Factors influencing patients' intention to use hospital information system in the old-new transition period: A case study of Hospital Examination Reservation System

doi:10.21203/rs.3.rs-1957829/v1

Objective

This study established a theoretical model to analyze the factors influencing patients’ intention to use a new Hospital Information System (HIS) and provide suggestions for hospital administrator: A case study of Hospital Examination Reservation System(HERS).

Background

The successful application of the new HIS could not only optimize the hospital examination process, but also improve the overall utilization rate of hospital examination equipment, and it could also improve patient satisfaction. But the key is how to successfully help the new HIS to transition.

Method

The target population of this study was patients who had used the HERS, and the questionnaire was filled through the Sojump. Structural equation modeling was used to analyze the data.

Results

Habit was the main positive factor influencing the behavioral intention of using HERS (β = 0.539, P < 0.001), followed by patient innovation (β = 0.252, P < 0.001), effort expectancy (β = 0.209, P < 0.001), facilitating conditions (β = 0.192, P < 0.001), and perceived privacy exposure have a significantly negative effect on behavioral intention (β=-0.126, P < 0.001). The above five variables explained 65.4% of the variation in behavioral intention.

Conclusion

In order to operate the new system better, hospitals could cultivate patients' HERS usage habits. Patients value the recommendations from doctors and innovative patients, and care about the privacy and the ease of operation, so hospitals could Invite patients with high innovation to recommend HERS to others and arrange staffs to guide and help the use of this system. And it is also important of training doctors.

UTAUT2

Hospital Examination Reservation System

Behavioral intention

HIS

In recent years, China's social informatization has shown a trend of rapid development, and using safer, more effective and more intelligent systems like Hospital Information System(HIS), Laboratory Information Management System (LIS), Picture Archiving and Communication Systems(PACS) has become key targets of the construction of smart hospitals, which aim to improve patient satisfaction and provide high-quality services.[1] There is growing evidence that increased patient safety, improved clinical effectiveness, and higher hospital reputation are associated with higher patient satisfaction.[2] However, the use of a new system will encounter many difficulties.[3] The new system is unfamiliar to users so learning to use it requires effort, and using it means abandoning the old system's habits. At present, most studies focus on the advantages and benefits of a new system, but there are few studies focusing on the use intention of the system in old-new transition period. The success of a new system depends on the successful use of the system.[4]

Long waiting times for medical treatment have been identified as a major cause of low patient satisfaction.[5–7]Due to the backward informatization in developing countries and the large number of patients in hospitals, it often takes a longer time for patients to wait for examination in China.[8] In a systematic review, 60% of the studies found that long waiting times is the most unsatisfactory factor for Chinese patients in hospital.[9] Therefore, how to reduce patients’ waiting time become the key to improve patients' satisfaction, almost all of the medical institutions are trying to find an effective method to control the service process for patients, such as reservation strategy and scheduling rules. Hospital Examination Reservation System (HERS) based on HIS and PACS is considered an effective method at present. HERS recommends the most effective examination sequence and time to patients through the data mining technique, and it can be used to check in, modify the appointment examination time and print the examination report. Relevant studies have shown that the use of HERS significantly reduces the waiting time of patients.[1, 10] Therefore, this study will take HERS as an example to study the influencing factors of the using of a new system, and to provide suggestions for the successful application of the new system in hospitals. The successful application of the system can not only optimize the hospital examination process, but also improve the overall utilization rate of hospital examination equipment.[11]

Theory and Hypothesis

Venkatesh et al proposed the UTAUT2 in 2012. According to the UTAUT2, performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit can influence people to use a new system, and the model can explain 70% of the variation in behavioral intention.[12] Nowadays, UTAUT and UTAUT2 have been widely used to explain the reason of use a new system, and many researchers had extended the model based on their own research. Hoque et al added technology anxiety and resistance to change to study the factors affecting the intention of the elderly to adopt mobile health(mHealth) services[13].Wang et al believes that task-technology fit will positively affect the intention to use healthcare wearable devices.[14] Prasetyo et al found learning value and instructor characteristics can affect the acceptance of medical education elearning platforms during the COVID-19 pandemic[15]. Based on relevant literature and the actual use of HERS, the following hypotheses are proposed in this study, and the proposed model is shown in Fig. 1.

