Journal of Health Care Communications Open Access

The Impact on Healthcare Service Quality and Patients Satisfaction via Adopting Mobile Health Technology: An empirical Study in a Public Ophthalmologic Hospital in China

Chenhui Jin^1#, Canghong Wang^2#, Xiaotong Zhu², Zhiying Kong¹ and Liang Zhu^{1* 1}Department of Public Health and Policy Management, Shenzhen Polytechnic, China
²Department of Public Health and Policy Management, Xi’an Peihua University, China
^#Equally contribution ^*Correspondence: Liang Zhu, Department of Public Health and Policy Management, Shenzhen Polytechnic, China, Email:

Received: 03-Oct-2022, Manuscript No. IPJHCC-22-14580; Editor assigned: 05-Oct-2022, Pre QC No. IPJHCC-22-14580 (PQ); Reviewed: 19-Oct-2022, QC No. IPJHCC-22-14580; Revised: 24-Oct-2022, Manuscript No. IPJHCC-22-14580 (R); Published: 31-Oct-2022, DOI: 10.36846/IPJHCC-7.10.70045

INTRODUCTION

Due to the huge population and lack of medical resources in current China, crowed and disorder hospital management usually makes the patients to experience the terrible clinical process, such as spending massive time checking in, queuing for payment, waiting for medical treatment etc., which easily rises up high conflicts of doctor-patient relationship and low service quality perception by patient [1,2]. As presented on China Internet Network Information Center latest version [3]. China’s Internet users have reached 854 million, meanwhile, mobile phone users have reached 847 million. Most of Chinese hospitals began to encourage patients to use mobile health technologies or applications (apps) for purpose of improving experience of the healthcare process, hospital service quality perception and patient satisfaction. Most of apps are designed for patients to download in their cell phones or tablet computers. In this way, the patients can access to information about the hospital and its medical practitioners, as well as consult, book and pay through mobile platforms. In terms of hospital management era, it will be an important manner for improving patient service quality perception and satisfaction.

In ophthalmologic hospital settings, there are a few studies regarding to the adoption and perception of health information technologies, such as electronic health records (EHRs) by ophthalmologists. In general, the adoption rate of EHRs is high in ophthalmological hospitals [4-6]. Although the EHRs is relatively low compared with that in other specialized hospitals [7], the results of the efficiency of workflow caused by EHRs use have not been consistently agreed by all researchers [4- 6,8]. Therefore, current literature does not involve any studies related to the effect of the mobile health apps use (MHAU) on service quality and patient satisfaction in ophthalmic hospitals. Furthermore, in other specialized or general hospitals settings, there is some conflicts and uncertainty about the mechanism by which patients using mobile health technologies to perceive service quality.

Therefore, this study focuses on exploring the impact of MHAU on patient satisfaction levels, in order to play the leading role of MHAU in promoting higher service quality levels in ophthalmologic hospitals. Specifically, according to the mobile health technologies and service quality literature, this study proposes that MHAU positively impacts patient satisfaction levels, while, MHAU actively drives clinical process change perceived by patient, and that has a positive impact on the service quality. Then, the three key factors impacting the MHAU levels were identified according to the literature of mobile health technologies, clinical process change perceived by patients, information accessibility, and physician-patient interaction. We initiated a survey of the patients at the Shenzhen ophthalmological Hospital between December 2018 and January 2019, and 618 validated questionnaires were collected, and the results support the research hypothesis presented, thus providing empirical support for the influence of MHAU and clinical process change perceived by patient on the patient satisfaction levels. The theoretical contributions of this study are included in two main points the first is to explore MHAU on clinical process change perceived by patient and its two dimensions (information accessibility and physician-patient interaction), enhance the digital age for the importance of MHAU, and for how mobile health technologies leading hospital digital innovation provides a theoretical explanation. The second is identified the key factors affecting patient satisfaction, for MHAU how to improve its patient satisfaction provides valuable theoretical and practical guidance.

