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Abstract

Introduction

Artificial intelligence (AI) is transforming healthcare, with benefits such as personalized treatment and efficient diagnosis; however, it also comes with challenges such as data protection and ethical concerns.

Aim

This study examined nurse leaders’ perceptions of AI's impact on their professional roles and their level of preparedness for AI integration.

Methods

This descriptive correlational study was conducted at various hospitals in Hail City, Saudi Arabia. The Shinners Artificial Intelligence Perception tool was used to evaluate 155 nurse leaders’ perceptions of AI and their readiness for its adoption.

Results

The SHAIP tool, previously validated in international studies, was further adapted and validated for this context using confirmatory factor analysis and reliability testing, ensuring robust measurement of nurse leaders’ perceptions and preparedness regarding AI. Nurse leaders showed a positive perception of AI's impact on their roles (mean score = 3.72), but lower perceived preparedness (mean score = 3.32). Education level (where holders of a Bachelor of Science in Nursing showed lower perceptions, with a coefficient of −0.66, p = .006) and department significantly influenced their perceptions of AI. No significant predictors of preparedness for AI were identified. Further, there was no correlation between the perceived AI impact and preparedness.

Conclusion

Although nurse leaders recognize AI's potential in healthcare, there is a gap between their perceptions and preparedness. Tailored education and training programs are required to enhance the integration of AI into nursing leadership and practice.

Keywords

Artificial intelligence nursing leadership healthcare AI integration AI preparedness nursing informatics

Introduction

The introduction of artificial intelligence (AI) is changing the approaches to nursing leadership.^1,2 AI technologies can enhance patient outcomes by improving administrative processes and diagnostic accuracy and developing tailored treatment strategies based on detailed medical data analysis.^3,4

This supports the growing body of evidence suggesting that effective nurse leadership improves patient outcomes through efficient communication and individualized care,^5,6 and the incorporation of AI with electronic medical records (EMRs) facilitating better utilization of patient information.^3,5 This is further supported by evidence indicating that effective nurse leadership coupled with improved patient information utilization through AI-enhanced EMRs improves patient outcomes.^3,5,6

However, the use of AI in healthcare poses several challenges that need to be addressed. These include ethical challenges, such as data privacy, potential job displacement, and algorithmic bias; technical challenges, such as data gathering from multiple sources; and implementation challenges, such as unequal resource distribution and the necessity of sufficient training. Effective nurse leadership is crucial to navigate these complexities and ensure successful AI integration. Evidence suggests that such leadership not only advances nursing and clinical practices but also fosters a smoother adoption of new technologies like AI.^5,7 Nurses have always been at the front line while implementing initiatives and changes; thus, they are strategically positioned to provide effective leadership in overcoming these issues.⁸

Hail City, Saudi Arabia, is known for its exceptionally emerging healthcare system and wide range of patients. Due to this rapid growth, there is considerable expectation that nurse leaders will be able to help with the ethical and practical concerns of AI that arise from serving a multicultural population. To meet these challenges and fully utilize the advantages of AI in this setting, nursing education needs to broaden its scope and include additional courses on nursing informatics and AI, so that nurse leaders can acquire basic expertise in dealing with modern technologies.^7,9 This approach is essential not only for improving their decisions but also for enabling them to develop an organizational culture that supports AI adoption. Effective nurse leadership has been shown to contribute to advanced nursing and clinician practices and foster the smooth incorporation of AI technologies into their work.^5,7

Different approaches taken by nursing leaders deeply affect the work environment and satisfaction of nursing personnel, especially during the integration of AI into healthcare.^10,11 The transformational style of leadership enhances nurses’ wellness and job satisfaction, thus facilitating the integration of AI into nursing functions.^10,12 Health system practitioners face specific challenges such as unequal resources, lack of training, and possible minimal computer literacy and infrastructure for sophisticated AI integration. Specific plans for the integration of AI are needed to fully exploit these technologies.¹³

Studies on the effects of AI on nursing management roles in Hail City are limited, highlighting the need for focused integration approaches. Specifically, there is a lack of research on AI adoption in nursing leadership within Saudi Arabia's unique healthcare environment, an insufficient understanding of the cultural and ethical considerations influencing AI acceptance among Saudi Arabian nurses, and the need for studies that address the specific training and educational requirements of Saudi nurse leaders to effectively integrate AI. Therefore, this research attempted to fill this gap by exploring the impact of AI on nurse managers, their willingness to adapt AI technologies, and the possible advantages and disadvantages of AI in healthcare.

This research analyzed the accuracy and consistency of the Shinners Artificial Intelligence Perception (SHAIP) instrument in measuring nurses’ AI perceptions and investigated the correlation between nurses’ perceptions of AI and their readiness for AI integration. In addition, the study investigated how age, sex, educational qualifications, and work unit influence AI perception and readiness among nurse leaders. Thus, this study supports the evolution of nursing education and training, while offering guidance on the formulation of AI integration plans in healthcare institutions and an advanced understanding of AI utilization in nursing management, specifically for the enhancement of patient care and health services by nurse leaders.

This study builds on previous work using the SHAIP tool¹⁴ by providing novel insights into AI perceptions and readiness within a specific and understudied population. While prior research has established the validity of the SHAIP tool in various contexts,^9,14 this study makes several key contributions. Unlike earlier studies, this research focused on nurse leaders in Hail City, Saudi Arabia, addressing a significant gap in understanding the cultural, ethical, and educational nuances that influence AI acceptance and integration within the Saudi Arabian healthcare context.

This study also contributes to the ongoing validation and refinement of the SHAIP tool through a rigorous confirmatory factor analysis. This process led to context-specific adaptation of the tool, enhancing its construct validity and reliability for the Saudi Arabian nurse leader population. This adaptation, which involved the removal of items to improve factor loadings and internal consistency, offers valuable methodological insights for future SHAIP tool applications. Furthermore, this research uniquely investigated the influence of education level and department on nurse leaders’ perceptions of the impact and preparedness for AI. Identifying these specific predictors contributes to understanding the factors shaping AI acceptance and readiness within nursing leadership. By focusing on these distinct areas, this study aimed to provide a more nuanced understanding of AI's role in nursing leadership and inform the development of targeted strategies for successful AI integration into Saudi Arabian healthcare.