Performance expectancy refers to the benefit patients expect from HERS, such as shortening waiting time and improving examination efficiency. The greater the benefit is, the higher behavioral intention patients will have, so hypothesis 1 is established:

H1 Performance expectancy has a positive effect on behavioral intention of using HERS

Effort expectancy refers to the degree of the ease for patients to learn to use or use the HERS. The easier the using is, the higher behavioral intention patients will have, thus establish hypothesis 2:

H2 Effort expectancy has a positive effect on behavioral intention of using HERS

Social influence refers to the degree of support from doctors and patients' relatives and friends for using HERS. The higher the degree of support is, the higher behavioral intention patients will have, so hypothesis 3 is established:

H3 Social influence has a positive effect on behavioral intention of using HERS

In a study of web-based interactive self-management techniques, social influence was found to indirectly affect behavior intention through performance expectancy[16], and the products recommended by doctors will be highly expected by patients[17].The higher the degree of support is, the higher performance expectancy patients will have, so hypothesis 4 is established:

H4 Social influence has a positive effect on performance expectancy of using HERS

Facilitating conditions refer to the adequacy of the necessary resources for using HERS, such as mobile phones and Internet. The more sufficient the resources are, the higher behavioral intention patients will have. Therefore, hypothesis 5 is established:

H5 Facilitating conditions has a positive effect on behavioral intention of using HERS

Studies have shown that when users find it convenient to use a system (they can get help from others quickly), the perceived difficulty is reduced[14, 18]. The more convenient using HERS is, the easier patients perceive using system will be. Therefore, hypothesis 6 is established:

H6 Facilitating conditions has a positive effect on effort expectancy of using HERS

Habit refers to the habitual or automatic behavior of patients using the HERS, which presents the results of previous experience. Once an action becomes a habit, it is automatic and does not require a conscious decision.[12] Habit is always considered as a predictor of actual use because its influence on actual use exceeds behavioral intention, our study does not collect actual use data, so hypothesis 7 is established:

H7 Habit has a positive effect on behavioral intention of using HERS

HERS can improve patients' satisfaction with medical treatment, but such information system will collect patients' demographic information and case data, and there is a risk of data leakage. Multiple studies have shown that perceived privacy risks affect people's use of system [17, 19, 20]. The higher the degree of perceived risk is, the lower behavioral intention patients will have, so hypothesis 8 is established:

H8 Perceived privacy risks has a negative effect on behavioral intention of using HERS

Innovation is the personal characteristic of patients. Rogers called the process of innovation adoption from the first contact with information about innovative products to the final adoption of new products. Innovators are bold, adventurous and keen on new ideas and concepts.[21] The more innovative patients are, the higher behavioral intention they will have. Therefore, hypothesis 9 is established:

H9 Patient innovation has a positive effect on behavioral intention of using HERS

Self-efficacy is the confidence that people will take the necessary actions in order to achieve their desired goals[22]. Research shows that many innovative ideas and behaviors are based on people's confidence, and self-efficacy is an important factor affecting innovation. [21, 23–25] The more innovative patients are, the more confident they will be in using the system, which means patients perceived that it was easier for them to use the HERS, so hypothesis 10 is established:

H10 Patient innovation has a positive effect on effort expectancy of using HERS

Midgley et al believed that innovators were more willing to take the risk of using a new system[26]. According to the Diffusion of Innovations, innovators are bold and adventurous[21], therefore, under the same degree of risk, patients with strong innovation have a lower degree of perceived risk, hypothesis 11 is established:

H11 Patient innovation has a negative effect on perceived privacy risks of using HERS

Data collection

The target population of this study was patients who had used the HERS. Six months after using the system, the questionnaire investigator was commissioned to stand beside the HERS machine or the medical technology department, collected the questionnaire data when patients finished using the HERS, and the questionnaire was filled through the Sojump (Changsha ran Xing InfoTech Ltd, China). The items were measured with a 5-point Likert scale ranging from “strongly disagree” (1) to “strongly agree” (5). Before the survey, we introduced the purpose of the study and filled in the questionnaire with the consent of the patients. The questionnaire was filled out by the patients themselves, and each WeChat account and mobile IP address could complete the questionnaire only once.

