Canadian Journal of Nursing Informatics

Nova Scotia Nurses’ Acceptance of Healthcare Information Systems: Focus on Technology Characteristics and Related Factors

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By Princely Ifinedo, PhD,

Odette Griscti, RN PhD,

Judy Bailey RN MN

and Sheila Profit RN MAdEd

Abstract

 Nurses’ Acceptance of Healthcare Information SystemsThis study used the technology acceptance model (TAM), which was expanded to include relevant constructs including computer self-efficacy, computer habit, and accessibility to computers/IS during nursing education, to investigate registered nurses’ (RNs’) acceptance of healthcare information systems (HIS) in Nova Scotia, Canada. Using a cross-sectional survey, we collected usable data from 197 RNs in the province. The partial least squares (PLS) path modeling technique was used to analyze the data. The results indicate that RNs’ perceived usefulness, favorable computer habits, and behavioral intentions to use HIS have positive effects on their acceptance of such technologies. Contrary to prediction, perceived ease of use, computer self-efficacy, and accessibility to computers/IS during nursing education did not have positive effects on nurses’ acceptance of HIS in the research setting. The study’s implications for research were noted. Healthcare professionals and administrators in the research setting and comparable parts of Canada and elsewhere can benefit from our study’s conceptualizations and insights.

 Introduction

 Across the world, hospitals, clinics, and related healthcare facilities use healthcare information systems (HIS) to make critical decisions (Buntin et al., 2011; Villalba-Mora et al., 2015). HIS encompasses computing hardware and software used for capturing, processing, storing, retrieving, and presenting healthcare information (Tung, Chang & Chou, 2008; Buntin et al., 2011). In general, HIS tend to improve patient safety, increase patient satisfaction, and enhance organizational efficiency by reducing costs, and improving general quality and standards in healthcare (Scott, 2007; Tung et al., 2008; Villalba-Mora et al., 2015). Against the backdrop of benefits accruing from HIS utilization in healthcare facilities, it is somewhat surprising that practitioners’ and academicians’ reports and studies have pointed to the underutilization of HIS and related technologies by healthcare professionals, including nurses (Timmons, 2003; Lee, 2006; Gonen et al., 2014; Ifinedo, 2016). Suffice it to say that healthcare facilities that do not use HIS efficiently will lose the trust of their patients (Tung et al., 2008) and be deemed not fit for practice.

Healthcare researchers in Canada (such as Zhang, Cocosila & Archer, 2010; Ifinedo, 2012; Leblanc, Gagnon & & Sanderson, 2012) and other parts of the world (including Timmons, 2003; Lee, 2006; Vanneste, Vermeulen & Declercq, 2013; Gonen et al., 2014; Maillet, Mathieu. & Sicotte, 2015) have investigated nurses’ acceptance of HIS and related technologies. However, research on nurses’ perceptions of HIS use behaviour or acceptance in our study’s location – Nova Scotia – is rare. Our study is motivated, in part, by a lack of knowledge of factors impacting HIS use among healthcare professionals in the province.

The government of Nova Scotia implemented a HIS, called NSHIS, at a cost of over $55.7 million (NSHIS, 2015). Examples of sub-systems in the acquired tool include electronic medical records (EMR), electronic health records (EHR), clinical decision support systems (CDSS), and electronic patient records (EPR). No prior empirical information exists regarding acceptance of the system by healthcare professionals in the province. Our present study aims to shed light on this issue. We also hope the body of work on healthcare professionals’ acceptance of technologies will benefit from information provided by our endeavor which seeks to present perspectives from a region of Canada that has not received adequate attention in the area.  For illustrative purposes, this study  focused attention on registered nurses (RN) because this group of healthcare workers have been known to display a certain level of reluctance with respect to accepting HIS and related technologies (Timmons, 2003; Lee, 2006; Kaya, 2011).