Theoretical Basis

Using Health Information Technologies

It should be emphasized that hospital service quality dimensions are various in different scenarios [9]. More specifically, from patients’ point of view, they more focus on tangibility and assurance. However, in regards to adopting health information technologies, patients will concentrate on convenience and interaction rather than tangibility. Therefore, via enhancing the convenience and patients’ service perception, health information technologies may facilitate improving hospital service quality to a large extent. From interaction perspective, it emphasis on responsiveness and empathy. On the other hand, convenience refers to a hospital deliveries high standard service to scale patients’ demands via hardware and software supports, for instance, patients’ waiting time reduction, information inquiry easiness, payment convenience etc. Although, it is considered that convenience is more tangible based, it is not identical in this context. In particular, different authors may hold different views of convenience, such as convenience itself [10-12], patient flows [13] or waiting time [14,15]. Thus, convenience can be determined by patient clinic waiting time and clinical information accessibility. As a result, it can be suggested that convenience should be included in SERVQUAL or service quality dimensions, especially when considering patients’ using health information technologies to get efficient and extensive clinical process.

Dimensions and Factors Associated with Service Quality

In early 1990’s, service quality has been well defined and illustrated in many researches. Garvin [16] defined the key dimensions of service quality as serviceability, aesthetics, and perceived quality in the user based definition approach. In common scenarios, service quality could only be evaluated and measured during the interaction process between customer and service suppliers rather than before the interaction or as the interaction results neither [17,18]. Meanwhile, Gronroos [19] divided service qualities in two types: technical quality (i.e. what service is delivered) and functional quality (how the service is delivered). Furthermore, Parasuraman, Zeithaml and Berry [20] established the most influential concept model of SERVQUAL. It, as an analytic hierarchy, measures the service quality process and delivery against customers’ expectations and experience in ten dimensions, which had been modified to five dimensions via research advancement process [21]. These five dimensions are tangibility, reliability, responsiveness, assurance, and empathy. This developed conceptual model is widely used in further study explorations among various service industries.

Based on the original five dimensions of SERVQUAL, Babakus and Mangold [22] modified the SERVQUAL particularly adopt ed by healthcare sector. As a result, the revised model has been broadly adopted to investigate on patient perceived healthcare service quality [22]. With the emerging of published researches on SERVQUAL and the healthcare service quality [23-26], the two main streams of study has been formed which are dimensions of the services quality and the application of SERVQUAL to evaluate hospital service quality. Additionally, researchers assessed the quality of Iranian healthcare services that adopt SERVQUAL tool via meta-analysis, and expected to investigate the association between demographic information and SERVQUAL [26]. Pai and Chary [23] explored the diversity of SERVQUAL dimensions and methodology to develop structure. Meanwhile, Fatima [24] illustrated many sub-dimensions in accordance with service quality and proved that SERVQUAL is constantly the most common used conceptual model to evaluate the health service quality. Additionally, Talib, Azam and Rahman [27] combined the research on the service quality and the patients’ satisfaction.

Methodology

The Main Hypothesis

To establish and hypothesize the research framework for this study, we examined the relationships between variables of the MHAU and service quality in existing literature. Chang and Chang [28] used the term “technology based service encounters” to describe the interaction between medical practitioners and patients during clinical process by e-commerce and Internet technology, such as online appointment system (OAS) and EHRs, revealing positive effect of technology based service encounters service quality without effecting patient satisfaction. Perception of service quality has a positive impact on patient satisfaction via the adoption of technology based service. While in a study of effect of OAS on patient satisfaction, Wang, Cheng and Huang [29] claimed that OAS technology based service encounters, compared with the hospital image, had a stronger effect on patient satisfaction. Based on the above results, we hypothesize that:

H1a: MHAU has a positive effect on patient’s perception of service quality.

H1b: MHAU has a positive effect on patient satisfaction.

Technology acceptance has a significant effect on relationship quality which is defined as the outcome of interactions between both parties [30,31]. Wu, Li and Li [32] applied the construction of “interaction quality” to describe how the service is delivered. It can be explained as the patient’s cognition of service quality in communication with doctors and nurses. Generally, it claimed that interactive quality has a positive effect on overall experience quality [33]. In addition, Petter and Fruhling [34] indicated that the system usage, as a variable to measure use of STAT Pack™ (an information tool used to aid in the diagnosis of pathogens in hospitals), has positive effect on individual and organization. These two variables describe how effective and useful the individual and organization using STAT Pack™. The results suggest that the use of the technology or systems may result in the change of the interaction between patients and doctors, thereby affecting perceived service quality. Thus, in our study, we hypothesized that the way of the MHAU affecting the patient perceived service quality works by the mediating role of clinical process change perceived by patient. Consequently, based on the discussion above, we can draw a conclusion and put forward hypotheses as follows:

H1c: MHAU is positively push forward clinical process change perceived by patients.

