Permission to conduct the study was obtained from the administration department of the hospitals and units involved. Researchers then approached eligible participants in a private room within their workplaces and invited them to complete the survey via an online Google Form. A unique link to the questionnaire was provided to the participants either in person or via email, depending on the participants’ preference. Data were collected online between September 15, 2023, and December 15, 2024. The online format allowed flexibility and convenience, enabling participants to complete the survey at their own pace and in their chosen location

Data analysis

Following data entry, a rigorous data-cleaning process was conducted to identify and rectify any inconsistencies or missing values. Then, data were analyzed using SPSS version 27.²⁶ Descriptive statistics were used to describe sociodemographic characteristics. Also, inferential statistical methods were applied, including Pearson's correlation to examine the relationships between the variables. Statistical significance was set at P < 0.05. Correlation is used to explore the strength and direction of the relationship between NI competencies and CDM. Regression analysis is then employed to assess the predictive power of NI competencies on CDM.

Results

A total of 249 nurses filled out the questionnaire, showing a response rate of 78%. The average age of the nurses was 33.2 years (SD = 4.23), with an average professional experience of 9.7 years (SD = 1.03). Moreover, around half of the nurses were males (53.8%), and 59.0% worked rotating shifts. The nurses were employed across various hospital units: medical-surgical units (32.0%), emergency rooms (24.0%), and intensive care units (16.0%).

In terms of NI systems, approximately one-third of the nurses reported using these systems (89.5%) “always,” while others used them “often” (20.5%). Regarding competency in using NI, a percentage of 56.2% of nurses rated their competencies as “good,” followed by “excellent” (20.1%). Concerning the perceived impact of NI systems on nurses’ CDM processes, most nurses reported that these systems impacted their CDM either “significantly” (33.3%) or “very significantly” (30.9%). Moreover, the majority of nurses (79.1%) reported having undergone training in NI, whereas 20.9% indicated that they had not received such training. Lastly, the most commonly used NI systems were EHRs (75.5%), followed by CDSS (26.5%) (see Table 1).

Table 1.

Sociodemographic characteristics of the study sample (N = 249).

Characteristic	n	%
Gender
Male	134	53.8%
Female	115	46.2%
Current shift pattern
Day shift	57	22.9%
Night shift	45	18.1%
Rotating shift	147	59.0%
Working units
Emergency room	60	24.0%
Intensive care unit	40	16.0%
Medical-surgical	80	32.0%
Obstetrics/gynecology	24	10.0%
Pediatrics	30	12.0%
Outpatients	15	6.0%
Frequency of using nursing informatics systems during typical work shifts
Never	-	-
Rarely	-	-
Sometimes	-	-
Often	51	20.5%
Always	198	89.5%
Nurses’ rating of their competency in using nursing informatics
Very poor	-	-
Poor	34	13.6%
Fair	25	10.0%
Good	140	56.2%
Excellent	50	20.1%
The belief that nursing informatics systems impact their clinical decision-making process
Not at all	7	2.8%
A little	25	10.0%
Moderately	57	22.9%
Significantly	83	33.3%
Very significantly	77	30.9%
Received nursing informatics system training
Yes	197	79.1%
No	52	20.9%
Ranking of the nursing informatics systems usage frequency by nurses
EHR	188	75.5%
CDSS	66	26.5%
Patient monitoring systems	68	27.3%
Medication administration systems	61	24.5%
Variable	M	SD
Age (years)	33.2	4.23
Professional experience (years)	9.7	1.03

n: number of nurses; %: percentage; M: mean; SD: standard deviation; EHR: electronic health records; CDSS: Clinical Decision Support Systems.

Nurses reported a total mean score for NI competencies of 3.04 (SD = 0.80). The highest mean subscore was for competencies in finding resources for ethical decision-making (M = 3.48, SD = 1.26), and the lowest mean subscore was for participating in system selection and evaluation (M = 2.11, SD = 1.22). The mean scores of basic computer skills proficiency and strengths in searching and downloading content were 3.27 (SD = 0.83) and 3.53 (SD = 1.36), respectively. Furthermore, the mean score of attitudes toward clinical informatics was 3.00 (SD = 0.97), reflecting positive attitudes toward health computing. The Application of Computer Skills in Clinical Settings subscale had a mean of 3.14 (SD = 0.97). In general, the overall mean SANICS score was moderate-to-high (M = 3.17, SD = 0.76) (see Table 2).

Table 2.