The technology acceptance model (TAM) (Davis 1989) was used as the background theoretical framework for our study. TAM is a prominent framework used by healthcare researchers to study the acceptance of technologies by clinicians (Holden & Karsh, 2010; Maillet et al., 2015). TAM offers information related to the nature of technology characteristics, i.e., the ease of use and advantages of the acquired system, as well as other attitudinal perceptions. We complemented TAM by infusing it with such factors as computer habit, computer self-efficacy, and accessibility to computers/IS during nursing education to deepen insight. Past studies indicated that the aforementioned factors matter in the acceptance of technological innovations among clinicians (Vincent et al., 2007; Aggelidis & Chatzoglou, 2009; Kaya, 2011; Vanneste et al., 2013).

Using the core components of TAM and incorporated factors, we sought to answer the following question: What are the effects of technology characteristics and related factors on Nova Scotia nurses’ acceptance of HIS? We hope our study’s results and conclusions will benefit practitioners, hospital administrators, and policy makers in the research location and comparable parts of the country.

Theoretical foundations and constructs 

TAM is generally considered useful for understanding users’ acceptance of technologies. TAM is not a perfect theoretical framework; nonetheless, many have used it for its simplicity and ease of replication (Yarbrough & Smith 2007; Holden & Karsh 2010). In fact, several researchers in the healthcare sector have based their studies on TAM (Yarbrough & Smith 2007; Holden & Karsh 2010; Maillet et al., 2015). TAM’s constructs explain about 30-40% of the variance in individuals’ acceptance of IS (Yarbrough & Smith 2007; Holden & Karsh 2010).

TAM suggests that users’ perceived usefulness (PUSS) and perceived ease of use (PEOU) are two major determinants of information systems’ (IS) acceptance. Perceived usefulness (PUSS) refers to “the degree to which a person believes that using a particular system would enhance his or her job performance” (Davis, 1989, p. 320). Perceived ease of use (PEOU) refers to “the degree to which a person believes that using a particular system would be free from effort” (Davis, 1989, p. 320). Behavioral intention (BEHI) refers to the willingness to use the system.  It is considered the most proximal antecedent to HIS use (HISU), which in our study refers to the frequency of use of HIS in work environments (Davis, 1989).

Habit, as proposed in Triandis’ theory of interpersonal behavior (Triandis, 1979), refers to behavior that has become automatic requiring only minimal mental effort to accomplish. In the same light, computer habit (CHAB) refers to a computer behavior that has become automatic. Self-efficacy is a major component of Bandura’s social cognitive theory (Bandura, 1977). Accordingly, computer self-efficacy (CSEF) is a socio-cognitive factor that refers to strength of belief in one’s own ability to complete tasks using computers (Compeau, Higgins & Huff, 1999; Vanneste et al., 2013). Accessibility to computers/IS during nursing education (ACNE) refers to the accessibility of computing/IS knowledge during one’s nursing program; this variable was based on insights from past work (Burkes, 1991; Kaya, 2011).

Research model and hypotheses

Our research model is shown in Figure 1. It shows the effects of each of the five independent factors on BEHI, which in turn impacts HISU, the dependent construct. Discussions about the formulation of the research hypotheses are provided next.

Figure 1. The proposed research model

Figure 1. The proposed research model

Nurses would be more willing to use HIS if they appreciated the tangible benefits or advantages of such technologies. Past studies provided evidence to support the foregoing claim. Indeed, previous research showed PUSS as a strong motivator of healthcare professionals’ intentions to use technological innovations in healthcare settings (Aggelidis & Chatzoglou 2009; Holden & Karsh 2010; Kuo, Liu & Ma. 2013; Ketikidis et al. 2012). Hence, we predict that:

H1: Perceived usefulness of HIS will have a positive effect on behavioral intention to use HIS.

It is logical to expect that nurses who perceive HIS to be complex tools will have less than favorable views of such systems; conversely, those who believe HIS are easy systems will develop favorable intentions toward such healthcare applications. Results from both past (Yarbrough & Smith 2007; Holden & Karsh 2010; Ifinedo, 2012, 2016)) and recent (Kuo et al., 2013; Maillet et al. 2015) studies that used TAM to examine nurses’ acceptance of healthcare technologies provided strong support to this viewpoint. Hence, we predict that:

H2: Perceived ease of use of HIS will have a positive effect on behavioral intention to use HIS.

