Factors influencing Registered Nurses’ intention to use Health Information Technology in clinical practice: An integrative literature review

Sarah Ibrahim, RN, MN, PhD¹
Lorie Donelle, RN, PhD¹
Souraya Sidani, PhD²
Sandra Regan, PhD¹

1. Arthur Labatt Family School of Nursing, Western University, London, ON.
2. Daphne Cockwell School of Nursing, Ryerson University, Toronto, ON.

Abstract

BACKGROUND:There has been an increased and expanded implementation of health information technology to support the provision of care, enhance patient safety, and reduce health care expenditure. The development and integration of health information technology create significant changes to nursing practice which has been associated with nurses’ low intention to use health information technology; intention is a direct predictor of actual usage. This is particularly concerning as nurses constitute the largest group of regulated healthcare providers, spend the most amount of time with patients, and are the largest user-group of health information technology in practice; highlighting the importance of understanding the factors that influence nurses’ intention to use health information technology in practice. 

AIMS: To explore and summarize current empirical literature on factors that influence nurses’ intention to use health information technology in practice.

METHODS:An integrative literature review. Electronic searches were conducted using six scholarly databases

FINDINGS:Factors reported to influence nurses’ intention to use health information technology in practice reflected characteristics of nurses using the technology, the health information technology and the organizational environment. 

CONCLUSION:The findings highlight the importance for nurse and health system managers as well as educators to foster an organizational culture that supports the use of HIT in nurses’ day-to-day practice by: a) offering adequate education and training; b) including user champions to support HIT implementation; c) having an adequate representation of nurses during the design phase of the HIT and being receptive to their feedback; and d) having sufficient technological support in practice.

Keywords: Health Information Technology, behavioural intention, nurses, nursing, integrative review, and predictors

Acknowledgement:The researchers would like to thank the Iota Omicron Chapter Research Grant and Age Well Graduate Student and Postdoctoral Award in Technology and Aging for funding. 

Background

Over the last two decades, there has been increased and expanded implementation of digital health services. Specifically, we are witnessing an increased use of health information technology (HIT) to support the provision of care, improve quality of health care services, and enhance patient safety(Al-Khasawneh, & Hijazi, 2016; Health Canada, 2015). The use and integration of HIT for the delivery of health care services have the potential to support patients and health care providers (HCPs) in planning and carrying out care (Bowles, Dykes, & Demiris, 2015; Canadian Home Care Association, 2015; Home Care Ontario, 2016; Hsiao & Chen, 2016; Kuo, Liu, & Ma, 2013). Despite the potential benefits, the integration of HIT remains difficult and creates significant changes to nursing practice; resulting in low intention to use HIT by nurses in practice (Creswell, Bates, & Sheikh, 2013; Hsiao & Chen, 2016; Hung, Tsai & Chuang, 2014; Ifinedo, 2012). Low intention to use HIT is particularly concerning as nurses constitute the largest group of regulated HCPs, spend more time with patients than any other HCPs (Delucia, Ott, & Palmieri, 2008)and are the largest user-groups of HIT (Gagnon, Orruno, Asua, Abdeljelil, & Emparanza, 2012). Thus, nurses represent an important group to target for HIT implementation processes (Cho, Kim, Choi, & Staggers, 2016). 

The low intention to use HIT is attributed to the interaction between humans and technology, which is affected by a number of factors associated with the human, technological, and organizational/ environmental characteristics (Rouleau et al., 2017). Theoretical and empirical evidence suggests that behavioural intention to use HIT is a direct predictor or determinant of actual HIT usage (Ifinedo, 2012; Venkatesh, Morris, Davis, & Davis, 2003); highlighting the importance of understanding the factors that influence this predictor.

Understanding these factors can guide the development of initiatives and strategies to address them with the goal of enhancing nurses’ intention and actual HIT use in practice. To date, several studies have been conducted examining the factors that influence nurses’ intention to use HIT in their practice, however, it is unclear which set of factors are most influential. Further, Strudwick (2015) conducted an integrative review of studies that investigated factors predicting nurses’ use of HIT, however, the studies included in the review were required to use the Technology Acceptance Model (TAM), TAM2, Unified Theory of Acceptance and Use of Technology (UTAUT) models or a variation of these models. This requirement may have potentially led to the exclusion of studies that examined other factors that influence nurses’ intention to use HIT using alternative models (i.e., Theory of Planned Behaviour and Theory of Reasoned Action) or no models to being excluded; potentially contributing to lack of comprehensiveness and clarity about the most influential factors.

Aims

The aims of this integrative literature review were to explore and summarize the current literature on the factors that influence nurses’ intention to use HIT in practice. The research questions guiding this review were: (1): What are the factors that influence nurses’ intention to use HIT in clinical care and community health sectors? and (2) What factors have a direct or indirect (moderated or mediated) association with nurses’ intention to use HIT in practice? 

Design

An integrative literature review method was conducted. This review method was used as it allows for a more comprehensive understanding of the phenomenon of interest through the inclusion of diverse research methodologies (i.e., experimental and non-experimental studies) (Whittemore & Knafl, 2005). The integrative review process followed Whittemore and Knafl’s (2005) five steps: (1) problem identification; (2) conducting a comprehensive literature search to identify all relevant literature; (3) evaluation of the overall quality of the studies; (4) data analysis, which includes ordering, coding, categorizing, synthesizing and summarizing the data from the primary sources; and (5) drawing conclusions and presenting findings.