Data analysis

The demographic characteristics of patients were analyzed by using the SPSS (Version 24.0. Armonk, NY: IBM Corp; 2021). AMOS26.0 (Version 26.0. Armonk, NY: IBM Corp; 2021) was used to evaluate the reliability of the construct and the structural model. Reliability was measured by Cronbach's alpha and composite reliability, both of which needed to be greater than 0.70. We measured the validity of convergence based on the average variance extracted (AVE), and a value higher than 0.50 indicated that the construct had good convergence. The discriminant validity was acceptable if the correlation coefficient between structures was less than the square-root of the corresponding structure. The model fit was generally considered acceptable if χ²/df was less than three; the goodness of fit index, normed fit index, comparative fit index and the incremental fit index were all above 0.90, and the root mean-squared error of approximation was less than 0.08. For the structural model, 2,000 bias-corrected samples were extracted to calculate the path coefficients and their significance. P-value (two-tailed) less than or equal to 0.05 was considered to have significant effects. R² was used to represent the explanatory degree of the independent variables with respect to the dependent variables.

Demographic characteristic

A total of 394 valid questionnaires were collected in this study, among which 279 (70.81%) patients were female, 251 (63.71%) patients had a high school education level or above, 283 (71.83%) patients were urban residents, and the average age of patients was 44.048 ± 16.657 years. 84 patients (21.32%) still found it difficult to use after 6 months.

The measurement model

Two indexes with low factor loading (HT4 and PI4) were removed. It can be seen from Table 1 that AVE, CR and Cronbach's alpha are all greater than recommended values, which indicates good reliability and convergent validity. The discriminant validity of each construct is shown in Table 2. Although correlation coefficients of 3 constructs (SI vs FC, SI vs HT and FC vs HT) are greater than the square root of corresponding AVE, the other correlation coefficients meet the requirements, so discriminant validity is still considered acceptable, and the data collected can be used to establish the structural model.

Table 1

Convergent validity and reliability analysis
Construct	Item	Factor lodaing	AVE(> 0.5)	CR(> 0.7)	Cronbach’s α (> 0.7)
PE	PE1	0.945	0.891	0.961	0.960
	PE2	0.958
	PE3	0.929
EE	EE1	0.894	0.840	0.940	0.940
	EE2	0.929
	EE3	0.926
SI	SI1	0.820	0.717	0.910	0.922
	SI2	0.829
	SI3	0.863
	SI4	0.873
FC	FC1	0.833	0.698	0.902	0.891
	FC2	0.909
	FC3	0.808
	FC4	0.787
HT	HT1	0.907	0.798	0.922	0.919
	HT2	0.925
	HT3	0.846
PI	PI1	0.745	0.641	0.842	0.840
	PI2	0.764
	PI3	0.886
PPR	PPR1	0.823	0.699	0.874	0.871
	PPR2	0.909
	PPR3	0.771
BI	BI1	0.899	0.805	0.943	0.942
	BI2	0.933
	BI3	0.906
	BI4	0.849

Table 2

Discriminant validity
square root of AVE	PE	EE	SI	FC	HT	PI	PPR	BI
PE	0.944
EE	0.798	0.917
SI	0.807	0.875	0.847
FC	0.812	0.912	0.954	0.834
HT	0.793	0.863	0.901	0.956	0.893
PI	0.449	0.501	0.512	0.555	0.613	0.801
PPR	-0.106	-0.140	-0.126	-0.140	-0.114	-0.118	0.836
BI	0.621	0.686	0.674	0.709	0.756	0.638	-0.227	0.897

The structural model

Table 3 shows the fitting index values of the structural model. GFI is less than the recommended value, but it is still within the acceptable range, and other fitting index values all meet the optimal condition, indicating that the collected data fits the structural model well. It can be seen from Table 4 that 4 hypotheses were rejected among the 11 hypotheses in this study, and the standardized regression coefficients of all other variables were significant.