Incorporating AI into healthcare systems offers nursing executives both advantages and obstacles. With the continued advancement of AI, it is vital for nursing leaders to be educated on the intricacies of AI technologies, which requires evolution in the nursing education approach. The integration of innovation and collaboration facilitates nursing leaders’ ability to manage transitions in the healthcare delivery system, thereby enhancing the quality of care and overall patient outcomes.

Methodology

Design

This study utilized a correlational descriptive design to assess how AI affects hospital nurse managers and their degree of preparedness of in using AI.

Setting

The study was conducted at various hospitals in Hail City, Saudi Arabia, including government facilities such as King Salman Specialist Hospital and King Khalid Hospital, and private establishments such as Sharaf Hospital, Saudi German Hospital, and Habeed Medical Centre. These hospitals represent a diverse range of healthcare settings, including general hospitals, maternity and children's hospitals, and nursing homes to ensure that the study captured a comprehensive view of AI's impact on Hail City's healthcare system. Hail City, located in the northwestern region of Saudi Arabia, is a developed primary urban center that caters to patients of all types, owing to its diverse healthcare system. This specific region was selected because of the evolving healthcare system and heterogeneous patient demographics of Hail City, along with the researchers’ conviction that the pace of development had surpassed the expectations of nurse leaders to manage AI issues practically and ethically.

Participants and sampling

To ensure that every department in the hospital was represented, the researchers used a stratified sampling method. The study population included 438 nurse leaders, composed of nurse executives and managers. Using the Raosoft software, a sample size of 205 participants was determined to ensure adequate statistical power. This was based on a 95% confidence level with a 5% margin of error. These parameters are commonly used in health-related research because they strike a suitable balance between accuracy and practicality. Having 205 participants significantly contributed toward the detection of meaningful differences and relationships in the study population. The sample was proportionately allocated to every nurse leader across each department, and participants were selected through simple random sampling.

Of the 205 nurse leaders, 155 chose to participate, resulting in a response rate of 75.6%. This relatively low response rate may be attributed to factors, such as time constraints and concerns regarding anonymity. This may have implications for the generalizability of the findings as respondents may not fully represent the entire population of nurse leaders in Hail City.

The criteria for inclusion in this study were registered nurse executives and managers and current nurse leaders holding positions for at least two years at the time of the study. The exclusion criteria were nurses in executive or management roles with less than two years of experience and those who declined to participate in the study.

Questionnaire

This study used the SHAIP tool¹⁴ to investigate the impact of AI on the role and preparedness of nurse leaders.

Recognizing the importance of cultural and institutional contexts, the SHAIP tool was adapted to the specific setting of Hail City, Saudi Arabia. This adaptation process involved rigorous confirmatory factor analysis, which led to the removal of items that were ambiguous or less relevant to the local context. For instance, Item 7, which pertained to AI potentially taking over roles, was removed because it did not strongly resonate with the perceptions of nurse leaders in this region. This adaptation ensured the construct validity and reliability of the tool for the Saudi Arabian nurse leader population.

This instrument has been validated in previous studies and demonstrated acceptable reliability, with a Cronbach’s alpha of 0.804 for Factor 1 and 0.620 for Factor 2.

The first part of the questionnaire collected demographic information from respondents, including age, years of hospital experience, sex, education level, job title, and department. It also included six AI-related questions, such as understanding AI, use of AI, training related to AI, desire for further education, and specific AI topics of interest.

The second part of the SHAIP consisted of ten items. Items 1, 2, 3, 4, 5, and 7 pertained to Factor 1 (the impact of AI on nurse leaders’ professional roles), while Items 6, 8, 9, and 10 pertained to Factor 2 (nurse leaders’ preparedness for AI). The respondents were asked to complete the questionnaire by rating their responses on a 5-point Likert scale ranging from 1 (strongly agree) to 5 (strongly disagree).

Data collection

Data for this study were collected from June to November 2024 after all necessary approvals were secured and collaborations with the hospital admin were made. Nurse leaders who fulfilled the inclusion criteria received the questionnaire via email using a Qualtrics link. To boost participation, reminder emails were sent to non-respondents every two weeks for the four months of the data collection period. Nurse executives were also prompted to remind their teams about their voluntary participation. Along with the initial email invitation, detailed information of the study and the researcher's contact information were also disseminated. This facilitated broad outreach for respondents while maintaining a systematic data collection approach within the stipulated four months period.

Data analysis

Data collected via Qualtrics were analyzed using descriptive and inferential statistics (SPSS v28). Descriptive statistics (means, standard deviations, and frequencies) were used to summarize respondent demographics and AI-related responses.

A confirmatory factor analysis was performed to assess the validity of the SHAIP tool. The initial model showed a poor fit [χ²(34) = 55.6, p = .01]. Modifications, based on modification indices and factor loadings (removing low/cross-loading items), improved the fit [χ²(19) = 28.2, p = .08]. Details of these modifications are provided in the Results section.

Refinement of the SHAIP tool post-validation

After initial data collection, the SHAIP tool underwent confirmatory factor analysis to evaluate its construct validity among nurse leaders in Hail City. The initial confirmatory factor analysis model demonstrated a poor fit [χ²(34) = 55.6, p = .01], indicating that the original 10-item structure did not optimally capture the intended latent constructs-namely, “Perceived Impact of AI” (Factor 1) and “AI Preparedness” (Factor 2).

Modification indices and factor loadings were examined to identify items with low loadings on their intended factor or cross-loadings on multiple factors. This process revealed that Items 7 and 10 were problematic: Item 7 (assigned to Factor 1) showed a low factor loading and did not contribute significantly to internal consistency. Its content appeared ambiguous or less relevant to the specific experiences of nurse leaders in the Saudi context, reducing its utility in measuring perceived AI impact. Removing Item 7 improved the one-dimensionality and reliability of the “Impact” factor. Item 10 (assigned to Factor 2) also demonstrated a low factor loading and potential cross-loading, suggesting it failed to uniquely capture AI preparedness. It may have addressed overly broad or non-actionable aspects of preparedness, limiting its discriminative power. Dropping Item 10 enhanced the internal consistency of the tool and sharpened the focus of the preparedness subscale on the practical and measurable aspects of AI readiness.

