Participants of the study were clinicians from 307 secondary and tertiary hospitals in Hubei Province, China. The inclusion criteria were as follows: clinicians with experience in using IPCISs, those who knew the purpose of the study, and those who were willing to cooperate. In addition, to ensure a balanced sample, we required a sample size of at least eight clinicians each (including at least two members of the hospital IPC team) in the departments of respiratory medicine, urology, intensive care, neurology, endocrinology, and orthopedics, and at least five clinicians each in other departments.

This study was approved by the ethics committee of Tongji Medical College, Huazhong University of Science and Technology (2021-S063). All participants provided written informed consent, which included survey purpose, survey period, privacy protection, and investigator information. The study data were anonymized and deidentified.

This survey designed the constructs of information system quality (system, information, and service quality), use intention, satisfaction, individual benefits, and organizational benefits based on the D&M model, and added the construct of IPC culture based on the organizational culture perspective. The measurement items we used were adapted from previous related studies with modifications to suit the context of the study. To ensure the validity of the content, we asked 6 experts (2 from the information management field, 2 from hospital management, and 2 from the clinical front line) to check the clarity, redundancy, and understandability of the survey entries (see Table 1 for the constructs and measures). In addition, we tested the reliability and validity of the questionnaire. Kaiser-Meyer-Olkin (KMO) and Bartlett sphericity tests were performed on the constructs. The closer the KMO value is to 1, the stronger is the correlation between the constructs and the more suitable they are for factor analysis. The Cronbach α coefficient was used to evaluate the reliability of internal consistency among items, and the overall Cronbach α coefficient of the questionnaire was generally required to be above .7. In this study, the reflective items were screened through exploratory factor analysis and confirmatory factor analysis, and a questionnaire with reliability and validity was obtained. The final questionnaire comprised 7 components and 41 measurement items, including demographic characteristics, technical factors, hospital IPC culture, use intention, satisfaction, individual benefits, and organizational benefits. Scoring was performed using a 5-point Likert scale (1, completely disagree; 2, disagree; 3, not sure; 4, agree; 5, strongly agree). We used adaptive questioning and simplified the expression of the items to reduce the complexity of the questionnaire.

The questionnaire was generated through an online questionnaire platform [64] and distributed through an electronic link. Prior to distribution, researchers conducted predictive tests to ensure the online questionnaire’s availability and technical functionality, ensuring no vulnerabilities were present. The questionnaire was exclusively open to clinical doctors within the hospital information platform. To prevent participation from nontargeted visitors, an initial termination question was included in the questionnaire (ie, “Are you a clinician or nonclinician?”). If a respondent selected “nonclinician,” their questionnaire response was immediately terminated. The platform determined unique visitors based on the user IP address and user login name. Questionnaire response was mandatory and was supported by the government, which can avoid volunteer bias. Following the strategy of “protecting respondents’ anonymity and reducing assessment anxiety,” we assured respondents that their responses are anonymous and emphasized that there are no right or wrong answers. Finally, we arranged for 2 researchers to check the consistency and completeness of the questionnaire. Data collection lasted for a week.

Partial least squares-structural equation modeling (PLS-SEM) was used to analyze the extended D&M model in this study by using the SmartPLS software (v3.39) [65]. PLS-SEM is a variance-based approach estimating the parameters of a set of equations in a structural equation model by combining principal component analysis with regression-based path analysis [66]. It can handle complex models; has fewer limitations on data distribution, variable type, and actual sample size; and is suitable for developmental theories [67]. The reasons for using PLS-SEM were that the relevant data in this study were not normally distributed, and the extended D&M model was exploratory with a large number of structural model relationships. In order to further understand the influence degrees of different levels in constructs and their measurement items, this study conducted an importance-performance matrix analysis (IPMA). The IPMA results allowed prioritization of constructs and their measurement items to identify the most important factors for improvement. Typically, factors with relatively high importance and relatively low performance require the most attention [68,69].

A total of 12,493 clinicians from 246 secondary and tertiary medical institutions answered the questionnaire, and 12,317 questionnaires met the requirements after review, with a response rate of 98.6%. The demographic characteristics of the clinicians surveyed are shown in Table 2.