Search Methods

The databases used to retrieve relevant publications were: Cumulative Index to Nursing and Allied Health Literature (CINAHL), PUBMED, SCOPUS, PsychINFO, Nursing Allied and Health Database (ProQuest), and Google Scholar. The following keywords were used in conjunction with Boolean operators to refine the literature search: “health information technology” OR “digital health” OR “technology” OR “eHealth” OR “computers” OR “telemedicine” OR “telehealth” OR “telehomecare” OR “electronic documentation systems” OR “hybrid documentation systems” AND “public health” AND “home care” AND “acute care” AND “long-term care” AND “community” AND “intention” OR “behavioural intention” AND “registered nurses” OR “nurses” OR “nursing”.

Research articles were included in the review if the following selection criteria were met: (a) the sample represented nurses; (b) peer-reviewed articles reported on the association between factors and nurses’ intention to use HIT in practice, that is the provision of direct patient care through activities and interventions with and/or on behalf of patients; (c) study reports written in English; and (d) studies published between 2008 and April 2018. The search strategies were limited to the past 10 years to ensure information obtained on the technologies examined in the selected studies is relevant to what is currently being used in practice.

Quality Appraisal

Evaluation of primary sources in integrative literature reviews is complex and is to be conducted in a meaningful way(Whittemore & Knafl, 2005). In this integrative literature review, the quality of the research studies that met the selection criteria was assessed. Wittemore and Knafl (2005) suggested for primary sources of similar research designs, researchers can calculate quality scores and either include the scores into the design (i.e., inclusion/exclusion criteria) or the analysis. All studies that met the selection criteria used quantitative research methods and were assessed using a validated data evaluation tool, the Checklist for Assessing the Validity of Descriptive / Correlational Studies from the Joanna Briggs Institute (Pearson, 2014). A final score was computed as the sum of the scores assigned to the criteria and studies received a score representing: low quality (0-3), average quality (4-6) and high quality (7-10).

Data Abstraction

Data were extracted on the study characteristics and factors that influence nurses’ intention to use HIT. The following information on the study characteristics was extracted: (a) author’s last name(s) and year published; (b) country in which the study was conducted; (c) research design; (d) health care sector; (e) type of technology; and (f) sample characteristics. Specific to the factors that influence nurses’ intention to use HIT, data were extracted on: (a) type of conceptual framework used (if applicable); (b) factors identified to influence nurses’ intention to use HIT in practice; and (c) reported association of the factors with nurses’ intention to use HIT as indicated in the study results.

Data analysis consisted of data reduction, data display, data comparison, drawing conclusions and verification (Whittemore & Knafl, 2005). Specific to data reduction, an initial subgroup classification process was developed based on the study characteristics (i.e., sector, type of technology, and sample characteristics) and factors that influence nurses’ intention to use HIT in practice (i.e., use of conceptual/theoretical framework, identified factors, and reported associations). This was followed by the extraction of the information from the primary sources, which allowed for sufficient organization of the information for data comparison. Specific to data display, the extracted data from each individual article was converted into a chart to enhance the visualization of patterns within and across the data sources (Whittemore & Knafl, 2005). For data comparison, an iterative process of data examination was employed in which patterns and associations were identified. For drawing conclusions and verification, the first author reviewed and cross-checked the extracted data to ensure consistency with the original articles and emergent patterns and all authors reviewed findings for agreement.

Results

Search Outcomes

The literature search yielded 446 titles and abstracts. Of these, 57 were excluded because they were duplicates. Of the remaining 389 articles, 369 were excluded because they did not meet the selection criteria. To ensure comprehensiveness of the literature search, a hand search of the reference lists in the selected articles was conducted; yielding an additional two articles for full review. A total of 22 studies were included in this review (Table 1). 

The majority of the studies (n=19, 86%) were rated as high quality (Asua, Orruno, Reviriego, & Gagnon, 2012; Bennani & Oumlil, 2014; Chung, Ho, & Wen, 2016; Gagnon et al., 2012; Hung et al., 2014; Ifinedo, 2012; Ketikidis, Dimitrovski, Lazarus, & Bath, 2012; Kowitlawakul, 2011; Kummer, Schafer, & Todorova, 2013; Lau, 2011; Leblanc, Gagnon, & Sanderson, 2012; Lee, Lin, Yan, & Tsou, 2013; Lin et al., 2016; Kim, Lee & Yoo, 2015; Phichitchaisopa & Naenna, 2013; Sharifian, Askarian, Nematolahi, & Farhadin, 2014; Shoham & Gonen, 2008; Song, Park, & Oh, 2015; Wu, Li, & Fu, 2011) and two (9%) scored as average quality (Wills, El-Gayer, & Bennetti, 2008; Zhang, Cocosila, & Archer, 2010). Although only two out of the 22 studies were found to be of average quality, all studies were included in this review given the limited empirical research exploring and examining factors that influence nurses’ intention to use HIT (Galimany-Masclans, Garrido-Aguilar, Girbay-Garcia, Lluch-Canut, & Fabrellas-Padres, 2011; Lau, 2011). Furthermore, one paper (Strudwick, 2015) (5%) was an integrative literature review and was not scored.

