Navigating technostress: a deep dive into health practitioners' technological challenges in hospital settings

Abstract

Background and purpose

Despite the increasing integration of information technologies in healthcare settings, limited attention has been given to understanding technostress among health practitioners in hospitals. This study aims to assess the prevalence of technostress creators among health practitioners and explore potential factors contributing to its occurrence, with the ultimate goal of informing strategies to mitigate its impact.

Method

Data were collected through a validated questionnaire administered to health practitioners at Tehran Apadana Hospital in Iran. The questionnaire encompassed demographic information and technostress assessment items. Statistical analysis was conducted using SPSS software to examine the relationship between technostress levels and demographic characteristics.

Findings

The analysis revealed that approximately 41% of health practitioners experienced medium levels of technostress, with 36% reporting high levels and 23% reporting low levels (χ2F = 55.4; p < 0.001). Notably, technology uncertainty emerged as the primary driver of technostress, followed by techno-overload, techno-complexity, techno-insecurity, and techno-invasion. Surprisingly, no significant relationship was found between technostress levels and demographic characteristics.

Conclusion

The study underscores the pressing need to address the prevalent medium to high levels of technostress among health practitioners in hospital environments. By shedding light on the key stressors and their distribution, these findings can inform human resources management strategies within healthcare systems to effectively support practitioners in navigating and managing technostress challenges.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-024-12196-1.

Introduction

The advent of the information age has ushered in a new era marked by the widespread adoption of Information and Communication Technologies (ICTs) across various spheres of life, including healthcare [1, 2]. The integration of modern technologies, such as computer and networking systems, has revolutionized the way healthcare services are delivered, offering benefits such as high memory capacity, speed, accuracy in information management, and resource optimization [3, 4]. However, the rapid advancements in technology have also given rise to challenges related to technostress, a psychological disorder experienced by individuals in the context of office automation [5]. As highlighted in the literature, the increasing reliance on ICTs in healthcare settings has led to heightened levels of anxiety and stress among health practitioners, impacting individual well-being, mental health, job performance, and organizational outcomes [6, 7].

The concept of technostress, which combines technology and stress, has gained prominence in research as a result of the complexities and challenges associated with the integration of ICTs in the workplace [5]. Researchers have emphasized the detrimental effects of technostress on individuals and organizations, citing disruptions in work environments, decreased job performance, increased job dissatisfaction, and adverse health outcomes [8, 9]. The relationship between technology use and anxiety levels among individuals has been a subject of growing concern, with studies indicating that the prevalence of technostress can disrupt work environments, diminish job performance, and lead to adverse health outcomes [10, 11]. The rapid pace of technological change and the increasing interaction with computers and digital tools have further compounded the management of stress factors in the workplace, underscoring the need for effective strategies to address technostress in healthcare settings [12, 13].

Technostress, as a modern disease of adaptation caused by the inability to cope with new computer technologies, manifests in various ways, including resistance to technology adoption and over identification with computer technology [5, 8]. The psychological disorder resulting from the use of new technologies has been linked to factors such as experience with technology, age, supervisory pressure, and the overall work environment climate [14–16]. Environmental and social factors, including inappropriate working conditions, power struggles, and job insecurity, have been identified as key contributors to technostress among individuals in the workplace [16–18]. Symptoms of technostress can manifest as anxiety, isolation, negative attitudes towards technology, irritability, exhaustion, increased errors, physical discomfort, and mental health issues, highlighting the pervasive impact of technology-related stress on individuals' well-being and job performance [19, 20].

The increasing reliance on ICTs in hospitals has raised concerns about the impact of technology on healthcare practitioners, who must continually update their technological skills to remain effective in their roles [21, 22]. The integration of new technologies in healthcare settings has introduced challenges for health practitioners, leading to increased levels of stress and anxiety related to technology use [21, 23]. The need to address technology stress among health practitioners is essential to ensure optimal performance and well-being in healthcare environments [24, 25]. By examining the factors contributing to technostress and identifying strategies to mitigate its effects, healthcare organizations can promote a supportive and efficient work environment for their staff [9, 21, 24, 26–28].