Descriptive statistics of informatics competencies among nurses working in governmental hospitals (N = 249).

Item	M	SD
Total SANICS score	3.17	0.76
Clinical informatics role	3.04	0.80
As a nurse, I am able to find resources in the computer available for ethical decision-making in future clinical services	3.48	1.26
As a nurse, I am able to play the role of a supporter for the leader (manager) so that changes and the concept of information science may be incorporated into my professional area in future clinical services	3.33	1.35
As a nurse, I am able to encourage myself and others to use information systems or application programs	3.16	1.24
As a nurse, I am able to improve the integrity and accessibility of information with consideration for confidentiality and legal, ethical, and security issues but not restricted by them in future clinical services	3.11	1.20
As a nurse, I am able to participate in the processes of selection, design, application, and evaluation of an information system in future clinical services	2.11	1.22
Basic computer skills	3.27	0.83
I am able to search and download contents of interest	3.53	1.36
I am able to use a multimedia presentation program	3.48	1.45
I am able to use a word processor	3.47	1.47
I am able to use an operating system (e.g. folder copy, deletion, edition)	3.45	1.44
I am able to use a communication network (e.g. file server, www) for system search	3.44	1.44
I am able to use a computer system in a safe way	3.41	1.40
I am able to use a database program to build a simple database or to make a table	3.29	1.39
I am able to use electronic telecommunication devices (e.g. modem) for communication with other systems	3.28	1.27
I am able to use computer peripheral devices (e.g. CD-ROM)	3.28	1.34
I am able to use a presentation program (e.g. PowerPoint) for making slides or screens	3.19	1.33
I am able to use a database application program to enter and search information	3.14	1.25
I am able to manipulate the Windows system (e.g. file handling, printer setting)	3.07	1.28
I am able to do an online literature search	3.04	1.31
I am able to view the basic configuration of a computer system	3.04	1.31
I am able to solve minor problems in application programs	2.96	1.28
Attitudes toward clinical informatics	3.00	0.97
I believe that health computing, namely, computer-based medical services, will be popularized further	3.06	1.32
I believe that the computer is merely a tool for providing better nursing services, and it cannot replace human functions	3.04	1.34
I do not think that a nurse has to be a computer programmer for the effective use of the computer	3.06	1.30
I believe that it is worthwhile for a nurse to participate in the design, selection, application, and evaluation of application programs	2.88	1.20
Application of computer skills	3.14	0.97
I am able to log in to the patient medical information system (authorization using personal ID and password)	3.22	1.31
I am able to use an application program for developing a test tool.	3.14	1.30
I am able to search data in the patient medical information system	3.14	1.34
I am able to use an application program for diagnosis coding	3.05	1.28
Wireless device skills	3.22	1.14
I am able to use wireless devices (e.g. PDA, smartphone) for data input	3.26	1.51
I am able to use wireless devices to search and download resources for patient safety and quality nursing services	3.18	1.29

M: mean; SD: standard deviation; SANICS: Self-Assessment Nursing Informatics Competencies Scale.

The mean score of perceived CDM competencies among the nurses was 3.30 (SD = 0.94). Specifically, the mean score for knowledge utilization was 3.22 (SD = 0.91); for critical thinking, it was 3.37 (SD = 0.96); for clinical judgment, it was 3.21 (SD = 0.91); and for ethical considerations, it was 3.48 (SD = 0.99) (see Table 3).

Table 3.

Descriptive statistics of clinical decision-making competencies among nurses (N = 249).

Item	M	SD
Total Clinical Decision-Making in Nursing Scale score (CDMNS-PT)	3.30	0.94
Knowledge utilization	3.22	0.91
I consider the patient's preferences when making decisions	3.47	1.47
I consider the patient's family's preferences when making decisions	3.45	1.44
I consider the patient's socioeconomic status when making decisions	3.41	1.40
I consider the patient's psychological state when making decisions	3.14	1.30
I consider the patient's educational level when making decisions	3.14	1.34
I consider the patient's occupation when making decisions	3.06	1.30
I consider the patient's prognosis when making decisions	2.88	1.21
Critical thinking	3.37	0.96
I consider the patient's religious beliefs when making decisions	3.59	1.48
I use my intuition to make decisions	3.55	1.42
I consider the patient's spiritual beliefs when making decisions	3.43	1.46
I consider the patient's cultural background when making decisions	3.34	1.83
I consider the patient's emotional state when making decisions	3.22	1.41
I consider the patient's marital status when making decisions.	3.06	1.38
Clinical judgment	3.21	0.91
I consider the patient's age when making decisions	3.39	1.42
I consider the patient's physical state when making decisions	3.35	1.37
I consider the patient's sexual orientation when making decisions	3.31	1.41
I consider the patient's social support network when making decisions	3.16	1.38
I consider the patient's previous experiences when making decisions	3.12	1.33
I consider the patient's health insurance coverage when making decisions	2.90	0.99
Ethical considerations	3.48	0.99
I consider the patient's gender when making decisions	3.39	1.36
I consider the patient's diagnosis when making decisions	3.42	1.37
I consider the patient's treatment options when making decisions	3.77	1.45
I consider the patient's quality of life when making decisions	3.34	1.40