Insignificant variables were deleted from the model, and the new model were shown in Fig. 2. The model’s fitting index values met the requirements. Patient innovation have an indirect effect on behavior intention (β = 0.014, P < 0.001), and perceived privacy risk played a mediating role in this indirect effect. Facilitating conditions have an indirect effect on behavior intention (β = 0.192, P < 0.001), and effort expectancy played a mediating role in this indirect effect.

Habit was the main positive factor influencing the behavioral intention of using HERS, followed by effort expectancy, patient innovation and facilitating conditions, and perceived privacy exposure have a significantly negative effect on behavioral intention. The above five variables explained 65.4% of the variation in behavioral intention.

Table 3

Fit indexes
Fit indexes	χ²/df	GFI	NFI	CFI	RMSEA	IFI
Measurement model	2.631	0.874	0.930	0.956	0.064	0.956
Recommended value	< 3	> 0.9	> 0.9	> 0.9	< 0.08	> 0.9

Table 4

Standardized regression weights between the model variables
Path (Support)	β	t-value	P-value
H1: PE → BI (No)	0.096	1.191	0.233
H2: EE → BI (Yes)	0.239	1.980	0.048
H3: SI → BI (No)	-0.011	-0.032	0.975
H4:SI→PE(Yes)	0.837	18.624	< 0.001
H5: FC →BI (No)	-0.251	-0.572	0.567
H6:FC→EE(Yes)	0.913	25.014	< 0.001
H7: HT→ BI (Yes)	0.593	3.142	0.002
H8: PPR → BI (Yes)	-0.138	-3.465	< 0.001
H9:PI→BI(Yes)	0.269	4.480	< 0.001
H10: PI→EE(No)	0.028	1.010	0.312
H11: PI→PPR(Yes)	-0.113	-1.964	0.050

Help patients to develop habits of using the new system.

This study found that patients with the habit of using a new system had a higher behavioral intention, which was consistent with the results of Baudier, Zanetta et al[27, 28]. Nowadays, more and more hospitals begin to use smart medical system to improve medical quality and service. From the first contact with such new systems, patients will gradually get familiar with the use of them. With the passage of time (namely experience), patients can form different degrees of habits based on their interaction and familiarity with the system[12]. Ajzen et al point out that feedback from experience can influence beliefs,[29] so patients who are accustomed to using similar information systems are more likely to use the HERS. "Habit" is defined as a specific form of automaticity in which responses are directly cued by the contexts.[30] Therefore, in the old-new transition period, patients would ignore the existence of the new system due to the habit, and they will habitually ask doctors for help making manual appointments and check-ins (manual registration is the old form of hospital), which leading to low utilization rate of new system and high complaint rate of patients. Habits are hard to change, they take repetition to form[31], so it is important to arrange volunteers to guide patients when hospitals use a new system. During intervention, it is also important to remind the patients to form the habit of using the new system.[32]

Invite patients with high innovation to recommend HERS to others.

The influence of innovation on behavioral intention has been confirmed by relevant studies [33–35], Thakur also pointed out that improving innovation will reduce the perceived risk of using a new system, which is consistent with our findings. In this study, innovation is a patient's personal trait, which can be expressed as four levels of meaning, namely (1) hope: stay committed to goals and redirect path to the goals to achieve success when encountering difficulties, (2) self-efficacy: have confidence in taking on challenging and put in the effort necessary to succeed, (3) resilience: pick up and maintain or increase efforts to succeed when beset by adversity, and (4) optimism: has a positive feeling about future success[36, 37]. Patients with higher level of innovation have higher initiative in using the HERS during the old-new transition period than other patients, and lower frustration after failure. Therefore, such patients are more willing to use or continue to use the system. However, innovation is a personal characteristic of patients, it is difficult for hospitals to intervene in it. According to diffusion of innovations theory, a belief in the effectiveness of an innovation may be more important than an understanding of the actual results[38], so it may be an effective way that inviting patients with high innovation and doctors to recommend the use of the new system, and patients with low innovation will value their opinions.

Arrange staffs to help the use of the new system.