Study Characteristics

A total of 22 studies examining nurses’ intention to use HIT in practice were included in this review. All papers were published between 2008 and 2016. The majority of studies were conducted in an acute care setting (n=18, 82%), whereas two were conducted in the home care setting (n=2, 9%) and one in both acute and home care (n=1, 5%). The studies were conducted in the United States (n=3, 14%), Canada (n=3, 14%), and Taiwan (n=5, 23%); two were conducted in Spain (n=2, 9%) and one in each of China, Morocco, Israel, Iran, Macedonia, Australia, South Korea and Thailand (Table 1). 

All studies used a theoretical framework, which are presented in Table 1. The most commonly used theoretical frameworks noted in this review were: The Unified Theory of Use and Acceptance Technology (UTAUT), modified/extended Technology Acceptance Model (TAM), and TAM. Similar constructs, representing a range of factors that influenced behavioral intention were identified in the theoretical frameworks that guided the studies. The constructs included: performance expectancy(consistent with perceived usefulness), that is, the degree to which the end-user believes the technology will provide benefits in performing certain activities and result in performance gains (Venkatesh et al., 2003); effort expectancy(consistent with perceived ease of use), which denotes the ease associated with the use of the technology; social influence (consistent with social norm), that is, the impact important persons (i.e., supervisors and colleagues) have on the end-users’ use of the technology; and facilitating conditions, that is, the perceived level of support within the organizational and technological infrastructure in using the technology(Venkatesh et al., 2003).

The type of HIT investigated in the studies varied (Table 1) with the most frequent ones being health information systems (i.e., sensor-based medication systems and nursing information system) and electronic documentation systems. Computers, personal digital assistants (PDAs) and web tools (i.e., Blogs, Podcasts, and Wiki pages) were investigated in older publications (i.e., 2008) and electronic documentation and health information systems in most recently published papers (i.e., 2016). The studies’ sample sizes ranged from 52 to 942 (Kim et al., 2015; Wu et al., 2011)and female nurses constituted the majority of participants.

Factors influencing nurses’ intention to use HIT 

The factors reported to affect nurses’ intention to use HIT in practice reflected: (1) characteristics of nurses using technology; (2) characteristics of the health information technology; and (3) characteristics of the organizational environment (Table 2). 

Characteristics of nurses using technology 

A total of five studies (Ifinedo, 2012; Kummer et al., 2013; Lin et al., 2016; Phichitchaisopa & Naenna, 2013; Song et al., 2015) explored the influence of the demographic characteristics of nurses on their intention to use HIT in practice. The findings indicated that age, level of education, professional experience and technology experience were significantly associated (directly or indirectly) with behavioural intention for HIT use. 

Three studies (Kummer et al., 2013; Phichitchaisopa & Naenna, 2013; Song et al., 2015)examined age as a potential factor. The findings indicated that age was significantly associated with effort expectancy, social influence and behavioural intention; such that nurses over 40 years of age reported lower effort expectancy and higher social influence (Ifinedo, 2012). Kummer et al. (2013)found that age had a significant impact on social influence and personal innovativeness; such that social influence increased with age and personal innovativeness decreased with age. This indicated that overall, with age, nurses reported increased resistance to using HIT. Song et al. (2015)on the other hand found that age had a significant negative impact on nurses’ behavioural intention to use HIT within the acute care sector. Overall, inconsistent findings were found regarding the influence of age on nurses’ behavioural intention to use HIT in practice. 

Four studies (Ifinedo, 2012; Kummer et al., 2013; Lin et al., 2016; Shoham & Gonen, 2008) explored level of education, professional experience and technology experience on behavioural intention to use HIT in practice (Table 2). None found a direct association between these factors and nurses’ intention to use HIT in practice. One study found that nurses’ level of education positively moderated the relationship between perceived usefulness and perceived ease of use on attitude and behavioural intention; with the relationship being stronger for nurses with higher levels of education in acute and home care sectors (Ifinedo, 2012). Another study found that professional experience negatively moderated the influence of subjective norm on intention to use in the acute care sector (Kummer et al., 2013). This suggests that with an increase in nurses’ professional experience, the influence of subjective norm on intention to use the HIT was weakened. One study reported that technology-related experience moderated the relationship between intention and actual behavioural usage; such that nurses with more experience had higher intention to use and actually used the HIT in practice (Lin et al., 2016). Further, nurses with previous technology-related experience were found to have a more positive attitude towards the use of the computer in their practice in comparison to nurses with less experience (Shoham & Gonen, 2008). Of the four studies, there was no direct association or influence of level of education, professional and technology-related experience on nurses’ intention to use HIT in practice.

