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Factors Affecting the Extent of Patients’ Electronic Medical Record Use: An Empirical Study Focusing on System and Patient Characteristics

BACKGROUND: Patients’ access to and use of electronic medical records (EMRs) places greater information in their hands, which helps them better comanage their health, leading to better clinical outcomes. Despite numerous benefits that promote health and well-being, patients’ acceptance and use of EM...

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Detalles Bibliográficos
Autores principales: Agrawal, Lavlin, Ndabu, Theophile, Mulgund, Pavankumar, Sharman, Raj
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8587186/
https://www.ncbi.nlm.nih.gov/pubmed/34709181
http://dx.doi.org/10.2196/30637
Descripción
Sumario:BACKGROUND: Patients’ access to and use of electronic medical records (EMRs) places greater information in their hands, which helps them better comanage their health, leading to better clinical outcomes. Despite numerous benefits that promote health and well-being, patients’ acceptance and use of EMRs remains low. We study the impact of predictors that affect the use of EMR by patients to understand better the underlying causal factors for the lower use of EMR. OBJECTIVE: This study aims to examine the critical system (eg, performance expectancy and effort expectancy) and patient characteristics (eg, health condition, issue involvement, preventive health behaviors, and caregiving status) that influence the extent of patients’ EMR use. METHODS: We used secondary data collected by Health Information National Trends Survey 5 cycle 3 and performed survey data analysis using structural equation modeling technique to test our hypotheses. Structural equation modeling is a technique commonly used to measure and analyze the relationships of observed and latent variables. We also addressed common method bias to understand if there was any systematic effect on the observed correlation between the measures for the predictor and predicted variables. RESULTS: The statistically significant drivers of the extent of EMR use were performance expectancy (β=.253; P<.001), perceived behavior control (β=.236; P<.001), health knowledge (β=–.071; P=.007), caregiving status (β=.059; P=.013), issue involvement (β=.356; P<.001), chronic conditions (β=.071; P=.016), and preventive health behavior (β=.076; P=.005). The model accounted for 32.9% of the variance in the extent of EMR use. CONCLUSIONS: The study found that health characteristics, such as chronic conditions and patient disposition (eg, preventive health behavior and issue involvement), directly affect the extent of EMR use. The study also revealed that issue involvement mediates the impact of preventive health behaviors and the presence of chronic conditions on the extent of patients’ EMR use.