Sharp focus on action-enabled dimensions such as capability to train and implement was enhanced alongside construct validity due to removing items and thereby improving factor coherence and strengthening underlying theory [χ²(19) = 28.2, p = .08]. Construct reliability improved for both factors as culturally and contextually relevant applicability of the findings enhanced construct validity of the SHAIP tool for Saudi Arabia (Table 1).

A multiple linear regression was used to explore the predictors of Factors 1 and 2. The predictors included age, experience, sex, education level, job title, department, AI understanding/use/training, desire for further education, and AI topics of interest. The regression assumptions (linearity, normality, homoscedasticity, and independence) were met using graphical and statistical methods. Multicollinearity and outliers were also assessed.

Composite reliability analysis was used to assess the internal consistency of the SHAIP tool for Factors 1 and 2.

Ethical considerations

This study was approved by the institutional review board (No: 22023-28). The respondents were informed about the purpose and aim of the study, the data collection procedure, and the risks associated with it. Informed consent was obtained from all participants, which explicitly stated that participation was voluntary and participants could withdraw at any time.

All personal identifiers were removed to ensure the confidentiality and anonymity of the respondents, and data distribution was secured using Qualtrics.

The primary data were stored on a hard disk that was password-protected and available only to the researchers. For a period of 5 years after the study has been completed, the data will be kept secure, after which it will be deleted. These measures were taken to ensure that the participants’ rights were respected while gathering pertinent information regarding the utilization and preparedness of AI among nursing leaders.

Results

This section details the findings regarding nurse leaders’ demographic information and their perceptions of AI in Hail City. Table 2 outlines the demographic characteristics of the 155 nurse leaders. Most respondents (81.94%) easily comprehended concepts regarding AI; however, only a small proportion (58.71%) stated that they utilized AI in their main work activities. This shows a possible discrepancy between AI understanding and use in professions based on self-assessment.

Table 1.

Summary table: effects of dropping Items 7 and 10.

Aspect	Before Refinement	After Dropping Items 7 and 10
Model fit (CFA)	Poor (p = .01)	Improved (p = .08)
Factor structure	Some items with low/cross-loadings	All retained items had strong loadings
Internal consistency	Low Cronbach's alpha	High Cronbach's alpha
Construct clarity	Some conceptual overlap	Clear, distinct constructs
Contextual relevance	Generic/ambiguous items	Locally relevant, actionable items

Table 2.

Demographic characteristics of the respondents (N = 155).

Variable	Mean	SD	N	%
Age (years)	33	6.4
Years of hospital experience	10	6.22
Gender
Male			101	65.16
Female			54	34.84
Education level
Bachelor's in nursing			102	65.81
Master's in nursing			53	34.19
Job title
Nurse manager			115	74.19
Nurse executive			40	25.81
Department
Ear, nose, throat (ENT)			13	8.39
Surgical			38	30.96
Critical care unit (CCU)			23	14.84
Medical			71	45.81
Understanding of AI
Yes			127	81.94
No			28	18.06
Use of AI
Yes			91	58.71
No			64	41.29
Received training related to AI
Yes			104	67.10
No			51	32.90
Desire for AI education
Yes			137	88.39
No			18	11.61
Topic interested in
Training on AI techniques			133	85.81
The ethics of AI in healthcare			22	14.19

The results further indicated that the largest proportion of the respondents were male (65.16%), young (M_age = 33 years), and holders of a Bachelor of Science in Nursing (BSN) degree (65.81%). The majority worked as nurse managers (74.19%), and most were from medical units (45.81%). Although many participants showed a high level of understanding of AI concepts, there was a striking difference between their understanding and implementation. For instance, although 81.94% of the respondents claimed to understand the concept of AI, only 58.71% claimed to have implemented AI in their primary work. Therefore, although many nurse leaders seem to have a baseline understanding of AI, it is not sufficient for practical implementation. Almost all the study participants (88.39%) were motivated to undergo additional self-AI training, particularly technique training (85.81%). This demonstrates the emerging need for professional AI self-management competencies among nurse leaders in Hail City.

Table 3 presents the initial and revised model fit indices. The initial model did not meet the criteria of a good fit based on the results of a chi-square test that showed a value of 55.6, which was significant at p value of .01 [χ²(34) = 55.6, p = .01]. The fit indices indicated that the revised model improved compared to the previous model. The researchers accept the null hypothesis of perfect fit as the revised model's non-significant chi-square value [χ²(19) = 28.2, p = .08] demonstrates that the fit to the data is satisfactory.

Table 3.

Comparison of initial and revised model fit indices for the SHAIP tool.

	CCSSUPSTART2 CSSUPEND(DF)	CCSSUPSTART2 CSSUPEND/DF	P	TLI	CFI	RMSEA
INITIAL MODEL	55.6 (34)*	1.6	0.01	0.94	0.95	0.06
REVISED MODEL	28.2 (19)	1.5	0.08	0.97	0.98	0.05
CRITERIA	P > .05			>.95	>.95	<.05

*p < .05.

The TLI values also improved, from the previous value of 0.94 to 0.97, surpassing the threshold of 0.95. The CFI value also did not drop below the threshold, registering 0.98 after improving from 0.95. In addition, the RMSEA value improved from 0.06 to 0.05, which is considered acceptable.

Considering the rest of the factor loadings, Items 7 and 10 were not loaded on any of the factors. The composite reliability for Factor 1 was reasonable (0.804), whereas Factor 2 (0.620) was slightly above the generally accepted limit.

However, it is important to briefly explain why the item “AI may take over part of my role” was statistically weak, despite its relevance in broader discussions about workforce concerns and anxieties surrounding AI implementation. In this study, the item “I believe that one day AI may take over part of my role as a healthcare professional” had a low factor loading and did not contribute significantly to the internal consistency of the “Impact” factor. This suggests that within this specific group of nurse leaders in Hail City, their perceptions of AI's impact on their roles were not strongly influenced by concerns about job displacement. However, it is crucial to acknowledge that this finding may be context-specific. In other settings or with different professional groups, this concern could be a much stronger factor shaping perceptions of AI.

