4.2 Article

Testing the technology acceptance model for evaluating healthcare professionals' intention to use an adverse event reporting system

Journal

INTERNATIONAL JOURNAL FOR QUALITY IN HEALTH CARE
Volume 20, Issue 2, Pages 123-129

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/intqhc/mzm074

Keywords

technology acceptance model; trust; patient safety; reporting systems

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Background. Many healthcare organizations have implemented adverse event reporting systems in the hope of learning from experience to prevent adverse events and medical errors. However, a number of these applications have failed or not been implemented as predicted. Objective. This study presents an extended technology acceptance model that integrates variables connoting trust and management support into the model to investigate what determines acceptance of adverse event reporting systems by healthcare professionals. Method. The proposed model was empirically tested using data collected from a survey in the hospital environment. A confirmatory factor analysis was performed to examine the reliability and validity of the measurement model, and a structural equation modeling technique was used to evaluate the causal model. Results. The results indicated that perceived usefulness, perceived ease of use, subjective norm, and trust had a significant effect on a professional's intention to use an adverse event reporting system. Among them, subjective norm had the most contribution (total effect). Perceived ease of use and subjective norm also had a direct effect on perceived usefulness and trust, respectively. Management support had a direct effect on perceived usefulness, perceived ease of use, and subjective norm. Conclusion. The proposed model provides a means to understand what factors determine the behavioral intention of healthcare professionals to use an adverse event reporting system and how this may affect future use. In addition, understanding the factors contributing to behavioral intent may potentially be used in advance of system development to predict reporting systems acceptance.

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