M: mean; SD: standard deviation; N: sample size; CDMNS-PT: Clinical Decision-Making in Nursing Scale.

The clinical informatics role subscale of SANICS demonstrated significant positive correlations with the total CDMNS scores (Pearson r = 0.52, P < 0.05). In addition, this subscale was positively correlated with all subscales of the CDMNS, including clinical judgment (r = 0.63, P < 0.01), critical thinking (r = 0.57, P < 0.05), knowledge utilization (r = 0.53, P < 0.01), and ethical considerations (r = 0.54, P < 0.05), and with the total CDMNS score. Furthermore, the basic computer skills subscale significantly correlated with clinical judgment, knowledge utilization, and ethical considerations subscales. Moreover, the attitudes toward clinical informatics subscale were significantly correlated with the critical thinking (r = 0.42, P < 0.01) and ethical considerations (r = .41, P < 0.01) subscales. The application of computer skills subscale had significant positive correlations with clinical judgment (r = 0.39, P < 0.05), critical thinking (r = 0.36, P < 0.05), and knowledge utilization (r = 0.34, P < 0.05) subscales. The wireless device skills subscale also showed significant correlations with the clinical judgment (r = 0.40, P < 0.05), knowledge utilization (r = 0.32, P < 0.05), and ethical considerations (r = 0.34, P < 0.05) subscales (Table 4).

Table 4.

Bivariate correlation statistics between SANICS subscales and CDMNS subscales (N = 249).

Variable	Clinical informatics role	Basic computer skills	Attitudes toward clinical informatics	Application of computer skills	Wireless device skills	Total SANICS score
Clinical judgment	0.42**	0.37*	0.15	0.39*	0.40*	0.63*
Critical thinking	0.38*	0.31	0.42**	0.36*	0.108	0.57*
Knowledge utilization	0.35*	0.29*	0.212	0.34*	0.32*	0.53**
Ethical considerations	0.54*	0.201*	0.41**	0.35	0.34*	0.54*
CDMNS total	0.33*	0.27*	0.38*	0.32*	0.31*	0.52*

*P < 0.05 and **P < 0.01 indicate levels of significance; SANICS: Self-Assessment Nursing Informatics Competencies Scale; CDMNS-PT: Clinical Decision-Making in Nursing Scale.

The multivariate regression model predicting CDM among nurses (R = 0.78, R² = 0.61, F = 35.21, P < 0.001) identified the following significant predictors: total SANICS score (B = 1.15, β = 0.63, P < 0.001), age (B = −0.32, β = −0.05, P = 0.006), professional experience (B = 0.81, β = 0.31, P = 0.002), frequency of informatics system usage (B = 2.45, β = 0.42, P = 0.04), training in NI (B = 3.11, β = 0.22, P < 0.001), and rating of informatics competency (B = 4.00, β = 0.51, P < 0.001) (Table 5).

Table 5.

Predictors of clinical decision-making among nurses (N = 249).

Predictor variables	B	β	T	P	Tolerance	VIF
(Constant)	12.525		13.53	<0.001
Total SANICS score	1.15	0.63	14.01	<0.001	0.62	1.61
Age	−0.32	−0.05	−1.13	0.006	0.88	1.14
Professional experience (years)	0.81	0.31	7.71	0.002	0.79	1.27
Frequency of informatics System usage	2.45	0.42	19.92	0.041	0.75	1.33
Training in nursing informatics (yes/no)	3.11	0.22	21.59	<0.001	0.81	1.23
Rating of informatics competency	4.00	0.51	18.87	<0.001	0.65	1.54

B: unstandardized coefficients; β: standardized coefficients; T: t-statistic; P: level of significance; VIF: variance inflation factor; N: sample size.