Traits of Nurses

Several studies (Bennani & Oumlil, 2014; Chung et al., 2016; Kummer et al., 2013; Lin et al., 2016; Shoham & Gonen, 2008; Strudwick, 2015; Wu et al., 2011) found nurses’ individual traits to influence their intention to use HIT in practice. The first trait reported as influential was personal innovativeness, that is, an individual’s “psychological state of willingness to take a risk by trying out an innovation” (Wu et al., 2011, p. 58). A total of four studies examined the influence of personal innovativeness. Personal innovativeness was found to have a positive, significant and direct influence on nurses’ behavioural intention (Shoham & Gonen, 2008), perceived ease of use (Strudwick, 2015; Wu et al., 2011), perceived behavioural control (also referred to as facilitating conditions) (Strudwick, 2015), and perceived usefulness

(Kummer et al., 2013) within the acute care sector. The second trait reported as influential to nurses’ intention to use HIT was trust, that is, nurses’ confidence in the quality and reliability of the HIT and in the organizational support. One study(Bennani & Oumlil, 2014)found that trust was a direct and statistically significant predictor of nurses’ intention to use HIT; nurses with more trust towards the HIT had higher intention to use it in their acute care practice. The third trait reported as influential to nurses’ intention to use HIT was self-efficacy, that is, the degree to which nurses feel capable of successfully performing the activity(Chung et al., 2016). In the two studies that examined the influence of self-efficacy, it was found to have a significant, positive and direct influence on nurses’ intention to use computers(Shoham & Gonen, 2008) and patient health records in the acute care sector (Chung et al., 2016). 

An additional nurses’ characteristic noted to influence their intention to use HIT was attitude, that is, a nurse’s favourable perception towards the HIT (Table 2). All nine studies (Chung et al., 2016; Hung et al., 2014; Kim et al., 2015; Kowitlawakul, 2011; Lau, 2011; Leblanc et al., 2012; Shoham & Gonen, 2008) and the review(Strudwick, 2015) found nurses with positive and favourable attitudes towards the HIT were more inclined to use it in their acute careand combined acute and home care practice^.

 Characteristics of Technology

A total of 15 studies(Asua et al., 2012; Chung et al., 2016; Gagnon et al., 2012; Kim et al., 2015; Ketikidis et al., 2012; Kowitlawakul, 2011; Kummer et al., 2013; Kuo et al., 2013; Lau, 2011; Leblanc et al., 2012; Lee et al., 2013; Phichitchaisopa & Naenna, 2013; Sharifian et al., 2014; Wills et al., 2008; Wu et al., 2011) and one review (Strudwick, 2015) reported on the characteristics of technology. Performance expectancy and effort expectancy were the identified factors in this category (Table 2). More specifically, performance expectancy and effort expectancy were found to have a direct and statistically significant influence on nurses’ intention to use HIT in acute and home care practice. The relationship indicates that nurses’ perceiving HIT as easy to use and useful for their practice, were more inclined to use it in clinical (acute and home care) practice.

Characteristics of Organizational Environment

A total of 17 studies(Asua et al., 2012; Bennani & Oumlil, 2014; Chung et al., 2016; Hung et al., 2014; Gagnon et al., 2012; Ketikidis et al., 2012; Kim et al., 2015; Kowitlawakul, 2011; Kummer et al., 2013; Kuo et al., 2013; Lee et al., 2013; Phichitchaisopa & Naenna, 2013; Sharifian et al., 2014; Song et al., 2015; Wills et al., 2008; Wu et al., 2011; Zhang et al., 2010) and the one review (Strudwick, 2015)reported on the characteristics of the organization. Social influence and facilitating conditions were the identified factors in this category (Table 2). More specifically, social influence and facilitating conditions were found to directly influence nurses’ intention to use HIT in practice. These relationships indicate that if persons such as colleagues were supportive and encouraged the HIT usage coupled with the organizational (i.e., training and support) and technical (i.e., availability of equipment, resources and IT support) infrastructures being readily available, nurses had higher intention to use the HIT in practice. 

Discussion

Behavioural intention to use HIT by nurses is a complex inter-related technical and social issue that is situated within an organization; making the introduction and uptake of HIT a non-linear and dynamic process that is affected by many factors(Creswell et al., 2013). Three categories of factors were found to influence nurses’ intention to use HIT in practice. The first category was related to the characteristics of nurses using technology. Specific to the demographic characteristics of nurses, inconsistent findings were noted regarding the influence of age. As a result of the inconsistent findings, there is less certainty about the association between age and intention. A potential explanation to this finding is that regardless of age, in general, there has been an increase in both personal and professional technology usage coupled with the increased efforts within post-secondary institutions and healthcare organizations on technology-related educational and training efforts (Canadian Association of Schools of Nursing, 2013; Kaya, 2011). As such, regardless of age, nurses are required to engage in training and update their technology-related knowledge and skills to be able to appropriately use the technology, which has become a standard part of day-to-day clinical nursing practice(Sharit & Czaja, 2017). 

Only one study explored level of education and found that nurses with higher levels of education had higher intention to use HIT in their acute and home care practice. However, it is important to note that further exploration and examination of the influence of level of education on nurses’ intention to use HIT is warranted as inferences cannot be made from one study. Similar findings were noted with nurses’ professional experience. Only one study examined professional experience as a factor that may influence nurses’ intention. The findings indicated that nurses’ professional experience and experience using HIT in the workplace was negatively influenced by the relationship between social influence and intention to use HIT. This implies that nurses with more clinical experience or experience with HIT tend to be more independent and that colleagues, peers and management tend to have less of an influence on their intention to use HIT in practice. Nurses’ technology-related experience was found to moderate the relationship between intention and actual HIT usage; with more experienced nurses having higher intention to use it in their practice. 