The revised model removed Item 7 “I believe that one day AI may take over part of my role as a healthcare professional” and Item 10 “I believe that AI technology should make an error, full responsibility lies with the healthcare professional” allowing Factor 2 to focus on the perception of preparedness for AI among healthcare professionals. The adapted two-factor, eight-item model had a better overall fit to the metrics than the previous model.

Evaluative factor analysis of the SHAIP questionnaire revealed two distinct dimensions among nurse leaders (Table 4): their perception of AI's impact on professional roles and their perceived preparedness for AI integration. The components listed in the SHAIP questionnaire were rated on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree).

Table 4.

Factor analysis of nurse leaders’ perceptions of AI impact and preparedness (SHAIP tool).

SHAIP ITEMS	FACTOR 1: PERCEPTIONS OF AI IMPACT ON PROFESSIONAL ROLES	FACTOR 2: PERCEPTIONS OF PREPAREDNESS FOR AI
ITEM 1. I BELIEVE THAT THE USE OF AI IN MY SPECIALTY COULD IMPROVE THE DELIVERY OF PATIENT CARE	.904
ITEM 2. I BELIEVE THAT THE USE OF AI IN MY SPECIALTY COULD IMPROVE CLINICAL DECISION-MAKING	.887
ITEM 3. I BELIEVE THAT AI CAN IMPROVE POPULATION HEALTH OUTCOMES	.876
ITEM 4. I BELIEVE THAT AI WILL CHANGE MY ROLE AS A HEALTHCARE PROFESSIONAL IN THE FUTURE	.638
ITEM 5. I BELIEVE THAT THE INTRODUCTION OF AI WILL REDUCE FINANCIAL COSTS ASSOCIATED WITH MY ROLE	.661
ITEM 7. I BELIEVE THAT ONE DAY AI MAY TAKE OVER PART OF MY ROLE AS A HEALTHCARE PROFESSIONAL	.514
ITEM 6. I BELIEVE THAT HEALTHCARE PROFESSIONALS IN GENERAL ARE PREPARED FOR THE INTRODUCTION OF AI		.589
ITEM 8. I BELIEVE THAT I HAVE BEEN ADEQUATELY TRAINED TO USE AI SPECIFIC TO MY ROLE		.722
ITEM 9. I BELIEVE THERE IS AN ETHICAL FRAMEWORK IN PLACE FOR THE USE OF AI IN MY WORKPLACE		.941
ITEM 10. I BELIEVE THAT SHOULD AI MAKE AN ERROR; FULL RESPONSIBILITY LIES WITH THE HEALTHCARE PROFESSIONAL		.714
TOTAL VARIANCE %	51.372	12.073
MEAN OF MEAN SCALE SCORE (SD)	3.72 (.836)	3.32 (.846)

The first factor, accounting for 51.37% of the variance, reflected beliefs about how AI affects their roles, including their potential to improve patient care, assist with clinical decisions, promote positive outcomes, manage costs, and impact professional role expectations. High factor loadings (0.638–0.904) for these items indicate a strong association with this factor. Figure 1 presents the path diagram of the confirmatory factor analysis CFA model for Factor 1 (“Perceived Impact of AI”), illustrating the standardized factor loadings for each item retained in the revised model.

Figure 1.

Path diagram of the confirmatory factor analysis model for factor 1 (‘perceived impact of AI’) of the SHAIP tool.

Notably, the nurse leaders expressed a more positive view of AI's impact on their roles (mean score = 3.72) than their current level of preparedness for AI (mean score = 3.32). Furthermore, items related to AI's impact on professional roles demonstrated a stronger correlation than those related to preparedness. This suggests that nurse leaders may have a more unified understanding of how AI affects their roles than their diverse views on preparedness.

The second factor, explaining 12.07% of the variance, focused on perceived preparedness for AI and encompassed items such as overall preparedness, adequacy of training, ethical guidelines for AI use, and accountability for errors. These items also exhibited high factor loadings (0.589–0.941) as shown in Table 4. Figure 2 illustrates the path diagram of the confirmatory factor analysis model for Factor 2 (“Perceived Preparedness for AI”), visually depicting the standardized factor loadings and the relationships between each item and the underlying construct of preparedness. This figure complements Table 4 by providing a graphical representation of how each item contributes to the preparedness factor, highlighting the relative strengths of the associations and supporting the construct validity of this dimension.

Figure 2.

Path diagram of the confirmatory factor analysis model for factor 2 (‘perceived preparedness for AI’) of the SHAIP tool, showing standardized factor loadings among nurse leaders.

A multiple linear regression analysis was performed to assess what influences nurse leaders’ perceived impact of AI on their professional roles (Factor 1) (Table 5). The model was non-significant [F(15, 140) = 1.64, p = .071], meaning that no combination of predictors explained a great deal of the variance, but some individual predictors were significant. For example, the education level of respondents was significantly correlated with Factor 1 scores because nurses with a BSN (B = ‒0.66, p = .006) had lower perceptions of AI's impact compared to those with an MSN (B = ‒0.49, p = .062). Additionally, employees’ performance in the medical department was linked to a lower Factor 1 score (B = ‒0.54, p = .035). Hence, education level and the department they worked in were the only two individual factors that delineated the perceived impact of AI on professional roles, and were found to be significant.

Table 5.

Predicting nurse leaders’ perceived impact of AI on professional roles (factor 1).