Discussion

This study aimed to describe the relationship between NI competencies and CDM among registered nurses in Jordan. The findings showed that the nurses demonstrated moderate to high levels of NI competencies. This indicates that while the nursing workforce in these hospitals possesses a reasonable level of proficiency in NI competencies, there is room for improvement. Among the subscales of NI competencies, basic computer skills and proficiency scored the highest (M = 3.27, SD = 0.83). However, the clinical informatics subscale scored a mean of 3.04 (SD = 0.80). The relatively high score of basic computer skills can be attributed to the widespread integration of basic digital tools in healthcare education and practice in the country. Many nursing programs include fundamental computer training as part of their curricula. Electronic systems for documentation and communication have become very common in healthcare institutions. On the other hand, the lower scores in the clinical informatics role subscale may reflect limited opportunities for nurses to engage in system design or evaluation processes, as these roles are often reserved for information technology professionals or senior administrators. Moreover, there may be a lack of advanced training programs specifically targeting these high-level skills, limiting nurses’ ability to develop expertise in this area. The results were consistent with the findings of a study conducted among Finish nurses²⁷ and the findings of a systematic review of 13 studies that revealed a mean average score of nurses’ informatics competencies of 3.4 and basic computer knowledge and skills of 3.9.²⁸ On the contrary, the results were higher than the level of mean informatics competencies scores among Jordanian nurses (M = 2.49, SD = 0.73).²⁹ This disparity is likely due to differences in the availability of structured NI education, the level of technological integration in healthcare systems, and institutional support for informatics roles.

The results showed a moderate level of CDM competencies among nurses (M = 3.30, SD = 0.94). Subscale-level analysis revealed moderate scores across the dimensions of knowledge utilization (M = 3.22, SD = 0.91), critical thinking (M = 3.37, SD = 0.96), and clinical judgment (M = 3.21, SD = 0.91). However, ethical considerations emerged as the highest-scoring subscale with a mean of 3.48 (SD = 0.99), on the upper edge of the moderate range, approaching high competency. These results likely reflect the educational background and the context of clinical training of nurses in Jordan, where critical thinking and CDM are integrated into nursing curricula but may not be emphasized to the same extent as in more developed healthcare systems. In Jordan, while nursing curricula do include elements of critical thinking and CDM, NI is not yet a standardized or required component of most undergraduate nursing programs. This contrasts with many developed countries—such as the USA, Canada, and parts of Europe where NI is formally integrated and often mandated within bachelor's programs, ensuring that students gain foundational competencies in data management, electronic health records, ethical informatics use, and technology-supported decision-making. However, moderate scores in knowledge utilization and clinical judgment could reflect some limitations in resources, such as access to comprehensive patient data or decision support tools, which are critical for enhancing these aspects of CDM. These tools enhance judgment by offering real-time suggestions or alerts that help prioritize risks and suggest evidence-based interventions.^30,31 Nevertheless, this score is higher than reported in a study conducted in the West Bank of Palestine, which reported a mean score of 2.59 in CDM among nurses.³² It was also higher than the level reported and revealed in another study (M = 102.3 ± 15.3) (n = 186).³³ However, the researchers enrolled operation room nurses only. This difference in CDM competencies between nurses from different countries can be attributed to the difference in education and healthcare systems.

The results showed that the total SANICS was moderate to strong correlated with the overall CDMN score (Pearson r = 0.52, P < 0.05). This means that high levels of NI competencies are strongly associated with high CDM competencies. This is congruent with the findings of previous studies.^9,28,32,34 These studies were conducted in different countries, including the Middle East and the Americas. They showed the global relevance of informatics training and its impact on CDM in diverse healthcare settings. However, the study of Hussein was a single hospital study and enrolled nursing managers only (N = 97).³⁵

The results of the regression analysis also revealed that other significant predictors of CDM competencies included the frequency of informatics system usage (B = 2.45, β = 0.42, P = 0.04), training in NI (B = 3.11, β = 0.22, P < 0.001), professional experience (B = 0.81, β = 0.31, P = 0.002), and age (B = −0.32, β = −0.05, P = 0.006). Specifically, these results underscore the importance of training in NI, which is consistent with the findings of a previous study.²⁷ Professional experience also emerged as a significant predictor of CDM competencies, emphasizing the role of accumulated knowledge and clinical exposure in shaping CDM capabilities. This was supported by a study conducted in Croatia.³⁶ It is expected experienced nurses are likely to draw on both tacit knowledge and familiarity with NI to navigate complex clinical scenarios effectively. Furthermore, the results of the present study showed a negative correlation between age with CDM competencies (β = −0.05). Although it was statistically significant, it is weak. This was supported in a previous study³⁷ and contradicted the finding of another study³⁸ that was conducted during the COVID-19 pandemic and enrolled nursing students only, which limited the comparability of the findings.