H2: Clinical process change perceived by patients has positive effect on perceived service quality in hospitals.

In the investigation of relationship between service quality and patient satisfaction, some authors state that service quality directly effects on patient satisfaction [35-41], while others argue that service quality influences the patient satisfaction by mediating factors [42-45]. Faria and Mendes [46] confirmed that apart from the direct effect of service quality on patient satisfaction, the institutional reputation also fluctuate the relationship between service quality and patient satisfaction. Johnson and Russell [47] found the mediating role of healthcare provider and nurse/assistant within service quality and patient satisfaction. Thus, we hypothesize that:

H3: The perceived service quality is positively related to patient satisfaction.

Control Variable

To make the study more scientific, we identified four control variables: gender, age, education, and occupation. Usually, compared with the women, men is more rational and insensitive and more likely to satisfaction, they tend to simplify complex problems when they experience trouble. Compared with the older, the younger show skilled use in health information technologies, while the older tend to show fear for using these technologies, which can easily trigger dissatisfaction among older patients. The level of education is usually proportional with the ability to solve and identify problems, with the ability to proficient use in health information technologies. Patients’ occupation is significantly associated with skilled use of health information technology. Based on this, the gender, age, education, and occupation of the respondents were included as control variables that might influence patient satisfaction.

Research Model

Based on the above hypotheses, the research model was established, as illustrated in Figure 1.

Figure 1: Research model and hypotheses formulated in this study

Research Design

Questionnaire Design

By reviewing a large amount of literature, we directly introduced, selected, and modified the relevant mature scale to design variables and items of this study. The total number of scales is four, with eleven dimensions, which are: MHAU, clinical process change perceived by patient (dimensions included: physician-patient interaction, and information accessibility), service quality (dimensions included: tangibility, reliability, responsiveness, assurance, empathy, and convenience) and patient satisfaction (dimensions included: treatment outcome and visit time) with measurement of control variables, some demographic variables may have an impact on patient performance. Each questions of the scale and its’ source reference Table 1. Furthermore, the independent variable, MHAU, may be affected by the patient’s gender, age, education level, and occupation, thus indirectly or directly affecting the patient satisfaction. In addition, the above control variable may also have effects on the patient perceived clinical process change and service quality. Therefore, the gender, age, education, and occupation of the respondents were added to the measurement scale. The Likert 7-point recording method was adopted for each item, with 1 being “very disagree” and 7 being “very agree” [48].

Table 1: Source of the questions of each scale

Data Collection

In order to improve the validity of the questionnaire as much as possible, the paper version of the questionnaire was adopted. The patients were required to fill out the questionnaires as long as the medical consultation was approaching to the end on the paperwork. From December 19, 2018 to January 22, 2019, 647 questionnaires were issued, which involved 618 valid questionnaires. The rate of sample validity was 95.5%. The reasons for the invalid questionnaires are: the patients were impatient to answer questions which lead to the filling time is significantly lower than the rational and reasonable filling time of 20 minutes; the patients were very concentrating at the beginning, but they were interrupted by some emergency issue; some critical questions were neglected.

Empirical Test And Result Analysis

Demographic Analysis

The demographic information of patients who use mobile health apps is summarized in Table 2. The proportion of patient gender was 39% (male) and 61% (female), respectively. The majority of patients’ age were between 31 and 40, accounting for 46.3% of the total patients interviewed, followed by those ages between 21-30 and 11-20, accounting for 27% and 10.2%, showing that the group of patients using mobile health technologies were younger generation, and basically matched with the age distribution of overall patients in the hospital. The ratio of the patients who have the bachelor degree and diploma was the largest, accounting for 41% and 21.4% of the total, indicating that the education level of the targeted patients was generally high. From the perspective of occupation, the highest proportion was non-government related enterprises, such as foreign-funded enterprises, privately owned corporations and self-employed households, accounting for 59.5%. In summary, the distribution of the demographic information of the patient samples in this study is even and reasonable and can represent the overall patient population of the hospital.