The literature on technostress highlights a complex interplay of factors that affect employee well-being and productivity across various organizational settings [28–31]. Research indicates that technostress levels are consistent among different occupational groups, demonstrating its universal impact [28, 31]. Organizational environments significantly influence perceptions of technostress, with centralized companies often reporting higher levels, particularly in innovative contexts [28, 29, 31]. Contributing factors include rapid technological change, inadequate technical support, and negative attitudes toward technology [32, 33]. Health practitioners have reported physical symptoms related to technostress, and coping mechanisms such as discussions and breaks from technology have been identified [34, 35]. Additionally, demographic factors like gender, age, and occupation play a role in technostress levels, which are linked to reduced job satisfaction and increased turnover [32, 33]. The importance of person-environment fit in minimizing stress levels has been emphasized, alongside the need for organizational support to manage technology-related stress effectively [36, 37]. Furthermore, there are legal implications regarding the classification of technostress as a disability, prompting recommendations for preventive measures to reduce employer liability [29, 32, 36, 37].

The study aims to assess the status of technostress among health practitioners in a private hospital in Iran. While the primary focus is on understanding the current state of technostress, we will also discuss potential implications and general recommendations based on our findings to enhance workplace productivity and employee well-being.

Methodology

Study design

This study utilized a cross-sectional survey conducted at Tehran Apadana Hospital in Tehran, Iran (2022–2023).

Population

The study population included all health practitioners located in the Apadana Hospital.

Research questions

1. RQ1: What is the level of technostress among health practitioners of Apadana Hospital?

2. RQ2: Is there a significant relationship between technostress and demographic characteristics (Technostress, Gender, Marital status, Job status, Education, Age, and Job experience) of health practitioners in Apadana Hospital?

Sampling

In this study, a census method was employed to ensure comprehensive data collection from the target population of health practitioners at Tehran Apadana Hospital. The decision to utilize a census approach was influenced by specific study limitations, including time constraints and the need for a thorough understanding of technostress levels among all eligible practitioners.

The primary limitations included limited access to participants during busy clinical hours and the necessity to gather data promptly due to project timelines. These factors impacted our ability to conduct a more extensive sampling process.

Given the relatively small size of the eligible population (85 practitioners), a census method was deemed appropriate to capture a complete picture of technostress within this specific group. This approach allowed us to include all consenting participants, thereby enhancing the reliability and validity of our findings.

The sample size was determined based on the total number of eligible participants within the hospital. Out of 85 eligible practitioners, 55 consented to participate, resulting in a participation rate of approximately 65%. This sample size was sufficient for conducting meaningful statistical analyses while reflecting the diversity of roles within the hospital, including physicians, nurses, and laboratory staff.

All health practitioners working at Tehran Apadana Hospital during the study period were considered eligible. Participants must have been employed in the hospital for at least three months to ensure familiarity with the work environment and technology used. Individuals on extended leave or those who declined to participate were excluded from the study.

Data collection

The questionnaire was distributed to a diverse group of professionals, including physicians, nurses, laboratory staff, surgical technicians, and patient care technicians specifically working in critical divisions such as the Intensive Care Unit (ICU) and Coronary Care Unit (CCU). Prior to distribution, the purpose of the study and the significance of the questionnaire were communicated to all potential participants during informational sessions. This ensured that participants understood the context and importance of their contributions. The questionnaires were then distributed in person by trained research assistants who were present in the hospital during various shifts to maximize participation. This approach facilitated direct engagement with participants and allowed for immediate clarification of any questions regarding the questionnaire. Participants were informed that completing the questionnaire would take approximately 10–15 min. This time frame was communicated to ensure that participants could allocate sufficient time without feeling rushed. After one day, the completed questionnaires were collected by the research team to maintain confidentiality and ensure data integrity. Participants were assured that their responses would remain anonymous and used solely for research purposes.

Instrument

The questionnaire consisted of two parts: demographic characteristics and a technostress questionnaire based on Ragu-Nathan et al.’s [38]. Completing the questionnaire took approximately 10 to 15 min. To assess technostress, we utilized a scale developed in prior research [31, 38], which encompasses five key dimensions: Techno-overload (5 items), Techno-Invasion (4 items), Techno-Complexity (5 items), Techno-Insecurity (5 items), and Techno-Uncertainty (4 items). Participants rated each item based on its frequency using a Likert scale from 1 to 5, where 1 indicates strong disagreement and 5 indicates strong agreement. The Cronbach's alpha coefficient for the Persian validated version of the questionnaire [39] was found to be 0.824, indicating good reliability (Supplementary file 1).

Data analysis

Data were collected and analyzed using SPSS software version 24. Statistical methods such as frequency percent, polygon graph, percent and mean data, and multivariate regression coefficient test were employed. Normality assumptions were tested using the Kolmogorov–Smirnov method. Pearson's correlation coefficient was initially considered for RQ2 analysis but was replaced by multiple ordinal correlation coefficient due to the nature of the variables.

