JMIR Publications

ABSTRACT

Background:

Electronic health records (EHRs) represent an important aspect of digital healthcare and in order to promote their use further, we need to better understand the drivers of their acceptance among healthcare professionals. Technology acceptance theories can be utilized to better understand users’ intentions towards using EHRs. This study used a modified version of the Unified theory of acceptance and use of technology (UTAUT) to examine the factors that are associated with intentions to use an EHR application among General practitioners (GPs) in the then Republic of Macedonia, a country that has been underrepresented in the extant literature. An online questionnaire was sent to 1174 GPs, of whom 458 completed the questionnaires (response rate = 40.2%). The questionnaire assessed performance expectancy, effort expectancy, facilitating conditions, and social influence in relation to the GPs’ intentions to use future EHR system. Job relevance, descriptive norm, satisfaction with currently used e-Health systems in the country, and other technology/internet use were also measured. Hierarchical linear regression analysis showed that effort expectancy, social influence, facilitating conditions, and job relevance, but not performance expectancy, were significantly associated with intentions to use the EHR. Multiple mediation modelling analyses further showed that social influence (z = 2.64, P = .001), facilitating conditions (z = 4.54, P = .001), descriptive norm (z = 4.91, P = .001), and effort expectancy (z = 5.81, P = .008) mediated the association between job relevance and usage intentions. Finally, moderated regression analysis showed that the association between social influence and usage intentions was significantly moderated (P = .022) by experience (Bexperience×social influence = .005, 95%CI = .001 - .010, β = .080). Additionally, the association between social influence and intentions was significantly moderated (P = .029) by age (Bage×social influence = .005, 95%CI = .001-.010, β = .077). The theoretical and practical implications of the findings are discussed.

Objective:

The main objective of this research was to assess the readiness of GPs in the country for future acceptance of EHR system. Another objectives were: to address the role of the basic predictors of the original UTAUT model on EHR use; to assess the effect of other technology acceptance predictors such as job relevance, descriptive norm, and satisfaction (with existing health ICT systems already implemented in the country); and to identify the moderation effect of basic moderation variables such as age, gender, and previous work experience.

Methods:

Recruitment The target population was the GPs in the then Republic of Macedonia: all GPs who had contracts with the national health insurance fund were identified to be included in the study. Participants’ email addresses were provided from the national health insurance fund list. According to the list there were 1631 active GPs in the country at the time of the study, with 1174 active e-mail addresses of GPs registered in the list. An online web survey was created on the SharePointTM platform and an invitation email was sent to all email addresses. General information on the future EHR system was included in a short introduction to the survey. The email was sent on 1st July, 2014, followed by two reminder emails on 15th July, and 1st August. However, 35 emails were returned as they did not reach valid email addresses. Research instrument The original UTAUT model was modified with other technology acceptance extensions for the purpose of this research. The following technology acceptance items were added to the questionnaire: job relevance 11; descriptive norm i.e. estimated prevalence of EHR use by colleagues in the future 21, 22; current use of other technology for professional or leisure purposes 23; and satisfaction with existing e-Health systems that are used in country currently. Job relevance was added to the current research model as its effectiveness was established in previous study conducted by researchers 22. A single "user satisfaction" item, was developed to assess the GPs’ satisfaction with the currently-used ICT systems in healthcare in the country (the “Health Smart Card” system, and the “My term system”). The purpose of including this item was to assess the association of user satisfaction with existing healthcare ICT systems with intentions to use the future EHR. Performance expectancy 8, 15 was measured using five questions assessing aspects of participants’ beliefs on usefulness of future EHR system. Effort expectancy 8, 11, 12, 15 was measured using eight items assessing aspects of the ease of use of the future EHR system. Facilitating conditions 8 were measured through four items assessing the degree to which the participant believed that organizational infrastructure would support her/his use of the future EHR system. Social influence 8, 12, 15 was measured through three items assessing how the participant perceived what important others believed as to whether he or she should use the future EHR system. Descriptive norm 21 variable was measured with a single item, asking participants to estimate how many of their colleagues would use the proposed EHR system if it were implemented. Usage intentions 11, 12 were measured using four items assessing participants’ behavioral willingness to use the future EHR system. Job relevance 11, 20 of the future EHR system to the GP’s job was measured with two items reflecting greater perceived job relevance of the future EHR system to their work tasks. Computer use, use of the Internet for their professional and personal needs and use of “Other technology” as previously used technology acceptance constructs 23 - 25 were modified by the authors of this research, and were measured using five items. Satisfaction with the current system was measured as a possible technology acceptance construct using five questions measuring participants’ satisfaction with the currently used e-Health systems in the country (“My Term” and “Health Smart Card”). Questions relating to the three UTAUT moderators, i.e., gender, age, experience 8, were also included in the questionnaire. The voluntariness of use 8 was excluded from the questionnaire as the use of the future EHR in the country will be mandatory, so this question was redundant. The questionnaire was first developed in English, and then translated into the Macedonian language using the translation back-translation method 27. The original questionnaire and technology acceptance constructs used in the research are available from the authors on request 28.