Table 2: Summary of the demographic information of patients (N=618)

Measurement Reliability

We conducted the reliability test using Cronbach’s α-coefficient in this study. The α-coefficient is usually expected to be greater than 0.7, indicating that the reliability is acceptable [48]. To determine the factors that affect the reliability, we may calculate the corrected item total correlation (CITC) value. If the CITC is lower than 0.4 or the α-coefficient increases after deletion, we could consider deleting the corresponding question. Reliability analysis requires a separate analysis for each variable. The α-coefficients of the each variables or dimensions is greater than 0.7 (Table 3), indicated that the reliability of each variable is acceptable. The Cronbach’s coefficient of the subscale is higher than 0.8 (Table 3), and the Cronbach’s coefficient of the total scale is 0.846 (Table 3), indicating that it is relatively reliable.

Table 3: Reliability analysis of variables and dimensions

The results showed that the reliability of each variable and dimension was very high, and the CITC values of all the questions were greater than 0.4 and deleting any one of the questions did not improve the overall α-coefficient. Therefore, those 11 variables (including all dimensions), as well as 41 question items do not need to be modified or deleted, and its results are reliable.

Measurement Validity

The validity test included content validity and construct validity in this study. The content validity of the scale adopts the mature scale content, and is supported by a lot of theories. Meanwhile, five hospital management experts were invited to examine the content of the scale to ensure the content validity. The items used in this study mainly refer to the scales used by previous researchers, as well as the experience and opinions of experts, after revision, the contents completely conform to the conceptual description of relevant variables.

SPSS 24 statistical software was used for the exploratory factor analysis to verify the validity of the questionnaire structure in this study. Before exploratory factor analysis, KMO (Kaiser- Meyer-Olkin) statistics and Bartlett sphere test were used to determine whether the data were applicable for the factor analysis. If the KMO value is greater than 0.6, which is the general standard, and the p value of Bartlett spherical test is 0.000, less than 0.01, it indicates that the statistics is applicable for the factor analysis. Results of the structural validity analysis of MHAU, clinical process change perceived by patient (physician- patient interaction and information accessibility), service quality (tangibility, reliability, responsiveness, assurance, empathy, and convenience) and patient satisfaction (treatment outcome and visit time) are shown in Tables 4-7. Results of the factor analysis demonstrated that the co-relationship between the four variables and each factor basically met the professional requirements. The factor loading coefficient is all higher than 0.6, indicating that the structural validity of the questionnaire is attainable, and the data is valid.

Table 4: Construct validity analysis of mobile health apps use (MHAU)a

Table 5: Construct validity analysis of clinical process change perceived by patient a

Table 7: Construct validity analysis of service quality a

Correlation Analysis

Correlation analysis was conducted on the four variables and their dimensions, and all relevant results were summarized in Table 8. The correlation analysis showed that MHAU had the strongest correlation with information accessibility at the dimension level (correlation coefficient=0.813, p<0.01). Except for the moderate correlation with visit time (correlation coefficient= 0.401, p<0.01), the other factors were weak or no correlation. Apart from the strong correlation with MHAU, information accessibility was invisible or irrelevant to all other dimensions. There was a low correlation between physician-patient interaction and tangibility (correlation coefficient=0.368, p<0.01), and there was a moderate correlation between physician- patient interaction and other five dimensions of service quality. There was a weak correlation between the six dimensions of service quality and visit time. The most relevant element was convenience (correlation coefficient=0.396, p<0.01), while the six dimensions of service quality and treatment outcome were all moderately correlated.

Table 8: Correlation analysis of dimensions in research model

Regression Analysis and Validation of Model Hypothesis

Multiple linear regression analysis was used to verify the relationship between independent variables and dependent variables in this study. For the purpose of preventing the interference caused by the sample demographic information, the study included the patient’s age, gender, occupation and education level those were used as control variables in the model for analysis. The analysis of the relationship between model variables is conducted at two levels. The first level is among four variables, and the second level is between the dimensions of variables. Therefore, we established three groups of independent variables and dependent variables during the regression analysis. They are:

Group 1: Independent variables: MHAU; dependent variables: clinical process change perceived by patient (physician-patient interaction and information accessibility).

Group 2: Independent variables: MHAU, clinical process change perceived by patient (physician-patient interaction and information accessibility); dependent variables: service quality (tangibility, empathy, assurance, convenience, reliability and responsiveness).