Findings

To address RQ1, we conducted mean comparison tests, the results of which are presented in Table 1 and Fig. 1 below. We changed the mean scores to a triple scale of class intervals for interpreting the composite scores e.g. Low (1–2.33), Medium (2.34–3.67) and High (3.68–5.00). In order to change the values from 5 point Likert scale to composite scores, we calculated the number of persons regarding their responses as is common in related statistical instructions [40]. For example, number 18 in front of the first question “I am forced by this technology to work much faster” means that 18 respondents chose items close to Low like (1) Strongly Disagree to (2) Disagree in a way their values stand between 1 to 2.33. Analysis of the data in Table 1 and Fig. 1 revealed significant findings (χ2f = 55.4, p < 0.001, df = 4) indicating that technostress levels among health practitioners are distributed as follows: 41% at a medium level, 36% at a high level, and 23% at a low level. These results, with a confidence level exceeding 99%, lead to the conclusion that stress levels are notably elevated in techno-uncertainty (65.6%) and techno-overload (45.5%), while techno-invasion (49.1%), techno-complexity (43.6%), and techno-insecurity (52.7%) fall within the medium range.

Table 1.

Distribution of technostress scores (n = 55)

	Technostress creators	Low		Medium		High		Mean
		Frequency	Percent	Frequency	Percent	Frequency	Percent	r
Techno-overload	I am forced by this technology to work much faster	18	12.7	6	10.9	31	56.4	3.46
	I am forced by this technology to do more work than I can handle	30	55	12	21.8	13	23.6	2.52
	I am forced by this technology to work with very tight time schedules	26	47	16	29.1	13	23.6	2.62
	I am forced to change my work habits to adapt to new technologies	8	15	13	23.6	34	62	3.75
	I have a higher workload because of increased technology complexity	26	47	16	29.1	13	23.6	2.65
	Techno-overload (sum)	4	7.3	21	38.2	30	45.5	3.65
Techno-invasion	I spend less time with my family due to this technology	36	66	11	20	8	14.5	2.42
	I have to be in touch with my work even during my vacation due to this technology	36	66	8	14.5	11	20	2.43
	I have to sacrifice my vacation and weekend time to keep current on new technologies	27	49	13	23.6	15	27.3	2.76
	I feel my personal life is being invaded by this technology	34	62	15	27.3	6	10.9	2.39
	Techno-invasion (sum)	20	36	27	49.1	8	14.5	2.35
Techno-complexity	I do not know enough about this technology to handle my job satisfactorily	35	64	6	10.9	14	25.5	2.85
	I need a long time to understand and use new technologies	30	55	8	14.5	17	30.9	3.12
	I do not find enough time to study and upgrade my technology skills	32	58	12	21.8	11	20	2.87
	I find new recruits to this organization know more about computer technology than I do	21	38	17	30.9	17	30.9	3.45
	I often find it too complex for me to understand and use new technologies	35	64	10	18.2	10	18.2	2.71
	Techno-complexity (sum)	16	29	24	43.6	15	27.3	2.69
Techno-insecurity	I feel constant threat to my job security due to new technologies	37	67	8	14.5	10	18.2	2.76
	I have to constantly update my skills to avoid being replaced	16	29	11	20	28	50.9	3.94
	I am threatened by coworkers with newer technology skills	34	62	17	30.9	4	7.3	2.67
	I do not share my knowledge with my coworkers for fear of being replaced	41	75	7	12.7	7	12.7	2.52
	I feel there is less sharing of knowledge among coworkers for fear of being replaced	28	51	15	27.3	12	21.8	3.11
	Techno-insecurity (sum)	16	29	29	52.7	10	18.2	2.55
Techno-uncertainty	There are always new developments in the technologies we use in our organization	10	18	8	14.5	37	67.3	2.74
	There are constant changes in computer software in our organization	14	26	12	21.8	29	52.7	2.48
	There are constant changes in computer hardware in our organization	19	35	18	32.7	18	32.7	2.01
	There are frequent upgrades in computer networks in our organization	9	16	7	12.7	39	70.9	2.77
	Techno-uncertainty (sum)	6	11	13	23.6	36	65.6	3.75
	Total	62	23	114	41	99	36	2.99

Fig. 1.

Frequency percentage of technostress scores

Health practitioners have shown a predominant level of technostress across various technology-related dimensions. Among the factors contributing to technostress, technology-uncertainty emerged as the most pronounced, with an average rating of 3.75. This was followed by techno-overload, techno-complexity, techno-insecurity, and techno-invasion, which received average ratings of 3.65, 2.69, 2.55, and 2.35, respectively.