Nurses who believe in their own ability to complete tasks using computers will most likely have positive intentions to use HIS in work environments. Previous healthcare researchers demonstrated that clinicians with relevant computing self-efficacy have intentions to use technological innovations in work environments (Shoham & Gonen, 2008; Vanneste et al., 2013; Chiu & Tsai 2014). Hence, we predict that:

H3: Computer self-efficacy will have a positive effect on behavioral intention to use HIS.

We argue that nurses who have computer use behaviors that have become automatic would be able to utilize implemented HIS. Past studies suggested that nurses’ behavioral intention to use technological innovations is positively linked to their computing resources use habits (Vincent et al., 2007). Hence, we predict that:

H4: Computer habit will have a positive effect on behavioral intention to use HIS.

Nurses who received generic computer education during training believed that such exposure and access were pertinent to their current job needs vis-à-vis technology use (Eley et al., 2008; Ifinedo, 2016). Earlier studies (Burkes, 1991; Sinclair & Gardner, 1999; Kaya, 2011) reported a significant relationship between nurses’ exposure to computers and IS during their training and subsequent use of healthcare-related IS at work.  Hence, we predict that:

H5: Accessibility to computers/IS during nursing education will have a positive effect on behavioral intention to use HIS.

It has been demonstrated that behavioral intention is a reliable predictor of actual use of healthcare technologies (Holden & Karsh 2010). As actual usage was not employed in this study, we used nurses’ self-reported usage of IS instead. Prior healthcare studies (e.g., Ketikidis et al. 2012; Ifinedo, 2015) have shown that behavioral intention to use IS and usage are positively associated. Hence, we predict that:

H6: Behavioral intention to use IS will have a positive effect on self-reported HIS use.

Research methods

Procedure and sample

We collected data from members of the College of Registered Nurses of Nova Scotia, Canada, (http://www.crnns.ca) in a cross-sectional survey. After obtaining a list of 500 members of the association, each member was sent a packet containing a cover letter explaining the purpose of the survey, a questionnaire, and self-addressed, stamped envelope. We described HIS to participants as technologies capable of capturing, storing, retrieving, processing, and producing output (i.e. healthcare-related information) for clinicians with examples such as EHR, EMR, and CDSS. Several participants indicated familiarity with such systems. In addition, participants were encouraged to think of similar systems in use at their workplaces as they filled in the questionnaire.

We obtained usable data from 197 nurses for an effective response rate of 40.4%. Most of the participants (70%) had university education. 96% of the participants were female, which is an indication of the characteristics of RNs in Canada (Canadian Institute of Health Information, 2015). On average, participants have worked for 14.5 years with their current employers (S.D. = 10.8). Table 1 shows participants’ complete demographic information.

Table 1. Demographic profile of the sample (N =197).

Table 1. Demographic profile of the sample (N =197).

The instrument

Scales previously validated in the literature were used for our study. For PUSS, PEOU, and BEHI we adapted measuring items from Davis (1989) and Venkatesh at al. (2003). Items used to represent computer habit (CHAB) came from Limayem and Cheung (2008). Items used to operationalize CSEF were adapted from Compeau et al. (1999). Self-reported HIS use (HISU) was adapted from Venkatesh at al. (2003). All measurement items were anchored on a 7-point Likert scale ranging from “strongly disagree” (1) to “strongly agree” (7) in which participants were asked to indicate an appropriate response. The full list of measuring items for the constructs and their descriptive statistics is provided in Table 2. For accessibility to computers/IS during one’s nursing education (ACNE), participants were asked the question: “How accessible were computers/IS to you during your nursing education program? Participants indicated a choice on a scale ranging from “Not accessible, at all” (1) to “Very accessible” (5).