Group 3: Independent variables: MHAU, service quality (tangibility, empathy, assurance, convenience, reliability and responsiveness); dependent variables: patient satisfaction (treatment outcome and visit time).

The results of the three groups at variables and dimensions level are shown in Tables 9.

Table 9: Regression analysis of clinical process change perceived by patient and its dimensions as dependent variable

In the regression analysis of clinical process change perceived by patient and its dimensions as dependent variable, MHAU had a positive effect on clinical process change perceived by patient (regression coefficient=0.456, p<0.01) while the other variables had no effect. Therefore, H1c should be accepted. In the regression analysis of the two dimensions of clinical process change perceived by patient, it was found that MHAU had a significant positive effect on both information accessibility (regression coefficient=0.717, p<0.01) and physician-patient interaction (regression coefficient=0.196, p<0.01).

In the regression analysis of service quality and its dimensions as dependent variable, it was found that the impact of MHAU on service quality is insignificant (P=0.665>0.05), nevertheless, it had a positive effect on tangibility exclusively (regression coefficient= 0.093, p=0.007<0.01). While physician-patient interaction had an extensive positive effect on the six dimensions of service quality, and information accessibility had a positive effect only on the reliability of service quality (regression coefficient= 0.065, P=0.045<0.05). Thus, the null H1a is rejected unverifiable and alternative H2 is accepted.

In the regression analysis of patient satisfaction and its dimensions as dependent variable, the regression coefficients of MHAU and service quality as independent variables were 0.162 and 0.784, respectively. The p=0.01 confidence level was significant, indicating that the above two variables could impact patient satisfaction positively. Therefore, the H1b and H3 will be accepted.

In the study of the influence of various dimensions of service quality on patient satisfaction, we found that tangibility (regression coefficient=0.185, P=0.000<0.01), empathy (regression coefficient=0.192, p=0.000<0.01), convenience (regression coefficient=0.120, p=0.000<0.01), reliability (regression coefficient=0.133, p=0.006<0.01), have positive effect on patient satisfaction, while the influence of assurance (regression coefficient=0.068, p=0.168>0.01) and responsiveness (regression coefficient=0.042, p=0.535>0.01) is null.

In the study of the influence of MHAU and the dimensions of service quality on the treatment outcome and visit time, it is found that MHAU, tangibility and empathy had significant positive effect on the treatment results and the treatment time; assurance and reliability only had a positive effect on the treatment outcome, but have no significant effect on visit time; convenience only had a significant effect on visit time, while there was no effect on treatment outcome; responsiveness had no effect on both treatment outcome and visit time.

Discussion

Empirical Finding

The study found that the MHAU had no impact on perceived service quality but has a positive effect on its tangible dimension, patient satisfaction and both of its two dimensions. This result is consistent with the contemporary studies on the impact of technology based physician-patient service contact on patient satisfaction, as well as the research on the use of mobile information communication devices on patient satisfaction [29,49]. In this research it is demonstrated that the MHAU has a positive effect on patient satisfaction but has no effect on perceived service quality as a result of the conceptual differences in satisfaction and service quality. Satisfaction is the overall evaluation of the hospital’s service through the actual experience. This feeling is temporary and immediate. The perception of service quality is the patient’s understanding of the hospital service content. The understanding is constant, not necessarily access through personal experience. Experiences, such as patients who think that the professional level of a doctor in a hospital or the overall service quality of a hospital is high, can be obtained through notification or publicity of others, advanced equipment technology (both medical devices and digital clinical process) or architecturally well designed hospitals. This further illustrated that the medical software, which is a tangible factor of service quality, has a positive effect. To explain more profoundly, the use of health information technologies belongs to the patient’s personal experience, and the perception it generates could be more closely related with satisfaction than the quality of service. This can also be confirmed by the correlation analysis results.