Personal innovativeness and self-efficacy were the more commonly examined traits. Personal innovativeness was found to have a positive, direct and significant influence on nurses’ intention to use HIT, their perceived ease of use, their perception of the usefulness of HIT, and their perception about the organizational support for HIT use. This implies that nurses who are more innovative (in comparison to those who are less innovative) are able to work more easily, find the HIT to be useful and have higher intention to use it in their respective practice. Self-efficacy on the other hand was found to have a direct and positive influence on nurses’ intention. This relationship implies that nurses who perceive a higher ability in accomplishing the task are more likely to intend to use the HIT in their practice. Trust was also found to have a direct influence on nurses’ intention to use HIT in practice. However, it is important to note that this was examined in one study, highlighting the need for further examination and exploration of trust as a factor as inferences cannot be made from one study. Nurses’ attitude towards HIT was also found to be a strong direct predictor of intention in this review. This implies that nurses with a more favourable perception towards the HIT have higher intention to use it in their respective practice. 

The available evidence specific to the characteristics of technology showed that performance and effort expectancy had a direct, positive and significant influence on nurses’ intention to use HIT in practice. This relationship implies that nurses who perceive that using the HIT would benefit their job performance and is easy to use, had higher intention to use it in their practice. Similar findings were noted with the characteristics of the organizational environment. Social influence and facilitating conditions had a direct, positive and significant influence on nurses’ intention to use HIT in practice. The relationship implies that nurses who perceived that their colleagues, managers, and patients supported the adoption of the HIT, had higher intention to use the HIT in practice. Further, if nurses believed the necessary organizational (i.e., education and training) and technological infrastructures were readily available to them to support the use of HIT, they had higher intention to use the HIT in their practice. 

It is important to note that the evidence from this integrative literature review was derived predominantly from studies conducted in the acute care sector. A similar finding was noted and reported by Carrington and Tiase (2013). In their nursing informatics literature review, the researchers found that 42.5% of the studies took place in acute care, whereas only 3.7% occurred in community health sector specifically home care(Carrington & Tiase, 2013). In this review, a total of three studies focused on factors that influence nurses’ intention to use HIT within the community health sector, specifically home care. Further, none of the studies focused on other areas within community healthcare sector such as public health, ambulatory care, health centres, schools, and other community-based settings. This finding is a significant limitation of existing empirical evidence because the acute care sector differs from the community health care sector in terms of the working environments; which may influence nurses’ intention to use HIT in practice differently. For example, nurses working in the home care sector practice relatively independently and autonomously with limited direct and face-to-face interaction with colleagues(Tourangeau et al., 2014). In comparison, nurses working in acute care are surrounded by and work in collaboration with other HCPs to provide patient care. Moreover, the working environment of a nurse in the community health sector is unique: it is comprised of several different locations (i.e., patients’ home, nurses’ car and agency office), which may impact the availability of supplies and equipment and IT support(Tourangeau et al., 2014). In contrast, nurses working in the acute care sector have greater proximity to human and material resources(Lundy & Janes, 2014). 

Nursing is considered the most widely used professional resource in the community healthcare sector (Canadian Nurses Association, 2013). The growth in the community health care sector is attributed to the cost-effective alternative to that of acute or long-term care facilitiesand accessibility of technological advancements to support the delivery of efficient and high-quality care and services (Barrett, 2011; Canadian Nurses Association, 2013; Kitchen, Williams, Pong, & Wilson, 2011). The lack of empirical evidence regarding the factors that influence nurses’ intention to use HIT within the community health sector coupled with the rapid growth of technological innovations and growing demand on the community health sector both nationally and internationally highlights the need for further research and knowledge generation in this area(Home Care Ontario, 2016; Qu & Sun, 2015).

Implications

The findings of this review can inform and guide health care leaders and organizations in developing effective and proactive strategies and initiatives that support and encourage the use of HIT by nurses in their day-to-day practice (Hung et al., 2014; Stevenson, Nilsson, Petersson, & Johansson, 2010). This can be done through several initiatives. First, having adequate education and training offered to nurses, whether basic training for those with little to no knowledge and/or experience or advanced training for those with more technology knowledge and/or experience prior to implementation(Stevenson et al., 2010). The educational and training initiatives, which should be readily and continuously provided, may enhance nurses’ technology-related literacy and comfort as well as change prejudicial attitudes towards nurses’ intention and usage of HIT(Kuo et al., 2013; Stevenson et al., 2010). Additionally, through the training initiatives, nurses should have access to practice time with others (i.e., nurses) that are more comfortable and experienced with the technology(Stevenson et al., 2010). This extensive training would address the factors, attitude and facilitating conditions, that were found to have a direct influence on intention.

 Second, having an adequate representation of nurses in the design process of HIT is important. This type of opportunity and inclusion may ensure the technology aligns with nurses’ clinical needs and workflow to support the goal of optimal delivery of patient care services. The design of HIT is often led by non-nursing personnel (i.e., informatics, software and engineering professionals) who have limited knowledge and understanding of the complexity of nursing practice and in turn, how to design the respective HIT from HCP roles and perspectives (While & Dewsbury, 2011). Through such an opportunity, nurses may have more of a positive attitude and in turn, higher intention to use HIT if they are actively involved in the design and implementation process (Stevenson et al., 2010). Such an initiative would address the factors that influence nurses’ individual and technological characteristics.