Table 2. The questionnaire’s items and their descriptive statistics

Table 2. The questionnaire’s items and their descriptive statistics

Data analysis

The partial least squares (PLS) path modeling technique was used for data analysis. Other healthcare researchers have employed the technique in comparable studies (e.g., Vanneste et al., 2013). WarpPLS 5.0 (Koch, 2015) is the PLS software used in this study. PLS is suitable for our study because it places minimal demands on sample size and residual distributions, and allows the use of observed items to represent a construct (Hair, Ringle & Sarstedt, 2011). PLS tests the reliability and validity of the measures. Reliability of constructs with values of 0.7 or more are usually considered acceptable (Fornell & Larcker, 1981). Results in Table 3 show acceptable values for the study’s constructs.

Regarding validity of the constructs, each measurement item is expected to load highly on its latent construct and standardized item loadings exceeding 0.707 are considered adequate (Fornell & Larcker, 1981; Ifinedo, 2007). Our results in this aspect are acceptable (excluded due to space consideration, but are available upon request). The constructs are expected to be distinct (Hair et al., 2011). A minimum value of 0.5 average variance extracted (AVE) is preferred (Fornell & Larcker, 1981; Hair et al., 2011), and the square root of AVE should be larger than the correlations between that construct and all other constructs in the model. Table 3 shows the constructs’ psychometric properties are adequate.

Table 3. Composite Reliability, Cronbach Alphas, AVEs, and inter-construct correlations

Table 3. Composite Reliability, Cronbach Alphas, AVEs, and inter-construct correlations

With reliability and discriminant validity of the study constructs’ established, the structural model was then tested. The structural model indicated the significance of hypothesized relationships using the path significance (p), beta (?) coefficients, and coefficient of determination (R2), which is the amount of variance explained by the indicators. Figure 2 shows the results of PLS analysis. The independent variables explained 30% of the variance in BEHI, which further explained 32% of the variance in HISU.

Figure 2. Results for the proposed research model

Figure 2. Results for the proposed research model

Discussion

The objective of our study was to examine Nova Scotia nurses’ acceptance of healthcare information systems. Our focus was on technology characteristics, i.e., the ease of use and advantages of the acquired system, as well as other relevant computer related attitudes. TAM, was used as the base framework of our study, thus was further reinforced as a useful model for predicting clinicians’ acceptance of technologies. Namely, the amount of variance explained by the selected factors in our research conceptualization is about 31%, which compares to results in similar studies (Yarbrough & Smith 2007; Holden & Karsh 2010).

Our data supported 3 out of 6 formulated hypotheses. The unsupported hypotheses will be discussed first. The sampled nurses’ perceived ease of use did not have a positive effect on their behavioral intention to use HIS. Likewise, computer self-efficacy did not have a positive effect on behavioral intention to use HIS. As well, the construct related to nurses’ accessibility to computers/IS during their nursing education did not have a positive relationship with their intentions to use HIS at their current places of work.

With respect to the positive relationship between PEOU and favorable use of technologies by healthcare professionals, our result is analogous to observations reported by other researchers (e.g., Ketikidis et al. 2012). Plausible explanations for the other unsupported hypotheses might be due to extraneous factors. For example, the sampled nurses may not believe that the HIS they use at work are easy to use. By the same token, the nurses in our study may not believe that they have sufficient computing efficacy or competencies. Feedback received from respondents addresses these concerns:

Some nurses who have returned from [working] in the US tell us that better technologies are being used there. Perhaps due to my age and exposure to computers at the later part of my nursing career, I have negative opinions of computers [and of our HIS at work].” (Staff nurse, aged between 51 and 60 years).

The organization supports use of [HIS], but does not invest in educating staff. I feel there is a desperate need to move forward in giving staff the tools they need to best perform at work. More advanced computer training and knowledge is urgently needed.” (Nurse/Project Lead, aged between 51 and 60 years).

Regarding the supported hypotheses, our data results indicated that nurses’ perceptions of perceived usefulness of HIS have positive effect on their intentions to use such technologies at work. This result is in line with the views of others in the area (Aggelidis & Chatzoglou 2009; Holden & Karsh 2010; Kuo et al. 2013; Chiu & Tsai 2014; Ifinedo, 2015, 2016). Nursing professionals with favorable computer habits tend to be more willing to use IS at work; our result is in agreement with those espoused by Vincent et al. (2007). We found that nurses’ willingness to use HIS is positively linked to their acceptance of HIS. Our result supports those reported by others (e.g., Ketikidis et al., 2012).