The study highlights that age significantly influences CDM competencies in nurses, with older individuals potentially facing challenges such as cognitive decline, decreased adaptability to new practices, and physical fatigue, which can hinder their decision-making abilities. Additionally, older nurses may struggle to adapt to rapidly evolving informatics technologies, further affecting their CDM performance. Despite the potential for experience to mitigate some of these issues, the interplay of age, informatics competency, professional experience, and training in NI together accounts for much of the variability in CDM, emphasizing the complex factors that shape nurses’ decision-making capabilities.

The revealed predictors of CDM could help identify potential strategies that help provide a roadmap for targeted education and training programs that can empower nurses to meet the demands of modern healthcare environments. Also, the findings reinforce the importance of investing in NI education and infrastructure to support nurses with the tools and skills required for high-quality CDM. Structured mentorship programs, wherein experienced informatics users support less proficient colleagues, could also bridge competency gaps and facilitate knowledge transfer within healthcare teams.

Healthcare administrators should prioritize implementing user-friendly informatics systems that meet the practical needs of nursing staff. For nursing educators, this study underscores the importance of demonstrating how NI competencies directly impact CDM, highlighting the need for ongoing education tailored to practicing nurses. Such programs are essential for closing gaps in advanced informatics roles, including interpreting complex clinical data, managing decision support systems, and effectively using informatics tools to enhance patient care. Additionally, strong partnerships between academic institutions and healthcare organizations can help ensure that training remains current, practical, and aligned with the realities of clinical practice.

Policymakers should consider strategies to address barriers faced by older nurses in adopting new technologies by designing age-inclusive training programs that accommodate diverse learning needs and styles. Research initiatives are recommended to evaluate the long-term impact of NI systems on CDM. Moreover, future research should use objective measures of NI competencies and broader sampling across diverse healthcare settings.

One of the notable strengths of this study lies in its use of multivariate analyses, including regression techniques, which provided robust insights into the predictive relationships between variables. This analytical approach allowed for a more nuanced understanding of how various dimensions of NI competencies influence CDM, shedding light on the complex interplay of contributing factors. While the regression model implies a directional relationship—namely, that NI competencies predict CDM—this choice was theoretically grounded in existing literature suggesting that informatics proficiency enhances decision-making capabilities. Nevertheless, the study's cross-sectional, descriptive-correlational design inherently limits causal inferences, meaning that reverse causality (i.e. CDM influencing NI competencies) cannot be ruled out. Furthermore, reliance on self-reported data introduces potential response bias, as participants may over- or underestimate their competencies. Finally, the study's focus on a single country limits the generalizability of findings to broader international contexts.

Conclusion

This study described the relationship between NI competencies and CDM among registered nurses in Jordan. The results showed moderate to high levels of self-reported NI competencies, particularly in basic computer skills and wireless device usage; nurses demonstrated a solid foundation for leveraging informatics tools in their practice. In addition, there is a significant correlation between NI competencies and CDM, coupled with the predictive strength of variables such as professional experience, frequency of informatics system usage, and informatics training, emphasizing the need for targeted interventions to optimize these skills.

This study highlights the importance of integrating NI education into nursing curricula, fostering continuous professional development, and implementing supportive policies to enhance competency levels, which could empower nurses to make informed evidence-based clinical decisions.

Footnotes

Acknowledgment

The authors extend their appreciation to Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2025R844), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

ORCID iD

Khalid Al-Mugheed

Author contributions

Conceptualization: IAO and SAO; methodology: SAO, IAO, and GAS; validation: IAO, AD, and MMA-Q; formal analysis: IAO and SAO; investigation: SAO, MMA-Q, and GAS; data curation: MMA-Q, GAS, and AD; writing—original draft preparation: SMFA, SAO, MMA-Q, and IAO; writing—review and editing: AD and MMA-Q; visualization: SAO, IAO, and GAS; supervision: KA-M, IAO, and SAO; project administration: SAP and IAO. IAO is responsible for the overall content as guarantor. All authors have read and agreed to the published version of the manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2025R844), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

All data that were analyzed during this study are included in this article, and further inquiries can be directed to the corresponding author.

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