Variable	B	SE	95% CI	β	t	p
(Intercept)	4.21	0.50	[3.22, 5.19]	0.00	8.44	<.001
Age	0.003	0.01	[−0.02, 0.03]	0.02	0.22	.829
Year of experience	−0.02	0.01	[−0.05, 0.007]	−0.15	−1.44	.151
Gender	0.18	0.15	[−0.12, 0.48]	0.10	1.21	.228
Nurse executive	0.38	0.22	[−0.05, 0.82]	0.14	1.76	.081
Education level
BSN	−0.66	0.24	[−1.13, −0.19]	−0.37	−2.80	.006
MSN	−0.49	0.26	[−1.00, 0.02]	−0.24	−1.88	.062
Hospital department
Surgical	0.12	0.28	[−0.43, 0.66]	0.04	0.42	.677
Medical	−0.54	0.26	[−1.05, −0.04]	−0.21	−2.13	.035
CCU	−0.36	0.23	[−0.82, 0.10]	−0.15	−1.54	.126
ENT	−0.17	0.18	[−0.54, 0.19]	−0.10	−0.93	.354
Training received (no)	0.05	0.16	[−0.26, 0.36]	0.03	0.31	.754
Topic interested in (ethics of AI)	0.12	0.20	[−0.27, 0.52]	0.05	0.62	.537
Understanding of AI	−0.18	0.18	[−0.54, 0.18]	−0.08	−1.00	.320
Use of AI (no)	−0.19	0.15	[−0.49, 0.10]	−0.11	−1.30	.197
Desire for AI education (yes)	−0.08	0.22	[−0.51, 0.36]	−0.03	−0.35	.730

Note. Results: F(15, 140) = 1.64, p = .071, R² = .15.

B: unstandardized regression coefficient; SE: standard error of the coefficient; β: standardized regression coefficient; t: t-statistic.

Table 6 presents the results of the predictive analysis of nurse leaders’ perceived levels of preparedness for AI. The overall model was not statistically significant [F(15, 140) = 1.64, p = .071], indicating that the selected factors (age, experience, gender, etc.) individually or collectively did not explain any variation in nurse leaders’ perceived levels of preparedness for AI.

Table 6.

Predicting nurse leaders’ perceived level of preparedness for AI (factor 2).

Variable	B	SE	95% CI	β	t	p
(Intercept)	4.14	0.51	[3.14, 5.14]	0.00	8.20	<.001
Age	−0.003	0.01	[−0.03, 0.02]	−0.03	−0.29	.771
Year of experience	−0.02	0.01	[−0.05, 0.006]	−0.16	−1.54	.126
Gender	0.12	0.15	[−0.18, 0.42]	0.07	0.80	.427
Nurse executive	0.04	0.22	[−0.39, 0.48]	0.02	0.20	.841
Education level
BSN	−0.43	0.24	[−0.90, 0.04]	−0.24	−1.80	.075
MSN	−0.39	0.26	[−0.91, 0.13]	−0.20	−1.49	.138
Hospital department
Surgical	0.15	0.28	[−0.40, 0.71]	0.05	0.54	.591
Medical	−0.03	0.26	[−0.54, 0.48]	−0.01	−0.13	.900
CCU	0.13	0.24	[−0.34, 0.60]	0.06	0.55	.585
ENT	0.02	0.19	[−0.35, 0.39]	0.01	0.12	.908
Trained received (no)	−0.26	0.16	[−0.57, 0.05]	−0.15	−1.64	.104
Topic interested in (ethics of AI)	0.14	0.20	[−0.26, 0.54]	0.06	0.70	.488
Understanding of AI (no)	−0.19	0.18	[−0.55, 0.18]	−0.09	−1.02	.311
Use of AI (no)	−0.12	0.15	[−0.42, 0.18]	−0.07	−0.78	.438
Desire for AI education	−0.06	0.22	[−0.50, 0.38]	−0.02	−0.26	.796

Note. Results: F(15, 140) = 0.95, p = .515, R² = .09.

Table 7 demonstrates the relationship between AI's perceived impact on professional roles and perceived overall readiness for AI among nursing leaders. The mean score for AI's impact on role preparedness 3.41 on the descriptive scale, while the score for professional roles was 3.68. Pearson's correlation coefficient index was also null (r = .08, p = .31), indicating that nurse leaders’ perceptions of AI's impact and their preparedness to use AI were not linearly related.

Table 7.

Correlation between perceived preparedness for AI and perceived impact of AI on professional roles among nurse leaders.

Variable	M	SD	n	SE_M	Min	Max	Skewness	Kurtosis
Preparedness	3.41	0.84	156	0.07	1.00	5.00	−0.74	0.85
Professional roles	3.68	0.82	156	0.07	1.00	5.00	−1.35	2.29

Discussion

This pioneering study assessed nurse leaders’ perceptions of and readiness to use AI in Hail City, Saudi Arabia, providing a unique understanding of AI use in this particular healthcare setting.

Improving AI perception model accuracy in healthcare

It is essential to conduct model refinement processes and omit low-loading factors to optimize models that analyze health professionals’ perceptions of AI. This cycle improves the model fit, reliability, and validity while increasing the understanding of professionals’ views and attitudes, which contributes to the literature and lays the foundation for future studies^14,15 Although the modified model is valid within the construct of the ‘ impact of AI on professional roles’, the factor ‘preparedness for AI’ requires more refinement to depict multi-dimensionality appropriately. This is critical when dealing with perception and readiness because a positive perception is assumed to be linked to better healthcare outcomes. The interpretability and understanding of AI and its impacts are paramount.^15,16

Iterative model refinement must be conducted to integrate healthcare practitioners’ understanding and preparedness for adopting AI technology. Future research should analyze the impact of training programs and the gap between AI perceptions and applications.

Nurse leaders’ perceptions of AI impact and preparedness

The AI integration readiness factor showed the greatest variance among the data. This readiness included nurse leaders’ belief in their ability to use AI technologies, understanding the benefits that AI can bring, and their willingness to accept AI in their work. The high factor loadings on the ‘Perceived Impact of AI’ factor indicate that nurse leaders appreciate AI's potential in healthcare, while the positive loadings on the ‘AI Integration Readiness’ factor suggest they feel somewhat prepared for adoption. This overall positive sentiment aligns with that of Kotp et al.¹⁷ who reported high AI readiness among nurse leaders.

While preparedness to adopt AI is crucial, the effective integration of AI technology goes beyond leaders’ awareness; it also depends on nurses’ attitudes toward the technology. As Kirfan et al.¹⁸ and Higgins et al.¹⁹ pointed out, transformational leadership alone is insufficient to guarantee that nurses will operate with the aid of AI, indicating that more than simple leadership qualities are essential to bring forth the acceptance of AI. This underscores the somewhat ambiguous nature of AI implementation, which is not only about acceptance by users, but also anticipation by leaders.