To investigate RQ2, we conducted mean ordinal and sequential regression analyses, the results of which are detailed in Tables 2, 3, 4, 5 and 6 below. Prior to the primary analysis, we assessed the underlying assumptions, including conducting Probability Ratio tests and evaluating Goodness of Fit.

Table 2.

Technostress scores and demographic characteristics

Variable		Frequency	Percent
Technostress	Low	62	23
	Medium	114	41
	High	99	36
Gender	Woman	48	87.3
	Man	7	12.7
Marital status	Married	44	80
	Single	11	20
Job status	Permeant	40	72.7
	Contractual	10	18.2
	Temporary	2	3.6
	Other	3	5.5
Education	Bachelor’	38	69.1
	Master’	16	29.1
	PhD	1	1.8
Age	20—30	4	7.3
	31—40	28	50.9
	41—60	23	41.8
Job experience (years)	1—5	4	7.3
	6—10	10	18.2
	11—15	17	30.9
	16—20	12	21.8
	21—25	12	21.8
Sum		55	100

Table 3.

Probability ratio test statistics

Model	Likelihood	Probability Ratio Test
Null	89.7	χ2	df	Significant Level
Final	85.62	4.1	8	p < 0.50

Table 4.

Goodness of fit test

chi-square	χ2	Freedom degree	Significant Level
Pearson	74.5	78	p < 0.558
Deviation	74.5	78	p < 0.590

Table 5.

Pseudo-R-squared statistics

Determination coefficients	Value
Cox and Snell	0.072
Nagelkerke	0.085
McFadden	0.041

Table 6.

Model parameter estimation

Variable	Estimation	Standard error	Parent	df	Sig. level
Age	0.005	0.049	0.007	1	0.93
Marital status	0.067	0.808	0.801	1	0.371
Job status	- 1.348	1.507	0.221	1	0.638
Education	1.593	2.226	0.057	1	0.474
Job experience	2.61	- 0.96	0.569	1	0.561
Gender	1.65	- 2.23	0.968	1	0.65

The results presented in Table 3 indicate that the probability ratio test, with a chi-square value of 4 and 8 degrees of freedom at a significance level of P < 0.05, demonstrates that the final model has successfully outperformed the null model. This indicates that the regression model is robust, and the predictor variables effectively account for the variations in the criterion.

Pearson’s chi-squared test, along with chi-square deviations assessed for Goodness of Fit, indicate that the regression model is sound, and the predictor variables effectively capture the variations in the criterion (refer to Table 4).

The Pseudo-R-squared statistics in Table 5 indicate the following determination coefficients: Cox and Snell with a value of 0.072, Nagelkerke with a value of 0.085, and McFadden with a value of 0.041. These statistics provide insights into the amount of variance explained by the model in relation to technostress among the study participants.

In assessing the coefficients of determination within the context of sequential regression, as demonstrated by the Pseudo-R-squared values in Table 6, the three statistics have collectively accounted for approximately 4.1% to 8.5% of the variance in technostress among health practitioners showing that the variance explained by the model is low.

The association between the criterion and predictor variables, as indicated by the regression coefficients in Table 6, reveals the significance levels (p < 0.05) of various factors such as gender, age, marital status, job status, education level, and job experience, with coefficients ranging from −1.348 to 1.593 units in logarithmic terms. However, none of these predictors exhibited statistical significance (p < 0.05) and failed to strongly predict technostress. Consequently, there appears to be no discernible relationship between technostress and the demographic characteristics of health practitioners at Apadana Hospital.

Discussion

The findings of our study reveal that technology uncertainty emerged as the most significant factor contributing to technostress among health practitioners, with a mean rating of 3.75, followed closely by techno-overload (3.65), techno-complexity (2.69), techno-insecurity (2.55), and techno-invasion (2.35). Notably, the absence of a statistical relationship between technostress and demographic variables—such as age, marital status, job status, education level, job experience, and gender—suggests that technostress is a pervasive issue affecting all practitioners regardless of their backgrounds. This universality highlights the urgent need to explore the underlying causes of this phenomenon.

In comparison to previous studies, such as Akhtari et al. 2013, which identified techno-complexity, techno-overload, and techno-invasion as primary contributors to organizational stress, our findings present a nuanced perspective [41]. While those factors remain relevant, our study indicates that health practitioners are particularly impacted by issues related to technological change and the need for continuous skill updates. Specifically, concerns such as "technology changes," "updating computer and technology networks," and "the fear of job loss due to inadequate technology skills" were prominently rated by participants. This aligns with Tiemo and Ofua's 2010 research, which identified similar stressors among librarians, emphasizing the broader applicability of these findings across different professional contexts [42].