The study also found that the MHAU has a positive effect on clinical process change perceived by patient and its two dimensions, which is corresponded to the results of Petter and Fruhling [34] in studying the changes in clinical processes perceived pathogens in hospitals. However, only physician-patient interaction has a significant positive effect on service quality and its dimensions, which is consistent with Wu, Li and Li [32] finding that is physician-patient interaction has a positive effect on patient service quality. In addition, since the MHAU has no effect on the quality of service, this indicates that the change in the patient’s experience in the clinical process is an intermediate variable affecting the quality of service by MHAU. That is, the use of health information technologies initially changes the patient’s experience in the clinical process, then the patient’s perception of quality of hospital service, and the investment of health information technologies itself will not lead to an increase in perception of service quality. This view could explain why adoption of the EHRs in the ophthalmological hospitals did not enhance the efficiency of workflow [7], because due to the lack of meaningful interaction between physicians and patients [6], which gave rise to the undesirable patient experience despite how much of health information technologies that hospitals explored. In addition, the study confirms that service quality has a positive effect on patient satisfaction, which is consistent with the results of many researchers [50-56].

Theoretical Contribution

From the theoretical point of view, firstly, in terms of poor medical environment in Chinese hospitals, it is proposed that patients use health information technologies to influence the quality of service through the perceived changes of clinical process as an intermediate variable, which has never been reported in previous literature. Secondly, the study proposes to facilitate convenience to the dimension of hospital service quality, which has been verified and supplemented the original five dimensions of SERVQUAL scale.

Practical Significance

From the practical perspective, firstly, when hospital initiates to invest in new equipment or system, they should not only focus on the introduction of system use, but also, more importantly, the changes of patients’ experience in the clinical process after using systems or apps. Especially, the perception of change in initiatives of physician-patient interaction concerns patients are one of the most influential factors. Therefore, hospitals may choose to establish a better way to promote user interface friendly systems for both medical staff and patients by easily sharing messages, pictures, videos and other medical information. This point of view is also confirmed in the study that the negative perception of EHRs (i.e. electronic health records) productivity outcomes in the ophthalmological hospital can be improved by optimizing the usability of systems [6]. Secondly, in regards to hospitals with large outpatient visits in China, the evaluation of hospital service quality should be considered for convenience added in to the five dimensions commonly used for SERVQUAL. In this study, it is found that hospitals with larger patient’s populations and patients in larger hospitals are more concerned about convenience. Two hospitals mentioned in literature have a capacity of 880 and 600 beds, which can be considered as large hospitals [57,58]. The patients consider convenience as the primary factor affecting service quality. Specifically, prominent hospitals always bear excessive amount of patients’ population, which leads to the inefficient clinic process and poor service quality. As the research object, Shenzhen Ophthalmology Hospital is a public hospital in Shenzhen, China, which obtained more patients gathering for clinical treatment, with over 400,000 outpatient volume. During the investigation, patients in Shenzhen Ophthalmology Hospital concern more about the convenience in their entire clinical procedure. Therefore, in this context, convenience may be dimensions that have a greater effect on service quality perception.

Limitations and Future Research Directions

This study obviously has some limitations that could be improved in the further research in terms of model establishment, sample selection and research methods. The research model mainly explores the influence mechanism of MHAU on patient satisfaction but does not consider the relevant factors that affect the use of MHAs, such as the patient’s ability to acquire knowledge and technology, social influencing factors, and perceived features of mobile technology. Additionally, the research object is only one public hospital included, SOH, so the representativeness and comprehensiveness of the research results could be improved by involving other general hospitals to conduct comparative studies between public and private hospitals or by considering comparative studies of similar hospitals in different regions (Supplementary file).

Conclusion

In regards to this study, the MHAU directly and significantly affects patient satisfaction, but it has no direct effect on the hospital service quality. Nevertheless, it affects the variable of perceived changes of clinical process by patients at first, then the hospital service quality. Physician-patient interaction is an important factor that positively affects the quality of hospital service. Convenience is one of the important dimensions for patients to perceive the quality of service in Chinese hospitals, and it has a positive and significant effect on patient satisfaction. Furthermore, hospital management level should not only concentrate on the management system usage, but also on the organizational factors that affect the results of system usage, such as the perceived clinical process change by patients. When evaluating the quality of hospital service, especially in terms of large scale hospitals with large outpatient service range, the convenience dimension should be considered. Furthermore, managers should focus on enhancing the initiative of physician- patient interaction, as it is an important factor affecting patients’ perception of hospital service quality. Generally, this study provides evidence that can be referred in other investigations in a similar context and also need to be confirmed by future researchers in other hospital settings.

