Venous thromboembolism (VTE), including pulmonary embolism (PE) and deep venous thrombosis (DVT), is estimated to be the third fatal cardiovascular event[1, 2]. It is a common complication among inpatients, which causes perioperative mortality and unexpected death. During the past decade, the hospitalization rate of VTE has increased steadily from 3.2 to 17.5 per 100,000 population in China. Moreover, the complications of VTE, like post-thrombotic syndrome, chronic thromboembolic pulmonary hypertension and hemorrhage seriously affect patients' quality of life and cause a heavy disease burden[5, 6].
VTE is considered as a preventable event[7, 8]. It is estimated that appropriate prophylaxis can reduce the relative risk of DVT and PTE by 50% and 66.7%, respectively[9, 10]. However, the current prophylaxis rate is poorly low. A national, multi-center study revealed that only 14.3% of inpatients at risk of VTE received some form of thromboprophylaxis, among which just 10.3% received appropriate prophylaxis recommended by guidelines. The data emphasized the insufficient management of VTE and showed the necessity to improve the clinical practice of medical staff.
Nurses as the largest cluster of medical staff, play a critical role in identifying inpatients at risk of VTE, implementing prophylaxis measures, and making clinical decisions[13–15]. Many hospitals have adopted VTE clinical decision support system (CDSS) to assist nurses to assess inpatients' individual risk and overcome the barriers in offering prophylaxis[16, 17]. The VTE CDSS is a computerized application system based on artificial intelligence and clinical information storage technology, which aims to realize the functions of risk stratification with the embedded risk assessment models (e.g. Paduwa, Caprini, Geneva), electronic alert reminder, priority preventive measures recommended, and the record of prevention process[17, 18]. Previous studies[17, 19] have shown the introduction of VTE CDSS can significantly increase the rate of adequate prophylaxis and then decrease the incidence of VTE.
However, it was shown that there existed an apparent mismatch between the benefits and adoption of CDSS among nurses[20, 21]. Nurses might become less likely to use the CDSS as they thought it brought them workload, work complexity and perceived threat to professional autonomy[20, 22]. Several studies explored the factors related to nursing staff's use of clinical technology concluded that the value of technology was determined by the appraisal of users[20, 23]. What's more, it has been reported that over 40% of information technology was failed or abandoned for the poor adoption of users[24, 25]. Hence, it is important to understand nurses' attitude and use intention toward the VTE CDSS and seek the influential factors to help engineers improve the design of the system and then extend the implementation and utilization of the system[26, 27]. Nowadays, in the field of technological nursing health care, little attention has been paid to the CDSS used by the nursing staff. The factors that influence the nurses' acceptance of VTE CDSS are still unknown. The purpose of this research is to explore the acceptance of VTE CDSS among nurses and investigate the associated factors.
Theoretical framework and research hypothesis
The original model
The Unified Theory of Acceptance and Use of Technology (UTAUT) is a widely used model to assess users' acceptance which was proposed by Venkatesh, based on eight related psychological and social theories/models. It contains four key constructs, which are effort expectancy (EE), performance expectancy (PE), social influence (SI) and facilitating conditions (FC)[28, 29]. According to the UTAUT, the first three constructs are the core determinants of the users' behavioral intention (BI) while the last construct directly influences the actual behavior use (UB)[28, 29].
UTAUT can explain 70% variance in technology use[30, 31]. Since introduction, it has been applied to explore the critical factors related to the prediction of users' intention and actual use of the technology in various health care settings, such as the health information system[32–34], mobile medical technology[35–37] and other clinical information systems[38, 39]. To our knowledge, UTAUT has not been applied to the field of VTE CDSS.
The modified UTAUT and research hypothesis
To provide a context-related understanding of technology acceptance, the theoretical model must be identified and tested for different technologies and different user groups under certain circumstances. A better understanding of the determinants of the constructs in UTAUT and the associations between them would enable us to design organizational interventions that would increase user' acceptance and usage of new systems[36, 41]. Therefore, our research extended UTAUT to include additional key determinants based on antecedent researches and explore how these determinants affect the usage of the target system among nurses.
User satisfaction (US) is considered to be an important mediating variable influencing users' acceptance of the information technology[39, 42]. The Wixom and Todd (WT) model combined US with technology acceptance, within the model, information satisfaction and system satisfaction represent a user's attitude toward the use intention of information technology. In addition, Abdrbo believed that US can be measured with respect to the EE and PE constructs. Thus, in our research, we added the US variable and proposed the following hypotheses:
H1: EE has a positive effect on nurses' US.
H2: PE has a positive effect on nurses' US.
H3: US has a positive effect on nurses' BI to use VTE CDSS.
Facilitating conditions (FC) measures whether there is an existence of the organizational and technical environment can help remove the barriers to implement CDSS[28, 29]. It was reported that when both EE and PE played a role, the effect of FC on the intention to use would not be significant and thus in the original model, the direct relationship between FC and BI was not included. The review of 174 studies incorporating UTAUT found that 48 of these original studies investigated the direct relationship between FC and BI, and 32 of those studies reported significant positive effects. It is worth noting that most of the included studies also found a significant effect of EE and PE[29, 45] which was contrary to Venkatesh's view. So, it is necessary to re-examine the relationship between FC and BI, and then we proposed the following hypothesis:
H4: FC has a positive effect on nurses' BI to use VTE CDSS.
In 1977, Bandura proposed the self-efficacy (SE) based on social cognitive theory. In the field of information technology, Davis firstly discussed the influence of SE on students' intention to use a word processing software and found it was a vital determinant of the BI. Compeau defined computer SE as an individual's judgment on their ability to use information technology, which means SE is an individual's level of confidence in using information technology to complete specific tasks. Several studies[49–51] demonstrated that computer SE played an essential role in predicting users' BI. Hence, we added the SE as a variable and proposed the following hypothesis:
H5: SE has a positive effect on nurses' BI to use VTE CDSS.
Additionally, we set up 5 hypotheses based on the original model:
H6: EE has a positive effect on nurses' BI to use VTE CDSS.
H7: PE has a positive effect on nurses' BI to use VTE CDSS.
H8: SI has a positive effect on nurses' BI to use VTE CDSS .
H9: FC has a positive effect on nurses' UB.
H10: BI has a positive direct effect on nurses' UB.
The modified UTAUT is presented in Fig. 1.