Third, nurse managers and organizations are encouraged to foster a positive and supportive environment for the integration of HIT as well as be open to feedback from nurses for continued development and maintenance of the respective HIT in practice(Stevenson et al., 2010). These findings suggest that leadership within workplace organizations that value and integrate supportive and ongoing training and technical support for nurses will help to mitigate technology-related challenges that may arise during HIT usage in practice(Gagnon et al., 2012). 

Limitations

The limitation of this review is that the selection criteria were restricted to papers published in the English language. This may have led to the exclusion of papers published in languages other than English. As a result of the exclusion of non-English language papers, it is unknown if the findings from this review apply to nurses working in non-English speaking countries and if similar factors would influence nurses’ intention to use HIT in practice because of the differences in cultural perspectives. 

Conclusion

HIT continues to be widely integrated within health care systems. Nurses play a significant role in the delivery of health care services, which has become enabled and supported by technology. Nurses’ intention to use HIT has significant implications on the successful utilization and implementation of it within practice. This review sought to identify factors reported to influence nurses’ intention to use HIT within practice; which is relevant to the field of digital health. The review found that characteristics of individual, technological and organizational environment influenced nurses’ intention to use HIT in practice. However, the majority of evidence was derived from studies conducted within the acute care sector with little known within the context of public health, community, long-term care and home care; highlighting the importance of further research and exploration as the factors may differ depending on the context in which nurses practice within. Examining and understanding factors that influence nurses’ intention, which is a direct predictor to actual HIT usage, may inform strategies and interventions to support nurses’ use of HIT in their daily practice.

References

Al-Khasawneh, A., & Hijazi, H. (2014). A predictive e-health information system: Diagnosing Diabetes Mellitus using neural network-based decision support system. International Journal of Decision Support System Technology, 6(4): 451-469. doi: 10.4018/ijdsst.2014100103

Asua, J., Orruno, E., Reviriego, E., & Gagnon, M. P. (2012). Healthcare professional acceptance of telemonitoring for chronic care patients in primary care. BMC Medical Informatics and Decision Making,12: 139-149. doi: https://doi.org/10.1186/1472-6947-12-139

Barrett, L. L. (2011). Health at Home 2.0. Full Report. AARP. Retrieved from http://assets.aarp.org/rgcenter/health/healthy-home-11.pdf

Bennani, A. E., & Oumlil, R. (2014). IT acceptance by nurses in Morocco: Application of a modified unified theory of acceptance and use of technology. IBIMA Business Review, (2014), Article ID 849383, 1-10. doi: 10.5171/2014.849383.

Bowles, K., Dykes, P., & Demiris, G. (2015). The use of health information technology to improve care and outcomes for older adults. Research in Gerontological Nursing, 8(1): 5-10. doi: 10.3928/19404921-20121222-01

Canadian Association of Schools of Nursing (CASN). (2013). Nursing informatics teaching toolkit: Supporting the integration of the CASN nursing information competencies into nursing curricula.Retrieved from: http://digitalhealth.casn.ca/content/user_files/2017/12/FINAL-EN_Nursing-Informatics-Teaching-Toolkit.pdf

Canadian Home Care Association. (2015). Technology enabled home care: Supporting independence and improving health outcomes in the home setting. Retrieved from http://www.cdnhomecare.ca/media.php?mid=4442

Canadian Nurses Association (CAN). (2013). Optimizing the role of nursing in home health. Retrieved from: https://cna-aiic.ca/~/media/cna/page-content/pdf-en/optimizing_the_role_of_nursing_in_home_health_e.pdf?la=en

Carrington, J. M., & Tiase, V. L. (2013). Nursing informatics year in review. Nursing Administration Quarterly, 37(2):136–143. doi: 10.1097/NAQ.0b013e3182869deb

Cho, I., Kim, E., Choi, W. H., & Staggers, N. (2016). Comparing Usability Testing Outcomes and Functions of Six Electronic Nursing Record Systems. International Journal of Medical Informatics, 88, 78-85. doi: 10.1016/j.ijmedinf.2016.01.007

Chung, M. H., Ho, C. H., & Wen, H. C. (2016). Predicting intentions of nurses to adopt patient personal health records: A structural equation modeling approach.Computer Methods and Programs in Biomedicine, 136: 45-53. doi:10.1016/j.cmpb.2016.08.004

Creswell, K. M., Bates, D. W., & Sheikh, A. (2013). Ten key considerations for successful implementation and adoption of large-scale health information technology. Journal of American Medical Informatics Association, 20(e1): e9-e13. doi:10.1136/amiajnl-2013-001684

Delucia, P. R., Ott, T., & Palmieri, P. A. (2009). Chapter 1: Performance in Nursing. Reviews of Human Factors and Ergonomics, 5(1):1-40. 