Ethics Approval and Consent to Participate

The study was approved by the School of Medical Technology and Nursing (Shenzhen) research ethics committee. As a questionnaire survey was conducted in the study that informed consent was obtained from all interviewees in the manuscript. All methods were carried out in accordance with relevant guidelines and regulations in the ethical approval and consent to participate section of manuscript.

Consent for Publication

Not applicable.

Availability Statement Of Data And Material

The datasets used and/or analyzed during the current study available from the first or corresponding author on reasonable request.

Competing Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by a grant for the impact of mHealth technology on service quality provided by Department of Education of Guangdong Province (No. 2019GWTSCX126).

Authors' Contributions

CJ and CW wrote the study design and the manuscript. CJ conducted literature review and CJ and CW analyzed the data of literature and wrote the paper, LZ and KZ refined the research design, revised, reviewed manuscript and made final editing, XZ was responsible for language correction and revision, and participated in the data analysis, and all authors agreed with the manuscript’s results and conclusions.

Acknowledgements

We would like to thank Elsa Cardoso professor who she was doctoral supervisor of the first author (Chenhui Jin), and she came from Business School, Iscte University Institute of Lisbon, Portugal, for her contribution to the study, and thank Weidong Xia professor who he was co-supervisor of the first author, and he came from College of Business Administration, Florida International University, Miami, FL, USA, for his contribution. We also would like thank all participants who participated in the pilot study and research interviews. Meanwhile, we deeply appreciate your consideration of our manuscript, and we look forward to receiving comments from the reviewers.

References

1. Zhang Q, Li J, Zhang L (2019) Discussion on "Three Long and one Short" problems in outpatient service in hospital. Wisdom Health. 16:22-23.

   [Crossref]

2. Zhang P, Hu S, Hu Y (2019) Application practice of outpatient procedure optimization based on informationization. Medical Information. 16:20-22.

   [Crossref] [Google Scholar]

3. Discussion on "Three Long and one Short" problems in outpatient service in hospital
4.  Wakamiya S, Yamauchi K (2013) Evaluation of electronic medical records for ophthalmology based on changes in staff work through introduction of electronization. Stud Health Technol Inform. 192(1):968-975.

   [Crossref] [Google Scholar]

5. Boland MV, Chiang MF, Lim MC, Wedemeyer L, Epley KD, et al. (2013) Adoption of electronic health records and preparations for demonstrating meaningful use: An American academy of ophthalmology survey. Ophthalmology. 120(8):1702-1710.

   [Crossref] [Google Scholar]

6. Ehrlich JR, Michelotti M, Blachley TS, Zheng K, Couper MP, et al. (2016) A two-year longitudinal assessment of ophthalmologists' perceptions after implementing an electronic health record system. Appl Clin Inform. 07(04):930-945.

   [Crossref] [Google Scholar]

7. Lim MC, Boland MV, McCannel CA, Saini A, Chiang MF, et al. (2018) Adoption of electronic health records and perceptions of financial and clinical outcomes among ophthalmologists in the United States. JAMA Ophthalmol. 136(2):164-170.

   [Crossref] [Google Scholar]

8. Chiang MF, Boland MV, Margolis JW, Lum F, Abramoff MD, et al. (2008) Adoption and perceptions of electronic health record systems by ophthalmologists: An American Academy of Ophthalmology survey. Ophthalmology. 115(9):1591-1597.e5.

   [Crossref] [Google Scholar]

9. Pai YP, Chary ST (2013) Dimensions of hospital service quality: A critical review: Perspective of patients from global studies. Int J Health Care Qual Assur. 26(4):308-340.

   [Crossref] [Google Scholar]

10. Akter S, D’Ambra J, Ray P, Hani U (2013) Modelling the impact of mHealth service quality on satisfaction, continuance and quality of life. Behav Inf Technol. 32(12):1225-1241.

    [Crossref] [Google Scholar]

11. Akter S, D ’ambra J, Ray P (2013) Development and validation of an instrument to measure user perceived service quality of mHealth. Inf Manag. 50(4):181-195.

    [Crossref] [Google Scholar]

12. Chang HH, Chang CS (2008) An assessment of technology-based service encounters & network security on the e-health care systems of medical centers in Taiwan. BMC Health Serv Res. 8(1):1-13.