The factor “Perceived Impact of AI” on professional roles examined the perspectives of nurse leaders regarding the impact of AI technology on their responsibilities. This factor encompasses items on AI's ability to facilitate decision-making, enhance patient care, and replace specific nursing functions. This suggests that nurse leaders have considered how AI technology will change their roles, which necessitates open communication and direction regarding the use of AI in nursing practice. The results of our study, which identified nurse leaders contemplating AI's influence of AI on their roles, are corroborated by Gonzalez-Garcia et al.,¹ who also found that AI adoption in nursing administration augmented operational efficiency, decision-making, and leadership skills. Evidence from Laukka et al.²⁰ supported this finding. These researchers observed the perception of nurse leaders toward AI to be predominantly positive, indicating that they have a sense of self-efficacy concerning AI and its application in the clinical environment.

Therefore, to maximize the benefits of AI in nursing, healthcare organizations should prioritize comprehensive strategies that address leader enablement and user engagement. These include developing clear guidelines for AI integration, addressing potential role changes, and providing robust training and support to foster user acceptance among frontline nurses.

Theoretical interpretation of AI perceptions and preparedness

Technology adoption theories such as the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) provide useful frameworks for interpreting these findings. Nurse leaders’ positive perceptions of AI's potential align with the perceived usefulness proposed by the TAM's, while their desire for more training highlights concerns related to perceived ease of use. UTAUT's concepts of social influence (e.g., organizational culture) and facilitating conditions (e.g., training availability) are also relevant. Contrary to typical TAM/UTAUT predictions, the gap between perceived impact and preparedness suggests that organizational barriers may hinder AI adoption. Future research should further explore these factors.

Predictors of AI impact on professional roles

An examination of the impact of AI on the professional roles of nurse leaders in Hail City suggests that, although the model was non-significant, nurses’ educational level and the department they worked in emerged as significant individual predictors, with education level showing a particularly strong influence.

The most remarkable finding was the strong link between a person's educational background and how they viewed AI's impact on their work. Nurse leaders with a BSN degree reported having a lower perception of the influence of AI compared to those with an MSN degree. This indicates that the amount of education a nurse has may affect how they perceive AI's influence on their roles. This further supports the work of Vaidya et al.²¹ who pointed out that education level is a determining factor in the acceptance of AI because more educated people express higher levels of confidence in AI systems. This increased trust and understanding of AI, often fostered by advanced education such as an MSN degree, can result in deeper recognition of how greatly AI may impact the professional sphere, which includes a greater understanding of its capability to redefine the functions of professionals.

To address the challenges related to AI integration, it is essential to develop targeted training programs for nurse leaders. These programs should not only cover the technical aspects of AI, such as operations and data interpretation, but also address ethical challenges, including data privacy, algorithmic bias, and potential job displacement. Such comprehensive training will empower nurse leaders to utilize AI effectively while navigating its complexities and potential pitfalls.

Moreover, addressing the ethical challenges of AI is crucial for building trust and promoting responsible adoption of AI in healthcare. Similarly, Medina et al.²² argue that instruction through advanced training and digital skills is critical for the integration of AI technology, which also suggests that the level of education modifies the perception of what AI can do and the value it holds. Nurse respondents appeared to be less cognizant of AI's professional role in their departments. This may be because AI's current applications are rather simplistic, focusing on task automation (such as imaging analysis) that does not cover fundamental nurse functions, such as patient education or care coordination. Ahmed et al.²³ emphasize the intricacy of workflows in medical units as an obstacle to grasping the function of AI. Likewise, the lack of adequate AI knowledge, especially about integrating AI into predefined workflows, as noted in Table 2, is bound to diminish nurses’ perceptions of what is arguably the most important redesign chance – the integration of AI technology into nursing education and practice.

The lower perception of AI's impact on professional roles in medical departments may stem from the narrow focus of current AI applications on diagnostic tasks (e.g., imaging analysis), which do not yet address nurses’ core responsibilities such as patient education or care coordination. This aligns with the findings of Ahmed et al.,²³ who identify workflow complexity in medical units as a barrier to perceiving AI's utility. Additionally, insufficient AI training, particularly concerning workflow integration, as mentioned in Table 2, likely limits nurses’ ability to envision AI's broader role, a gap exacerbated by educational disparities (BSN holders perceived less of an impact than MSN holders).

Based on these results, healthcare institutions should design and implement specific training programs for AI integration at various educational levels and departmental specialties. Such training should incorporate the general principles of AI and allow nurses to evaluate and critique the recommendations made by AI systems. Training should also include practical examples of the application of AI systems, such as preventing medication errors and monitoring patients using AI systems for various existing departments.

In addition, ethical issues must be addressed. This would enable healthcare organizations to enhance the power of nurse leaders with tools to use AI for better patient care and not fear addressing the issues of AI integration into patient care. It is noteworthy that the researchers discovered the level of education and department to be relevant predictors of the perception of the impact of AI, which has not been mentioned before within the scope of nursing leadership. This scholarly contribution highlights the need for specific implementation strategies for AI integration relative to the educational background and departmental context.

The finding that education level and department significantly predicted nurses’ perceived impact of AI suggests that those with higher educational attainment and those working in certain departments may have greater exposure to or understanding of AI technologies. This aligns with previous studies showing that advanced education enhances technological literacy and openness to innovation among healthcare professionals.^7,9 Departments that are more technology focused or involved in informatics may provide more frequent interactions with AI, thereby positively shaping perceptions.⁵

Predictors of preparedness for AI among nurse leaders

This study examined the predictors of nurses’ self-reported readiness for AI adoption. Interestingly, the model did not yield any statistically significant results, indicating that the factors under consideration did not, on their own or in combination, explain a notable amount of the variance in AI-perceived preparedness. These findings are surprising and require further investigation.