Interestingly, while previous literature has often linked demographic factors such as gender and age to technostress [26, 29], our study found no such relationships. This discrepancy may reflect the rapidly evolving technological landscape of the 2020s, where advancements—particularly in artificial intelligence—have accelerated the pace of change beyond what was experienced in previous decades [43]. As noted by Lee et al. (2016), this rapid development can exacerbate technostress among employees as they struggle to adapt to new systems and technologies [44].

Moreover, our findings resonate with recent studies that highlight the role of organizational culture in shaping technostress experiences. For instance, research by Tarafdar et al. 2019 emphasizes that an organization's support for technological adaptation can mitigate technostress levels among employees [45]. In contrast, environments lacking adequate training and resources may exacerbate feelings of uncertainty and overload. This suggests that while individual factors contribute to technostress, organizational contexts play a critical role in either alleviating or intensifying these stressors [45].

The implications of stress arising from techno-uncertainty and techno-overload are significant; they can lead to burnout, decreased job satisfaction, and ultimately lower quality of patient care [30, 33]. Given these potential outcomes, healthcare organizations must implement targeted strategies aimed at alleviating technostress. Recommendations include providing ongoing training and support for practitioners to help them navigate technological advancements effectively. Additionally, fostering a supportive work environment that acknowledges the challenges associated with technology use is essential.

To mitigate these issues further, managers and employees should prioritize user-friendly technology interfaces, ensure information security, create practical work environments, and seek assistance from IT professionals when needed. By addressing these key stressors proactively, healthcare organizations can enhance practitioner well-being and improve patient care quality.

This study underscores the necessity for healthcare leaders and policymakers to prioritize interventions that tackle technostress comprehensively while laying a foundation for future research into effective mitigation strategies in this rapidly changing technological landscape. By integrating insights from existing literature with our findings, we can better understand the multifaceted nature of technostress and its implications for both practitioners and healthcare systems at large.

Limitations

Although our study offers valuable insights into the prevalence and dimensions of technostress among health practitioners, it is not without limitations. The cross-sectional design, coupled with the specific context of Apadana Hospital, may restrict the generalizability of our findings to broader healthcare settings. Future research would benefit from adopting a longitudinal approach to examine how technostress levels evolve over time and in response to various interventions.

Importantly, due to the results indicating that none of the demographic factors was able to predict technostress, there might be other factors that are of more effect in the context studied. Specifically, psychological or skill-related factors need to be taken more into account when studying technostress in future studies. To identify the factors that might influence technostress among health practitioners the most, it is advised to specify more complex models incorporating other sociotechnical factors like personality traits, self-efficacy, coping mechanisms, and organizational culture to obtain a more nuanced understanding of technostress within healthcare environments. By addressing these areas in future studies, researchers can develop a more comprehensive framework for understanding and mitigating technostress among health practitioners.

Conclusion

This study elucidates the prevalent levels of technostress creators among health practitioners at Tehran Apadana Hospital, revealing a substantial portion of the workforce battling with medium to high levels of technostress, predominantly driven by technology uncertainty and techno-overload, irrespective of demographic backgrounds. The findings indicate a critical need for healthcare management to adopt comprehensive strategies that mitigate technostress, emphasizing the importance of ongoing training, stress management resources, and the deployment of user-friendly technology. By highlighting the universal impact of technostress within the healthcare sector, the study underscores the urgency for healthcare leaders and policymakers to prioritize interventions that address these key stressors. Implementing targeted support measures can enhance practitioner well-being, improve patient care quality, and foster a healthier work environment, laying a foundation for future research on effective technostress mitigation and the dynamic between technology use and healthcare practitioners' mental health.

Authors’ contributions

H.K. and HR.S. were responsible for the study's conception and design. H.K. and HR.S. Preparing the first draft of the manuscript and revising the manuscript. T.W. did the analysis of the results, made critical revisions to the paper for important intellectual content, and supervised the study. All authors have read and approved the final manuscript.

Funding

Not applicable.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

In the research study conducted, informed consent was obtained from the provider participants who completed the questionnaires. Participants were assured that their information would be kept confidential throughout all phases of the study, and their privacy was respected. The Tarbiat Modares University Ethics Committee/IRB has waived the requirement for ethical approval for this study, as it did not involve patients. All methods employed in the study were conducted in accordance with relevant guidelines and regulations, including adherence to the Declaration of Helsinki. Confidentiality of participant information was maintained, and all procedures were carried out with the utmost respect for ethical considerations and participant rights.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.