Composite reliability and Cronbach α values were used to assess the internal reliability of reflective constructs. The results showed that all composite reliability values were higher than the recommended value of 0.8 (range: 0.934-0.992), and Cronbach α values were above the recommended value of .7, indicating sufficient reliability for all constructs. The results for convergent validity showed that the average variance extracted of all constructs was greater than the recommended threshold of 0.5 (range: 0.806-0.967), suggesting that the variance of all constructs was greater than the variation induced by individual measurement error, which confirmed the convergence validity (Table 3). The discriminant validity involved the Fornell and Larcker criterion. The results in Multimedia Appendix 1 demonstrate that the root mean square for each construct is higher than the cross-loading.

After confirming the reliability and validity of the measurement model, we tested the hypotheses by estimating the structural model. The explanatory power of the model was evaluated with the R² score. The R² values of satisfaction (0.833), use intention (0.821), individual benefits in communication (0.624), individual benefits in decision-making (0.676), and organization benefits (0.656) were all higher than the level of 0.5. This indicates that the extended D&M model had a moderate to high level of explanatory power, accounting for 82.1% of the variance in use intention. Evaluating the effect size (f²), it was found that the impact size of system use on individual decision-making benefits was large (2.090).

Through the bootstrapping process, we provided normalized path coefficients for each path to compare the effects of different constructs. First, we found that users’ use intention was significantly related to individual decision-making benefits (β=.474; P<.001), individual communication benefits (β=.466; P<.001), and organizational benefits (β=.461; P<.001), which suggests that when individuals are willing to use IPCISs to support their IPC work, they are better able to communicate with each other and make decisions, as well as facilitate management at the organizational level. Therefore, the hypotheses H6a, H6b, and H6c are supported.

After understanding the effects of use intention and satisfaction, we further investigated the antecedents that may drive individuals to use IPCISs and their satisfaction. The results showed that all quality dimensions (system quality, information quality, and service quality) had a significant effect on user satisfaction (system quality: β=.252; P<.001; information quality: β=.294; P<.001; service quality: β=.411; P<.001). Therefore, our data support hypotheses H1a, H1b, and H1c. With regard to use intention, there was significant influence by system quality (β=.089; P<.001), information quality (β=.102; P<.001), and service quality (β=.125; P<.001). Moreover, IPC culture (β=.059; P<.001) had a significant impact on use intention. Thus, hypotheses H2a, H2b, H2c, and H3 are supported. The structural model is shown in Figure 2. These results suggest that in addition to technical factors, organizational culture in IPC is an important influencing factor of use intention and satisfaction (Table 4).

It was found from the path coefficient that use intention had a greater impact on net benefits; thus, this study focused on the prioritization of attribute-level factors that affect use intention. We have shown the importance and performance indices of construct attributes in a grid (Figure 3), and the attributes present in the fourth quadrant are the ones that need to be improved as they represent high relative importance but low relative performance. We found that satisfaction, service quality, and information quality had higher importance and lower performance regarding use intention. In particular, the satisfaction index SA3 (satisfaction with the service quality of the information system), the service quality index FQ3 (the hospital provides training in the use of the information system), and the information quality index IQ3 (the legibility of the information) are directions that can be further improved. Additional assessment results of IPC culture are provided in Multimedia Appendix 2.

This study evaluated the application of IPCISs from the perspective of clinicians, using the extended D&M model as the theoretical framework. The extended model incorporates the cultural construct, providing a more comprehensive evaluation of IPCISs. Consistent with previous studies [70,71], our findings indicate that the technical factors of IPCISs, namely system quality, information quality, and service quality, positively impact user satisfaction and use intention. In addition, our research confirms that the use intention of IPCISs has an impact on individuals’ decision-making and communication, and organizational effectiveness, such as organizational management and response to hospital infection emergencies. This is partially consistent with similar findings in a previous report [72]. Notably, our study revealed that system usage contributes more to net benefits than satisfaction. This implies that if IPCISs meet the clinicians’ needs, they will experience the benefits of using the systems. Through this result, it can be seen that the application of information systems has a significant effect on the benefits that individuals and organizations are most concerned about. Therefore, it is crucial for hospitals to focus on system improvements that align with the actual needs of users, enabling effective decision-making and communication in IPC, as well as organizational IPC management.