A few possibilities may explain why the results are non-significant. First, although the sample size was large, it might have been inadequate to observe nominal impacts. Second, the measures of AI readiness may have omitted some dimensions of the construct's multifaceted nature. There could be several other factors not included in this study, such as organizational factors (e.g., leadership endorsement and organizational climate), personal factors (e.g., personality and cognitive styles), or technological factors (e.g., availability of AI tools and ease of use of AI applications).

These findings differ from those of other studies on healthcare technology adoption. While our study found no significant predictors of AI preparedness among nurse leaders, previous research has identified various factors that influence technology readiness in healthcare settings. For instance, Kim et al.²⁴ reported that age impacts technology adoption, whereas Ciampa et al.²⁵ stressed the importance of education in AI readiness. Such differences and many others, including those from studies such as Vaidya et al.,²¹ which reported that higher education correlates with AI tool usage being trusted more, point out the gap in research concerning the determinants of AI readiness among nurse leaders.

Contrary to previous studies, these results highlight the complexity of AI readiness. This suggests that influencing factors may be highly context dependent, particularly in the unique healthcare environment of Hail City, Saudi Arabia. More representative samples along with more complex AI readiness criteria and a broader array of possible organizational, personal, and technological predictors should be included in future investigations. Nurse leaders’ experiences with the integration of technology such as AI would also be useful in understanding the phenomenon and could be studied using qualitative approaches.

By contrast, the absence of significant predictors of preparedness highlights the potential disconnect between recognizing AI's impact of AI and feeling equipped to manage or implement AI solutions. Similar gaps have been reported in other healthcare settings, where professionals acknowledge the benefits of AI, but report insufficient training or confidence in use AI effectively.^13,26 This suggests that organizational factors, such as targeted training programs, leadership support, and institutional readiness, may play a larger role than individual characteristics in shaping preparedness.

Conceptual model of factors influencing AI perception and readiness

A conceptual model was developed (Figure 3) to visually synthesize the complex interplay of factors influencing nurse leaders’ perceptions and readiness. The model illustrates how ‘Individual Factors,’ encompassing nurse leader characteristics such as education level, AI understanding, and desire for AI training, interact with ‘Organizational Factors’ like training availability and leadership support, and ‘Technological Factors’ including AI usability and complexity, ultimately shaping ‘Outcomes’ of AI impact perception and readiness for AI integration. This representation is supported by the researchers’ finding that education level affects AI impact perception, highlighting the model's depiction of direct influences. The model also emphasizes the interactions between factor categories, acknowledging that individual AI understanding can influence perceived AI usability, and organizational support can moderate the effect of AI complexity on readiness. This holistic view builds upon existing frameworks such as the TAM and underscores the need for multifaceted approaches to AI integration.

Figure 3.

Conceptual model of factors influencing AI perception and readiness.

Interpretation of predictors and implications for practice

These findings underscore the need for tailored education and training initiatives to build practical AI competencies among nurse leaders across departments and educational backgrounds. Developing comprehensive AI training programs, including hands-on experience and ethical considerations, could help bridge the preparedness gap and facilitate the smoother integration of AI into nursing leadership. Future research should explore the organizational and systemic factors that influence AI readiness to design effective support mechanisms.

To address the gap between nurse leaders’ positive perceptions of AI and their self-reported preparedness, we proposed a comprehensive AI capacity-building program. This program is structured in three key phases: foundational knowledge, AI awareness, AI skill development, leadership, and strategic implementation. The first phase focuses on building a strong understanding of AI concepts, terminology, and ethical considerations. The second phase provided hands-on training using AI tools, data interpretation, and project management skills. Finally, the third phase equips nurse leaders with the leadership and strategic planning skills needed to effectively integrate AI into their practices and drive organizational change. By addressing these critical areas, the program aims to empower nurse leaders to navigate the evolving landscape of AI in healthcare confidently and competently.

For instance, the program's emphasis on ethical considerations directly responds to concerns raised by nurse leaders regarding data privacy and algorithmic bias (as discussed earlier in the ‘Predictors of AI impact on professional roles’ section). Furthermore, the inclusion of hands-on AI tool training aligns with the expressed need for practical AI skill development, as highlighted in Table 2, where a significant percentage of respondents indicated a desire for training in AI techniques.

This framework also aligns with technology adoption theories, such as the TAM, by addressing both perceived usefulness (by demonstrating AI's benefits) and perceived ease of use (through practical training), which are crucial for technology acceptance.²¹

Addressing the educational disparities revealed in our study, the program advocates for differentiated learning pathways, ensuring that both BSN- and MSN-prepared nurses receive tailored instructions that build upon their existing knowledge and address their specific needs.²²

The program incorporates department-specific modules to address the unique needs and challenges identified in our research. As nurse leaders in medical departments reported lower perceptions of AI's impact of AI on their roles, training in these departments should emphasize AI applications that directly support their workflows, such as AI-driven tools for enhanced patient education, streamlined care coordination, and improved patient monitoring. This tailored approach aims to demonstrate AI's relevance and value of AI beyond diagnostic tasks, thereby fostering its greater acceptance and utilization within medical units.²³

Correlation between perceived preparedness for AI and perceived impact of AI on professional roles among nursing leaders

As noted, there is a perplexing gap between nurse leaders’ AI potential appreciation and their integration preparedness, which can be explained by the missing correlation. This gap, coupled with our earlier findings on the non-significant predictors of AI preparedness, underscores the complex nature of AI integration in nursing leadership. This illuminates the fact that, beyond perception, a lack of organizational support and differences in learning styles affect AI preparedness. Thus, healthcare institutions need to focus on the formation of comprehensive AI training courses with particular attention paid to both technical and higher-order AI skills so that nurse leaders can thoroughly assess and employ AI in their decision-making processes. Attention to moral issues is highly warranted since the study results showed that such frameworks can have positive ethical impacts on the AI adoption self-efficacy of nurse leaders.

Moreover, the difference in education gaps highlights the need for separate training methodologies, where MSN-prepared nurses seem to be more affected by AI than BSN nurses. Departmental gaps and other structural issues also contribute substantially to readiness; therefore, effective AI integration necessitates collaborative multi-stakeholder approaches. Gendered inequities in leadership positions and other leadership issues were noted in this study. This, along with individual training and organization and system design, suggest that integrating AI within healthcare requires a comprehensive strategy.