Results:

A total of 458 completed questionnaires were eligible for analysis, yielding a response rate of 40.2%. The age of the respondents who took part in the research ranged from 24 to 65 years, (x ̅ = 44.15; SD = 11.41). Two thirds of the participants in the study, 66.2% (n = 303) were female, and 33.8% (n = 155) were males. The work experience of the participants ranged from <1 year, to GPs with 38 years of experience (x ̅ = 15.45 years of working experience, SD = 10.40). Reliability The internal consistency reliability of the technology acceptance constructs used in the questionnaire was assessed using Cronbach’s α 19, 30. The internal consistency reliability of the measures used in the study ranged from .69 to .94, suggesting that the measures we used were reliable (see Table 1). Descriptive statistics Participants in this research had high performance expectancy from the EHR system. More than half of the participants (across the five measured items) reported positive performance expectancy from the system. A small minority (between 10 - 15%) were not in favour. Between 18-24% of participants were neutral. Respondents also reported high effort expectancy from the system. They expressed more than 50% positive effort expectancy from the EHRs. A small minority (between 7 – 15%) appeared to have a negative attitude, and the neutral responses were higher (between 22 - 31%). Participants also reported over 50% agreement with statements on social influence constructs. Respondents believed that ‘Important others’ colleagues have high expectations from them when using the EHRs. A smaller minority (between 11 – 13%) reported that they did not agree with the statement, and a consistent proportion (between 30 - 33%) of participants were neutral. Participants reported that facilitating conditions were important for the future use of the system. All assessed items showed high levels of agreement (over 50%) with the statements. A smaller minority of participants (between 6 - 18%) appeared not to be in favour, and between 14 - 25% of respondents were neutral. All measured intention items had very high levels of ‘strongly agree’ (> 60%). A smaller minority (between 4 - 5%) appeared to have low intentions, and a consistent proportion (11 - 14%) of participants were neutral on intentions on future use. Bivariate correlations Bivariate correlations using Spearman’s rank order correlation coefficients were estimated prior to the regression analyses. Table 1 presents Spearman rank correlations.   Table 1: Spearman rank correlations 1 2 3 4 5 6 7 8 1. Performance expectancy - .71* .56* .66* .65* .58* .61* .59* 2. Effort expectancy - .68* .69*** .66* .61* .57* .68** 3. Facilitating conditions .61* .59* .58* .63* .62* 4. Job relevance - .65* .55* .59* .62** 5. Social influence - .58*** .68* .63* 6. Satisfaction - .56* .52* 7. Descriptive norm - .58* 8. Intention - x ̅ 3.95 3.82 4.04 3.87 3.73 3.40 3.96 4.41 SD 1.14 .87 .86 1.04 1.10 1.09 1.11 .91 Cronbach α: .91 .88 .74 .69 .93 .88 .85 .94 *** = P < .001; ** = P < .005; * = P < .05 The Spearman correlation showed that usage intention (the main outcome i.e., dependent variable of this research) correlated significantly and positively with all the technology acceptance constructs (r coefficients = .52-.71) included in the study. Gender differences in technology acceptance constructs Independent sample t-tests were used to assess for any gender differences with respect to technology acceptance constructs. The results indicated that the only significant differences were identified in performance expectancy, t(456) = 2.01, P =. 048; where male GPs reported significantly higher scores (x ̅ = 4.10, SD = .08) than their female colleagues (x ̅ = 3.87, SD = 1.17). Predicting intentions to use the EHR system in the future Hierarchical linear regression was used to assess of the multivariate association between intentions to use the EHR system and UTAUT constructs. The analysis was completed in two steps to differentially assess the effects of demographic and IT use/work-related constructs (entered at the first stage of the analysis), and the effects of technology acceptance constructs (the second step of the analysis). The overall model predicted (R2) 65.4% of the variance in intention to use the future EHR system (F = 106.77, P < .001). In the first step of the analysis, only the use of other technology variable (β = -.146, P < .001) predicted intentions to use the future EHR system. In the second step of the analysis, the addition of the basic UTAUT technology acceptance constructs significantly increased the predicted variance in intention to use the future EHR system by 63.2 percent points. The significant predictors of intention to use the EHR system at the final step of the analysis included job relevance (β = .172, P < .001), effort expectancy (β = .217, P < .001), social influence (β = .108, P = .41), descriptive norm (β = .198, P < .001), and facilitating conditions (β = .232, P < .001). The results of the hierarchical regression analysis are shown in Table 2.   