Étude multicentrique des facteurs influençant l’adoption d’un dossier clinique informatisé hospitalier par des infirmières

Like many other healthcare systems, healthcare institutions in Quebec are in the process of transitioning from paper-based patient records to Electronic Patient Records (EPRs). For some time now, this considerable and complex transition has had a high failure rate and unexpected consequences on care practices and the quality and safety of the care provided to patients. The acceptance of information technology by users is considered to be a critical success factor in realizing the benefits of EPR implementation. The goal of this multicenter cross-sectional study was to examine the explanatory factors of the adoption and actual use of an EPR by nurses and their satisfaction. It also aimed at comparing the outcomes by gender, age and experience and by EPR adoption stage.   To do so, a theoretical model based on the unified theory of acceptance and use of technology (UTAUT) was developed and tested on a sample of 616 nurses who use an EPR in four different acute care facilities. Specifically, the study tested 20 research hypotheses on the relationships among eight different constructs, namely EPR compatibility, self-efficacy, performance expectancy, effort expectancy, social influence, facilitating conditions explaining the actual use of an EPR by nurses and their satisfaction. Fifteen research hypotheses were supported following structural equation modeling analysis. The results revealed that performance expectancy, effort expectancy, facilitating conditions and compatibility of the EPR (with preferred work style, existing work practices and values of nurses) were strong determinants of the actual use of an EPR by nurses and their satisfaction. The variables explained 50.2% of the variance of performance expectancy, 52.9% of effort expectancy, 33.6% of actual use of an EPR by nurses and 54.9% of their satisfaction.  The strong fit of the model tested with the sample data showed the influence of performance expectancy on the actual use of the EPR by nurses (r = .55 p = .006) and on their satisfaction (r = .27 p = .010), the influence of the facilitating conditions on effort expectancy (r = .45 p = .009), EPR compatibility on performance expectancy (r = .39 p = .002) and on effort expectancy (r = .28 p = .009). The numerous hypotheses supported highlight the importance of the mediation effects captured by the performance expectancy and effort expectancy constructs. Comparisons based on age, experience and gender of the respondents did not reveal any statistically significant differences in terms of the acceptance and actual use of an EPR by nurses and their satisfaction. However, those based on the four EPR adoption stages revealed significant differences in terms of the relationships modelled. The results suggest that certain key relationships become more intense as the EPR adoption stages progress, thereby providing a better explanation of the variance in the satisfaction of nurses. Some results of the study also differ from the empirical data produced in a predictive perspective of information technology adoption.  The study shows the applicability of the technology acceptance models and theories for nurses working in hospital centres. The results suggest that an EPR that meets performance expectancies is the most determining factor in positively influencing the actual use of the EPR and the satisfaction of nurses. To manage change, the study identified some explanatory factors of the acceptance and use of an EPR, and the model testing revealed how the relationships evolve based on the various EPR adoption stages. The results could help guide decision makers and change agents in determining the measures to implement in order to maximize the benefits of a fully electronic infostructure in healthcare systems.

Keyphrases: Adoption, Satisfaction, Acceptance, Electronic patient record, Structural equation modeling, Cross-sectional study, Information technology, Nurse, Adoption stage, Technology acceptance model