Gagnon, M. P., Orruño, E., Asua, J., Abdeljelil, A. B., Emparanza, J. (2012). Using a modified technology acceptance model to evaluate healthcare professionals’ adoption of a new telemonitoring system. Telemedicine and e-Health, 18: 54-59. doi:10.1089/ tmj.2011.006

Galimany-Masclans, J., Garrido-Aguilar, E., Girbau-Garcia, M. R., Lluch-Canut, T., & Fabrellas-Padres, N. (2011). New technologies and nursing: Use and perception of primary healthcare nurses about electronic health record in Catalonia, Spain. Telemedicine journal and e-health : the official journal of the American Telemedicine Association, 17(8): 635–639. doi: 10.1089/tmj.2011.0008

Health Canada. (2015). Canada Health Infoway. Website. http://www.hc-sc.gc.ca/ahc-asc/performance/estim-previs/plans-prior/2015-2016/supplement/supplement-5-eng.php.

Home Care Ontario. (2016). Bringing Home Care into Ontario’s Technology Strategy. Available from http://www.homecareontario.ca/docs/default-source/position-papers/bringing-home-care-into-ontario’s-technology-strategy.pdf?sfvrsn=10

Hsiao, J. L., & Chen, R. F. (2016). Critical factors influencing physicians’ intention to use computerized clinical practice guidelines: An integrative model of activity theory and the technology acceptance model. BMC Medical Informatics and Decision Making,16(3). doi:10.1186/s12911-016-0241-3

Hung, S. Y., Tsai, J. C. A, & Chuang, C. C. (2014). Investigating primary health care nurses’ intention to use information technology: An empirical study in Taiwan. Journal Decision Support Systems,57: 331-342. doi: 10.1016/j.dss.2013.09.016

Ifinedo P. (2012). Technology acceptance by health professionals in Canada: An analysis with a modified UTAUT model. 45^thHawaii International Conference on System Sciences, 2937-2946. doi: 10.1109/HICSS.2012.556

Institute of Medicine. (2003). The future of the public’s health in the 21^stcentury. Washington, DC. The National Academies Press. Available from: https://doi.org/10.17.17226/10548

Kaya N. (2011). Factors affecting nurses’ attitudes toward computers in healthcare. CIN: Computers, Informatics, Nursing, 29(2): 121-129. doi: 10.1097/NCN.0b013e3181f9dd0f

Ketikidis, P., Dimitrovski, T., Lazarus, L., & Bath, P. A. (2012). Acceptance of health information technology in health professionals: An application of the revised technology acceptance model. Health Informatics Journal, 18(2): 124-134. doi: 10.1177/1460458211435425

Kim, S., Lee, K. H., & Yoo, S. (2015). Analysis of the factors influencing healthcare professionals’ adoption of mobile electronic medical record (EMR) using the unified theory of acceptance and use of technology (UTAUT) in a tertiary hospital. BMC Medical Informatics and Decision Making, 16, 1–12. doi: 10.1186/s12911-016-0249-8

Kitchen, P., Williams, A., Pong, R. W., & Wilson, D. (2011). Socio-spatial patterns of home care use in Ontario, Canada: A case study. Health and Place, 17,195-206. Retrieved from http://socserv.mcmaster.ca/rdc/RDCwp40.pdf

Kowitlawakul, Y. (2011). The technology acceptance model: Predicting nurses’ intention to use telemedicine technology (eICU). CIN: Computers, Informatics, Nursing, 29(7): 411-418.  doi: 10.1097/NCN.0b013e3181f9dd4a

Kummer, T. F., Schafer, K., & Todorova, N. (2013). Acceptance of hospital nurses toward sensory-based medication systems: A questionnaire survey. International Journal of Nursing Studies,50: 508-517. doi: 10.1016/j.ijnurstu.2012.11.010

Kuo, K. M., Liu, C. F., & Ma, C. C. (2013). An investigation of the effect of nurses’ technology readiness on the acceptance of mobile electronic medical record systems. BMC Medical Informatics and Decision Making, 13(88), 1-14. doi: 10.1186/1472-6947-13-88

Lau, A. S. M. (2011). Hospital-based nurses’ perceptions of the adoption of Web 2.0 tools for knowledge, sharing, learning, social interaction and the production of collective intelligence. Journal of Medical Internet Research, 13(4): e92-e104. doi: 10.2196/jmir.1398.

Leblanc, G., Gagnon, M. P., & Sanderson, D. (2012). Determinants of primary case nurses’ intention to adopt an electronic health record in their clinical practice. CIN: Computers, Informatics, Nursing, 30(9): 496-502. doi:10.1097/NXN.0b013e318257db17

Lee, C. C., Lin, S. P., Yan, S. L., & Tsou, M. Y. (2013). Evaluating the influence of perceived organizational learning capability on user acceptance of information technology among operating room nurse staff. Acta Anaesthesiologica Taiwanica, 51:22–27.doi: 10.1016/j.aat.2013.03.013

Lin, I. C., Lin, C., Hsu, C. L., Roan, J., Yeh, J. S., & Cheng, Y. H. (2016). The usage behaviour and intention stability of nurses: An empirical study of a nursing information system. The Journal of Nursing Research, 24(1): 48-57. doi: 10.1097/jnr.0000000000000103

Lundy, L. & Janes, S. (2014). Community Health Nursing: Caring for the Public’s Health. (3^rd ed.). Jones & Bartlett Learning.

