Modeling predictors of acceptance and use of electronic medical record system in a resource limited setting: Using modified UTAUT model

Abstract

The growing application of electronic medical record systems in non-industrialized nations has marked an important area of interest to determine factors associated with acceptance and use of these systems. There have been only a few studies done in this area, which are not yet sufficient to fully understand the antecedents in resource limited settings. The aim of this study is to introduce a modified UTAUT model and show its applicability to assess acceptance and use of electronic medical record (EMR) systems in resource limited settings. An institutional based cross-sectional study was conducted to assess acceptance of the EMR system among potential front-line users (doctors and nurses) in three selected health facilities. Descriptive analysis was performed to describe the characteristics of participants using structural equation modeling (SEM) to explain the extent of the relationship among latent variables. From a total of 423 participants, 95.6% (405) responded from Jun 14 to August 28, 2018 and nine other participants’ data were excluded for being outliers. The majority of the participants were male 64.9% (257) and most of the participants had a work experience of less than five years 52.7% (209). All constructs exhibited an acceptable level of reliability and validity with Cα and CR > 0.7 and AVE >0.5. Although self-efficacy has a surprisingly insignificant effect on the participants' attitude to use the EMR system, Performance expectancy, social influence and effort expectancy demonstrated significant influence with β = 0.278, P < .001, β = 0.397, P < .001, and β = 0.124, P < .05, respectively. The actual use of the EMR system is directly and significantly influenced by self-efficacy, effort expectancy, facilitating condition, and intention to use, with β = 0.701, P < .001, β = 0.145, P < .05, β = 0.125, P < .05, β = 0.539, P < .05, respectively. With 40.2% of the variance in intention to use the EMR system, and 58.5% of the variance in EMR use explained by this model, it could be helpful for determining factors associated with EMR acceptance and use.

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