Factors affecting the adoption of electronic health records of nurses in Hong Kong

Pao Yue-kong Library Electronic Theses Database

Factors affecting the adoption of electronic health records of nurses in Hong Kong

 

Author: Mak, Kwok Kuen
Title: Factors affecting the adoption of electronic health records of nurses in Hong Kong
Degree: D.H.Sc.
Year: 2017
Subject: Medical records -- Data processing -- China -- Hong Kong.
Nursing -- Data processing.
Hong Kong Polytechnic University -- Dissertations
Department: Faculty of Health and Social Sciences
Pages: viii, 130, 5 pages : color illustrations
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2961701
URI: http://theses.lib.polyu.edu.hk/handle/200/8939
Abstract: The purpose of introducing Electronic Health Records (EHR) systems into healthcare services is to manage patients' health information in a manner that effectively promotes better clinical decision making, and in turn enhances service quality and quality of care. This objective can only be achieved with the active adoption of EHR by clinical users. The determinants of their adoption are therefore worthy objects of investigation. Nurses represent a major stakeholder group in healthcare services. They need patients' health information to make clinical judgments, and as such, their adoption of EHR systems influences the quality of their clinical decisions and patient outcomes. However, little is presently known about the determinants of nurses' uptake of EHR. This study aims to develop a conceptual framework for understanding the factors affecting nurses' adoption of EHR systems through theoretical extension of the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2013). The model integrates both the mediating role of performance expectancy from models of technology adoption, and the concept of confirmation of expectation from the Theory of Expectation-Disconfirmation (Oliver, 1977). The model of the present study consists of seven constructs for explaining nurses' adoption of EHR systems in clinical practice. In addition to performance expectancy and confirmation of expectation, effort expectancy, social influence, facilitating conditions, behavioral intention, and usage behavior are also included. The study employed a cross-sectional survey where convenience sampling technique was used for data collection, and partial least square structural equation modeling for data analysis. Data were collected from nurses working in inpatient wards of a public hospital in Hong Kong. A total of 445 questionnaires were returned, of which 425 were suitable for data analysis. The results indicated that the proposed model is capable of assessing the adoption of EHR systems among nursing users, explaining 56% of the variance in their intention to adopt EHR systems and 67% of the variance in their actual usage. Stone-Geisser tests further confirmed the predictive validity of the model.
The present study found that performance expectancy, effort expectancy and confirmation of expectation were the significant determinants of behavioral intention, and that behavioral intention was significantly associated with usage behavior. Moreover, the results indicated that performance expectancy mediated the effects of effort expectancy and social influence on behavioral intention. These findings have important implications for both theory and practice. In addition to applying the extant models of technology adoption to nurses in clinical contexts, this study extended the Theory of Expectation-Disconfirmation, which is commonly used in marketing research, and applied it to study nurses' EHR adoption. The present work also leads to new discoveries, including (i) the mediating role of performance expectancy, (ii) the impact of nurses' confirmed expectations of their intention to use EHR systems, and (iii) the insignificance of the effects of external factors on nurses' EHR adoption. Several managerial implications also emerge. The model presented here has considerable potential to help system developers and hospital managers understand and identify the factors affecting nursing users' EHR adoption, and facilitate the planning and prioritization of resources for the development and implementation of EHR systems. Further extension and validation of the present research findings are recommended, particularly on the mediating role of performance expectancy and the association between confirmed expectation and intention. A longitudinal approach to examining the changes in nurses' perceptions, and the effects of these changes on their adoption behavior is useful in gaining a deeper understanding of the factors affecting nurses' adoption of EHR systems. In conclusion, the study successfully identified the determinants of nurses' EHR adoption in clinical contexts, and provided recommendations for researchers, system developers and hospital managers that can be used to increase the EHR adoption of nursing users. A wider adoption of EHR systems by nursing users can contribute to better health data management and clinical decision support, thereby enhancing the quality and safety of healthcare services.

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