Table 2: Predictors of intentions to use the EHR system Step Independent constructs 95% CI for B Standard. β Adj. R2 1 Age .007 – .021 .091 1.8 % Gender .212 – .157 .014 Work experience .022 – .007 .092 Computer use in years .016 – .019 .010 Use of other technology .593 – .110 -.146** Use of Internet for personal .055 – .160 .050 Use of Internet for work .089 – .142 .021 2 Age .014 – .004 .062 65.4% Gender .016 – .206 .049 Work experience .015 – .003 .049 Computer use/years .011 – .011 .001 Use of “Other technology” .144 – .151 .001 Use of Internet for personal .060 – .069 .004 Use of Internet for work .188 – .048 .096** Performance expectancy .012 - .135 .076 Effort expectancy .119 – .335 .217*** Facilitating conditions .157 – .336 .232*** Job relevance .070 - .232 .172*** Social influence .016 - .162 .108* Satisfaction -.063 - .064 .001 Descriptive norm .135 - .282 .198*** ***= p<.001; **=p<.005; *=p<.05 Indirect effects of job relevance on usage intentions We used multiple mediation methodology 31 to assess the indirect effect of job relevance on usage intentions, after controlling for the potential mediation effects of the UTAUT constructs. Bootstrapping and bias-corrected confidence intervals were used to assess the total and indirect effects of the independent variable X (job relevance), on the dependent variable Y (usage intentions), through the effects of multiple mediators, Ms (effort expectancy; social influence, descriptive norm; and facilitating conditions). For the analysis, we used the SPSS Macro Indirect 30 with 1000 resamples and 95% confidence intervals, and the Sobel test (z) was used to enable effect size comparisons between the mediators. The mediation analysis showed that the association of job relevance with intentions was mediated by effort expectancy (z = 5.81, P < .001), social influence ( z= 2.64, P = .008), descriptive norm (z = 4.91, P < .001), and facilitating conditions (z = 4.54, P <.001). The mediation effect of effort expectancy was significantly higher (P = .021) than the effects of social influence and descriptive norm. Moderation effects between UTAUT constructs Eight moderated regression analyses were used to assess the interactive effects of gender, age and working experience on the relationships between the UAUT constructs (effort expectancy, social influence, and facilitating conditions) on intentions to use the EHR system. Technology acceptance predictors were mean-centred to avoid multi-collinearity 32. Because the direct effect of performance expectancy was non-significant, we did not assess the interaction between this variable and gender, age and experience. An interaction term was computed (independent variable × moderator) for each pair of associations, and each moderated regression analysis was completed in two steps. The first step included the main effects of the independent variable and the moderator, and the second step included their interaction term. Unstandardized beta weights (B) and 95% confidence intervals (CIs) were estimated. The analyses identified only two significant moderation effects. Age significantly interacted (P = .029) with social influence (Bage×social influence = .005, 95%CI = .001-.010, β = .077), showing that when age was higher, the association between social influence and intentions was stronger (see Figure 1). Additionally, the relationship between social influence and intentions to use the system was significantly moderated (P = .022) by experience (Bexperience×social influence = .005, 95%CI = .001-.010, β = .080), showing that among GPs in the early stages of work experience there was a stronger relationship between the social influence and intentions to use the EHR system.

Conclusions:

The modified version of the UTAUT applied in this study is a useful tool for researchers for assessment of attitudes and intentions to use new e-Health systems. The main finding from this study, i.e., that effort expectancy, but not performance expectancy, was the strongest predictor of intentions for future use the EHR system contributes to new knowledge and is contrary to findings from previous studies. Other technology acceptance constructs such as descriptive norm, facilitating conditions, and job relevance were found to be lesser predictors of intentions to use of the EHR system in the country.