Our research offers new insights that differ from the insights of previous studies. In contrast to prior research [70,71], our IPMA revealed that service quality is a more important measure of the willingness to use IPCISs, followed by information quality. Among them, the dimension FQ3 (the hospital provides sufficient training on system use) emerged as a crucial component indicator for service quality. This highlights the necessity of enhancing clinicians’ operational capabilities when using IPCISs, as advocated by the APIC. Hospitals should consider augmenting training support to enhance clinicians’ proficiency in system usage. Research [73] also mentioned that providing end-user training and management support helps to create a supportive environment, which will pay dividends in increasing the use and effectiveness of the system. Regarding information quality, our analysis demonstrated that clinicians placed a premium on easy-to-read information fields and reports (dimension IQ3). Relevant research findings also confirmed that the information presentation method of the experimental inspection report impacts clinical treatment efficiency and drug use [74]. Bauer et al found that improving the presentation of biochemical metric reports allowed physicians to achieve better results in terms of assessment speed and user satisfaction [75]. Consequently, the findings derived from the IPMA provide specific directions for further improving IPCISs.

It is worth noting that this study found a significant positive influence of IPC culture on use intention, which is consistent with similar findings in previous research [52,53]. However, the IPMA results indicate that IPC culture has a relatively lower importance compared to other factors in influencing the intention to use information systems. We believe this reflects the current lack of emphasis on using information technology to assist in hospital infection control, despite the significant impact of IPC culture on the intention to use information systems. This highlights the significant interaction between organizational culture and use intention in task-oriented organizations [76], and indicates the need for interventions from IPC culture to enhance the awareness and information abilities of clinicians. Research indicates that besides the factors related to system usability, numerous organizational and cultural factors also affect the use of hospital management information systems [72]. Callen et al discovered that in constructive hospital cultures, health care professionals exhibited a positive attitude toward information systems, while in defensive and negative cultures, individuals display a negative attitude toward information systems [52]. Organizational culture, as a crucial social characteristic within an organization, is often overlooked when implementing information technologies. Solely emphasizing system performance may not lead to effective implementation. Our research findings indicate that strengthening the cultural atmosphere of IPC within hospitals can increase health care professionals’ attention to IPC and promote the use of IPCISs. Currently, countries worldwide are designing and implementing measures for hospital IPC, and our research results can provide evidence-based support for intervention measures.

Although this study provides interesting insights into understanding the effects of IPCIS applications, there are some limitations to consider. First, given the cross-sectional survey used to collect data in this study, our finding about how technical factors and hospital IPC culture affect IPCIS use cannot fully reveal the causal relationship between variables. Second, the sample for this study comes from a certain region. Although the sample size was quite large and representative, the generalizability of the research findings needs to be verified. In the future, longitudinal studies could be conducted, with collection of data in multiple stages to verify causality. Additionally, comparative studies on the use and evaluation of IPCISs in hospitals regarding different levels and types could be added to better provide insights for IPCIS construction.

This study evaluated the application of IPCISs among clinicians based on the extended D&M model. We confirmed the applicability of the extended D&M model in studying the use of IPCISs. We found that technical factors (system quality, information quality, and service quality) and hospital IPC culture have an impact on the successful use of IPCISs. Among them, service quality and information quality showed higher importance and lower performance for use intention. Moreover, use intention had a greater impact on decision-making in net benefits. Furthermore, the results of the IPMA suggested that providing information system training and improving the legibility of information in the system could improve the use intention of clinicians. These findings provide empirical evidence and specific practical directions for further improving the construction of IPCISs. We suggest that hospitals conduct more training on the use of IPCISs, modify the information presentation form on the system, and adjust the amount of information to improve the efficient use of IPC information by clinicians.