Institutional and system-level barriers to AI preparedness

Although Hail City nurse leaders understand how AI can create paradigm shifts in healthcare, their unpreparedness stems from institutional and systemic barriers. First, there is no organized auxiliary AI training as specialized educational seminars or workshops are not available. Owing to the lack of institutional importance placed on AI assimilation, there is no time, funding, or leadership interest directed toward enhancing nurse leaders’ skills. In addition, inadequate supporting technological frameworks, ambiguous institutional AI policies, stifled practical instructional learning, and autonomous skill-building are needed to foster confidence. Sociocultural barriers to technological advancement, along with other clinical obligations, can limit nurse leaders’ involvement in AI initiatives. These challenges can be resolved by developing sophisticated training programs accompanied by other elementary strategies, such as bolstered leader advocacy, improved digital infrastructure, and the amalgamation of AI skills into routine professional learning.

Implications for leadership development programs in Saudi Arabia

The findings have significant implications for the development of leadership programs for nurse leaders in Saudi Arabia, particularly in light of Vision 2030, which emphasizes technological advancements in the healthcare sector. The gap between nurse leaders’ positive perceptions of AI and their self-reported preparedness underscores the need for targeted training. These programs should incorporate technical skills training, focusing on the practical application of AI tools and data interpretation, and robust ethics training to address concerns such as data privacy and algorithmic bias. Furthermore, considering the influence of educational background on AI perception, leadership development should offer differentiated learning pathways that cater to various educational levels and departmental needs, with MSN nurses demonstrating a greater understanding of the potential impact of AI.

Implications for AI policy in Saudi Arabia

The findings offer valuable insights into shaping AI policies within Saudi Arabia's healthcare sector, particularly in alignment with the National Strategy for Data and AI. Given the identified concerns regarding ethical considerations, most notably, data privacy and potential algorithmic bias, policy development should prioritize the establishment of clear guidelines and frameworks for the responsible use of AI in healthcare. This includes mandating bias detection and mitigation strategies in AI algorithms, ensuring data security and patient confidentiality, and defining accountability and liability in AI-assisted decision-making. Moreover, to foster the effective integration of AI, policies should promote ongoing education and training for healthcare professionals, address potential workflow disruptions, and support research and innovation in AI applications in the Saudi Arabian healthcare context.

Limitations and future directions

This study has some limitations. First, its cross-sectional design precludes the establishment of causal relationships; future research should utilize longitudinal approaches to examine the evolution of perceptions and preparedness over time and identify potential causal links. Second, the reliance on self-report data introduces the potential for social desirability bias, as participants may have provided responses they perceived as favorable. Future studies could incorporate observational data or triangulate self-reports with alternative measures to minimize this bias. Finally, the study's focus on Hail City, including both government and private hospitals, limits the generalizability of the findings, given the potential cultural and institutional differences. Future research should consider multi-site designs across diverse regions or healthcare systems to enhance generalizability.

Conclusion

This study aimed to understand the perceptions of and preparedness for AI among nurse leaders in Hail City, Saudi Arabia. The analysis demonstrated that, while nurse leaders generally hold favorable views of AI, their preparedness for its integration is inconsistent and significantly influenced by educational qualification and the department they worked in. Specifically, nurse leaders had positive perceptions of AI's impact on their roles. However, those with lower education level had lower perceived preparedness, where BSN holders showed lower perceptions than those with an MSN. Therefore, healthcare organizations must prioritize and invest in comprehensive AI education and training programs that are tailored to the specific needs of nurse leaders. Only by proactively empowering nurse leaders with the knowledge, skills, and ethical framework to navigate AI confidently can we ensure its successful and responsible implementation to optimize patient care and transform healthcare delivery.

Footnotes

ORCID iDs

Nader Alnomasy

Habib Alrashidi

Salman Alsaqri

Ethical considerations

The study was approved by the Institutional Review Board (no. 22023-28). Informed consent was obtained,which explicitly stated that participation was completely voluntary and that participants could withdraw at any time.

Author contributions

Nader Alnomasy contributed to writing–review and editing and writing–original draft. Petelyne Pangket contributed to writing-review and editing,writing-original draft,methodology,investigation,formal analysis,and data curation. Bader Alreshidi contributed to conceptualization,formal analysis,investigation,methodology,supervision,writing–original draft,writing–review,and editing. Habib Alrashidi contributed to writing–review and editing,formal analysis,and conceptualization. Lucy Shinners contributed to writing–review and editing,writing–original draft,methodology,investigation,and formal analysis. Salman Alsaqri contributed to conceptualization,methodology,supervision,validation,and writing–review and editing. Romeo Jr Mostoles contributed to writing–review and editing,writing–original draft,supervision,and methodology.

Funding

The author(s) disclosed receipt of the following financial support for the research,authorship,and/or publication of this article: This research has been funded by Scientific Research Deanship at University of Ha’il Saudi Arabia through project number BA-23 019.

Declaration of conflicting interests

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

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Artificial intelligence in health care: Assessing impact on professional roles and preparedness among hospital nurse leaders

Abstract

Introduction

Aim

Methods

Results

Conclusion

Keywords

Introduction

Methodology

Design

Setting

Participants and sampling

Questionnaire

Data collection

Data analysis

Refinement of the SHAIP tool post-validation

Ethical considerations

Results

Discussion

Improving AI perception model accuracy in healthcare

Nurse leaders’ perceptions of AI impact and preparedness

Theoretical interpretation of AI perceptions and preparedness

Predictors of AI impact on professional roles

Predictors of preparedness for AI among nurse leaders

Conceptual model of factors influencing AI perception and readiness

Interpretation of predictors and implications for practice

Correlation between perceived preparedness for AI and perceived impact of AI on professional roles among nursing leaders

Institutional and system-level barriers to AI preparedness

Implications for leadership development programs in Saudi Arabia

Implications for AI policy in Saudi Arabia

Limitations and future directions

Conclusion

Footnotes

ORCID iDs

Ethical considerations

Author contributions

Funding

Declaration of conflicting interests

References