application validation methods in ehr evaluation models: a systematic literature review

Background: over the last few decades, several theoretical frameworks have been proposed to evaluate electronic health records (ehrs). these frameworks provide a theoretical basis for assessing the impact and outcomes of technology adoption in healthcare settings. this can help identify areas for improvement and ensure that ehrs effectively support healthcare delivery and patient care. objectives: the purpose of this study is to present a comprehensive review of the use of validation methods in electronic health records. methods: out of a total of 62 ehr evaluation frameworks in our previous literature review, at the final stage, 34 relevant articles were included for analysis. variables such as participants and study setting, analysis software, data gathering methods, missing data, and outlier handling, theoretical basis models to develop the ehr evaluation model, the relationship between variables of the ehr evaluation models with evaluation items, sampling technique and sample size reliability assessment methods and values, and statistical validation methods and criteria values were extracted. results: among the 34 papers that disclosed the validation methods utilized, the most widely used technique was structural equation modeling (sem), employed in 26.5% of the studies. other methods utilized were confirmatory factor analysis (cfa), and exploratory factor analysis (efa). a reliability assessment was performed in 82% of the articles. cronbach’s alpha and composite reliability (cr) were popular reliability (internal validation) methods on identified papers. conclusion: it is our belief that the results of this study can assist researchers in examining and modifying ehr evaluation frameworks to suit their specific needs. additionally, we believe that our findings serve as a solid foundation for the creation of new ehr evaluation frameworks. furthermore, we recommend that researchers utilize the findings presented in this article to enhance the implementation and utilization of sem, cfa, and efa methods in ehr evaluation models.