Facilitating conditions refer to the resource condition for patients to use the HERS. In this study, facilitating conditions did not directly affect the behavioral intention because HERS is free for using, and there will be staff to help solve the problems. Adequate convenience increases the ease with which patients use the system, which is consistent with previous studies.[14, 18] Patients will more like to use the system if they find HERS is easy to use. and perceived privacy risk would discourage patients from using the system, to sum up, the hospital should continue arranging staffs to help and guide the use of this system in the old-new transition period, and they can reassure patients by introducing them to the system's privacy capabilities. What’s more, it is necessary to continuously upgrade the system to make its operation clearer, simpler and more secure.

Train doctors before launching the system

Performance expectancy and social influence do not affect the behavioral intention, but the role of these two factors could not be ignored in related studies,[17, 39, 40] we think the reasons could be as follows: 1. Although HERS can improve the examination efficiency of patients, the improvement is mainly reflected by the data in HIS (comparing the average waiting time before and after using the HERS); 2. Six months after the launch of the HERS, some patients may have forgot the appointment examination process without the system and have no obvious perception of efficiency improving; 3. Doctors and patients are still not proficient in using the new system in the old-new transition period. The advantages of the new system have not been felt by them, so there are few cases of doctors recommending to patients and patients recommending to patients. Therefore, relevant training should be carried out among doctors when launching the new system in hospitals, and the role of the system should be explained to patients when promoting it. At the same time, strengthening testing and parallel switching can be adopted to pass the old-new transition period. [41, 42]

First, the samples in this study were only from one 3A hospital, so the representativeness is limited. Future multi-center studies can be carried out. Second, Because of lack of continuous innovation in the relevant apps, patients will suffer from burnout and behavior intention will vary over a long period of time using the system. Study has showed that the frequency of app use decreased by about 25% in three-month experiment. [43] Therefore, further longitudinal surveys are needed to observe this effect. Third, our model is for HERS, and it should be applied to other hospital information systems. Finally, potential moderators (age, gender et al) that could affect patients’ behavior intention were not taken into consideration.

When a new information system is launched in hospital, the operation, function and role of the system should be publicized first, the ease of use and the privacy exposure risk will influence the use intention of patients. Staffs should be continuously arranged to help patients solve problems in use and cultivate the habit of patients in using the new system. Medical institutions should invite patients with high level of innovation and behavioral intention to recommend the new system, and the recommendation from doctors is also very important. Testing of new systems should be Strengthened and the new and old systems can be carried out in parallel.

Ethical Approval and Consent to participate

The study followed the Helsinki Declaration, and was approved by The Ethics Committee of the First Affiliated Hospital of Chongqing Medical University. Formal consent was obtained from all participants before we used their information, and this paper does not contain any feature that can be used to identify the subjects. All participants all provided their informed consent.

Human and Animal Ethics

Not applicable.

Consent for publication

All authors gave their consent for publication.

Availability of supporting data

We have uploaded the data in supplementary material.

Funding

This work was supported by Chongqing medical scientific research project (Joint project of Chongqing Health Commission and Science and Technology Bureau) (2021MSXM147) and Chongqing Medical University (ZHYX202128).

Authors' contributions

Qi Wang generated the research protocol and conducted literature search, data acquisition, and manuscript drafting.Yingjie Ma generated the research protocol and conducted the research modeling, literature search, data analysis, and manuscript drafting.Lei Hu generated the research protocol and conducted literature search, data acquisition, and manuscript drafting. And this work is reviewed by Lei Hu, Jian Mao, Jingyan Song, Mingzhao Xiao, Qinghua Zhao, Fang Yuan, and Haoding Wang.

Competing Interests

The authors report no declarations of interest.

Acknowledgments

This work was supported by Chongqing medical scientific research project (Joint project of Chongqing Health Commission and Science and Technology Bureau) (2021MSXM147) and Chongqing Medical University (ZHYX202128).

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data.csv

Factors influencing patients' intention to use hospital information system in the old-new transition period: A case study of Hospital Examination Reservation System

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Abstract

Objective

Background

Method

Results

Conclusion

Figures

Introduction

Materials And Methods

Theory and Hypothesis

Data collection

Data analysis

Results

Discussion

Limitations

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1