Modeling patients' acceptance of provider-delivered e-health

Abstract

Objective: Health care providers are beginning to deliver a range of Internet-based services to patients; however, it is not clear which of these e-health services patients need or desire. The authors propose that patients' acceptance of provider-delivered e-health can be modeled in advance of application development by measuring the effects of several key antecedents to e-health use and applying models of acceptance developed in the information technology (IT) field.

Design: This study tested three theoretical models of IT acceptance among patients who had recently registered for access to provider-delivered e-health.

Measurements: An online questionnaire administered items measuring perceptual constructs from the IT acceptance models (intrinsic motivation, perceived ease of use, perceived usefulness/extrinsic motivation, and behavioral intention to use e-health) and five hypothesized antecedents (satisfaction with medical care, health care knowledge, Internet dependence, information-seeking preference, and health care need). Responses were collected and stored in a central database.

Results: All tested IT acceptance models performed well in predicting patients' behavioral intention to use e-health. Antecedent factors of satisfaction with provider, information-seeking preference, and Internet dependence uniquely predicted constructs in the models.

Conclusion: Information technology acceptance models provide a means to understand which aspects of e-health are valued by patients and how this may affect future use. In addition, antecedents to the models can be used to predict e-health acceptance in advance of system development.

Figure 1.

Three models of technology acceptance.

Figure 2.

Integrated model showing standardized estimates. Measured items are illustrated in rectangles (e.g., IM1). Latent variables are illustrated in ovals (e.g., IM); smaller ovals illustrate error of measurement (e.g., err1). Associations are illustrated by arrows that indicate the direction of prediction. Factor loadings are noted at the top right of item rectangles. Coefficients are noted for each association (i.e., directional arrow). Variance (R²) is noted for each latent variable within the model that has an association directed toward it.

Figure 3.

Integrated model with antecedents showing standardized estimates.