Pearson A. (2014). Joanna Briggs Institute Reviewer’s Manual. Available from: https://joannabriggs.org/assets/docs/sumari/ReviewersManual-2014.pdf

Phichitchaisopa, N., & Naenna, T. (2013). Factors affecting the adoption of healthcare information technology. Experimental and Clinical Sciences Journal, 12: 413-436. 

Qu Z, & Sun J. (2015). Understanding health information technology adoption: A synthesis of literature from an activity perspective. Information Systems Frontiers, 17(5): 1177-1190.

Rouleau, G., Gagnon, M. P., Cote, J., Payne-Gagnon, P., Hudson, E., & Dubois, C. A. (2017). Impact of information and communication technologies on nursing care: Results of an overview of systematic review. Journal of Medical Internet Research,25, 19(4), e122. doi: 10.2196/jmir.6686.

Sharifian, R., Askarian, F., Nematolahi, M., & Farhadin, P. (2014). Factors influencing nurses’ acceptance of hospital information systems in Iran: Application of the Unified Theory of Acceptance and Use of Technology. Health Information Management Journal, 43(3):23-8.

Sharit, J., & Czaja, S. J. (2017). Technology and work: Implications for older workers and organizations. Innovation in Aging, 1(1), 1026. doi: https://doi.org/10.1093/geroni/igx004.3735

Shoham, S., & Gonen, A. (2008). Intentions of hospital nurses to work with computers: Based on theory of planned behaviour. CIN: Computers, Informatics, Nursing, 26(2):106-116. doi: 10.1097/01.NCN.0000304777

Song, L., Park, B., & Oh, K. M. (2015). Analysis of the Technology Acceptance Model in Examining Hospital Nurses’ Behavioural Intentions Toward the Use of Bar Code Medication Administration. Computers, Informatics, Nursing, 33(4):157-165.doi: 10.1097/CIN.0000000000000143

Stevenson, J. E., Nilsson, G. C., Petersson, G. I., & Johansson, P. E. (2010). Nurses’ experience of using electronic patient records in everyday practice in acute/inpatient ward settings: A literature review. Health Informatics Journal, 16(1), 63-72. doi: 10.1177/1460458209345901

Strudwick G. (2015). Predicting nurses’ use of healthcare technology using the technology acceptance model: An integrative review. CIN: Computers, Informatics, Nursing,33(5): 189-198. doi: 10.1097/CIN.0000000000000142

Tourangeau, A., Patterson, E., Rowe, A., Sari, M., Thomson, H., Macdonald, G., Cranley, L., & squires, M. (2014). Factors influencing home care nurse intention to remain employed. Journal of Nursing Management, 22(8), 1015-1026. doi: 1111/jonm.12104. 

Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly,27(3), 425-478.doi: 10.2307/30036540

While A, & Dewsbury G. (20110. Nursing and information and communication technology (ICT): A discussion of trends and future directions. International Journal of Nursing Studies,48(10):1302-1310. doi: 10.1016/j.ijnurstu.2011.02.020

Whittemore, R., & Knafl, K. (2005). The integrative review: Updated methodology. Journal of Advanced Nursing, 52(5): 546-553. doi:10.1111/j.1365-2648.2005.03621.x

Wills, M. J., El-Gayer, O. F., & Bennetti, D. (2008). Examining healthcare professionals’ acceptance of electronic medical records using UTAUT. Issues in Information Systems, 2: 396-401. 

Wu, I. L., Li, J. Y., & Fu, C. Y. (2011). The adoption of mobile healthcare by hospital’s professionals: An integrative perspective. Decision Support Systems, 51: 587-596. Doi: https://doi.org/10.1016/j.dss.2011.03.003

Zhang, H., Cocosila, M., & Archer, N. (2010). Factors of adoption of mobile information technology by homecare nurses. CIN: Computers, Informatics, Nursing, 28(1): 49-56. doi: 10.1097/NCN.0b013e3181c0474a

Biographical Statements

Ms. Sarah Ibrahim

Sarah Ibrahim, RN, MN is a Doctoral Candidate in the Arthur Labatt Family School of Nursing at Western University. Ms. Ibrahim’s area of interest in in health information technology, home care, development, implementation and evaluation of health interventions. 

Dr. Lorie Donelle

Dr. Donelle, RN, PhD is an Associate Professor and Research Chair Arthur Labatt Family School of Nursing at Western University. Dr. Donelle’s area of expertise is in digital health and specifically in health information technology use among health care providers and patients, (digital) health literacy and health promotion. 

Dr. Sandra Regan

Dr. Regan, PhD is an Adjunct Associate Professor in the Arthur Labatt Family School of Nursing at Western University and Deputy Registrar, Education Program Review at the British Columbia College of Nursing Professionals. Dr. Regan’s areas of expertise are in health services research, policy development and analysis, and successful transition and retention in new graduate nurses. 

 Dr. Souraya Sidani

Dr. Sidani, PhD is a Professor at Ryerson’s Daphne Cockwell School of Nursing and Canada Research Chair in Design and Evaluation of Health Interventions.Dr. Sidani’s areas of expertise are in quantitative research methods, intervention design and evaluation, and measurement.