The implications of our study’s results for practitioners and healthcare administrators in Nova Scotia and comparable parts of Canada (and the world) are highlighted as follows. Communicating the advantages of HIS to nurses is an exercise worth undertaking. Intention to use HIS benefits from nurses’ appreciation of the usefulness of such systems as our results demonstrated. There is a need to procure and develop systems that are not considered difficult to use; complex systems may not promote acceptance (Davis, 1989). Advanced computer training and the acquisition of relevant IS knowledge should be given to nurses. When advanced computer training is provided, nurses’ computing competencies and confidence will increase to positively impact their attitudes toward IS use at work (Eley et al., 2008; Pilarski, 2010; Gonen et al., 2014). Comments provided by the study’s’ participants underscored this fact.

Measures that can strengthen computer use habits should be encouraged. For example, providing incentives (e.g., rewards) should be exploited to encourage the development of acceptable IS use habit. Evidence indicates that nursing education in the past did not leverage IS skills for nurses (Eley et al., 2008; Pilarski, 2010; Gonen et al., 2014). Nursing education and curriculum should find ways to expand the provision of relevant IS skills and expertise (nursing informatics) to future nurses. A recent study echoed a similar view in Canada (Nagle et al., 2014). Such training, many have recognized, will provide a strong foundation in stimulating nurses’ use and acceptance of modern IS tools in healthcare (Eley et al., 2008; Pilarski, 2010; Nagle et al., 2014).

Limitations

Our study has its share of limitations; the major ones are noted as follows. First, we used respondents’ self-reported usage of HIS for analysis; we cannot rule out bias related to socially desirable responses. Second, our data came from just one region of Canada (i.e., Nova Scotia). It is difficult to claim that our results can be generalized to all parts of Canada. Third, our study did not focus on any specific HIS. It is likely that factors affecting the use of disparate HIS, such as MNIS, CDSS, and EPR, may differ. Future studies can expand our research endeavor by overcoming the noted limitations of this current work.

Conclusion

Based on TAM, we designed a study to examine the effects of selected technological factors impacting Nova Scotia nurses’ acceptance of HIS. Our results showed that perceived usefulness, favorable computer habist, and behavioral intention to use HIS are main predictors of acceptance of HIS in the study’s location. Our data did not provide support for the pertinence of perceived ease of use of HIS, computer self-efficacy, and nurses’ accessibility to computers/IS during their nursing education as key determinants of HIS acceptance. This study adds to the growing body of knowledge regarding clinicians’ use of HIS with perspectives from Nova Scotia.

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ABOUT THE AUTHORS

 

Princely Ifinedo (Principal Author)

Cape Breton University

princely_ifinedo@cbu.ca

Princely Ifinedo is an Associate Professor in the Shannon School of Business at Cape Breton University, Canada. He holds a PhD in Information Systems (IS) from University of Jyväskylä, Finland and MBA from the Royal Holloway, University of London, UK. His research includes IS adoption in healthcare and human-computer interaction.

 

Odette Griscti

Cape Breton University

Odette Griscti is an Associate Professor in the Nursing Department. Odette received her BSc (Hons) degree from the University of Malta, her MSc in Nurse Education at the University of Malta, and her PhD at Dalhousie University. Odette’s research/areas of interest include: feminist post-structural studies, Foucauldian Research and Nurse: Patient Power Relationships

Judy Bailey

Cape Breton University

Judy Bailey is an Associate Professor of Nursing. She received her BScN from St. F.X. and her MN from Memorial University. Professor Bailey’s areas of interest are: Virtual Learning, Nursing Student Attrition, IPODS for evidenced-based practice and client education and Sustainable Happiness for Health Promotion and Well Being.

Sheila Profit
Cape Breton University

Sheila Profit is an Associate Professor of Nursing. She received both her BScN and MAdEd from St. Francis Xavier University, and is a Registered Nurse. Her areas of interest include: Resiliency, Health Promotion, International Health, Community Health and Mental Health.

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