    [Crossref] [Google Scholar]

13. Johnson DM, Russell RS (2017) SEM of service quality to predict overall patient satisfaction in medical clinics: A case study. Qual Manag J. 22(4):18-36.

    [Crossref] [Google Scholar]

14. Reidenbach RE, Sandifer-Smallwood B (1990) Exploring perceptions of hospital operations by a modified SERVQUAL approach. J Health Care Mark. 10(4):47-55.

    [Google Scholar] [PubMed]

15. Faria N, Mendes L (2013) Organizational image’s partial mediation role between quality and users’ satisfaction. Serv Ind J. 33(13-14):1275-1293.

    [Crossref] [Google Scholar]

16. Garvin DA (1984) What Does “Product Quality” Really Mean? Sloan Manage Rev. 26(1):25-43.

    [Google Scholar]

17. Zeithaml VA (1981) How consumer evaluation processes differ between goods and services. Mark Serv. 9(1):186-190.

    [Google Scholar]

18. Lehtinen U, Lehtinen JR (1982) Service quality: A study of dimensions. Unpubl Work Pap Serv Manag Institute. 439-460.

    [Google Scholar]

19. Grönroos C (1984) A service quality model and its marketing implications. Eur J Mark. 18(4):36-44.

    [Crossref] [Google Scholar]

20. Parasuraman A, Zeithaml VA, Berry LL (1985) A conceptual model of service quality and its implications for future research. J Mark. 49(4):41-52.

    [Crossref] [Google Scholar]

21. Parasuraman A, Zeithaml VA, Berry LL (1988) SERQUAL: A multiple-item scale for measuring consumer perceptions of service quality. J Retail. 17(4):375-390.

    [Google Scholar]

22. Babakus E, Mangold WG (1992) Adapting the SERVQUAL scale to hospital services: An empirical investigation. Health Serv Res. 26(6):767-786.

    [Crossref] [Google Scholar]

23. Pai YP, Chary ST (2013) Dimensions of hospital service quality: A critical review: Perspective of patients from global studies. Int J Health Care Qual Assur. 26(4):308-340.

    [Crossref] [Google Scholar]

24. Fatima I, Humayun A, Iqbal U, Shafiq M (2019) Dimensions of service quality in healthcare: A systematic review of literature. Int J Qual Heal Care. 8(4):1121-1134.

    [Crossref] [Google Scholar]

25. Rezaei S, Hajizadeh M, Zandian H, Fathi A, Nouri B (2018) Service quality in Iranian hospitals: A systematic review and meta-analysis. Med J Islam Repub Iran. 32(1):249-253.

    [Crossref] [Google Scholar]

26. Teshnizi SH, Aghamolaei T, Kahnouji K, Teshnizi SMH, Ghani J (2018) Assessing quality of health services with the SERVQUAL model in Iran. A systematic review and meta-analysis. Int J Qual Heal Care. 30(2):82-89.

    [Crossref] [Google Scholar]

27. Talib F, Azam M, Rahman Z (2015) Service quality in healthcare establishments: A literature review. Int J Behav Healthc Res. 5(1/2):1-24.

    [Crossref] [Google Scholar]

28. Chang HH, Chang CS (2008) An assessment of technology-based service encounters & network security on the e-health care systems of medical centers in Taiwan. BMC Health Serv Res. 8:87-95.

    [Crossref] [Google Scholar]

29. Wang WT, Cheng SY, Huang LY (2013) Technology-based service encounters using self-service technologies in the healthcare industry. Int J Hum Comput Interact. 29(3):139-155.

    [Crossref] [Google Scholar]

30. Chen SC, Liu SC, Li SH, Yen DC (2013) Understanding the mediating effects of relationship quality on technology acceptance: An empirical study of e-appointment system. J Med Syst. 37(6):9981-9986.

    [Crossref] [Google Scholar]

31. Crosby LA, Evans KR, Cowles D (1990) Relationship quality in services selling: An interpersonal influence perspective. J Mark. 54 (3):68-81.

    [Crossref] [Google Scholar]

32. Wu HC, Li T, Li MY (2016) A study of behavioral intentions, patient satisfaction, perceived value, patient trust and experiential quality for medical tourists. J Qual Assur Hosp Tour. 17(2):114-150.

    [Crossref] [Google Scholar]

33. Brady MK, Cronin JJ, Brand RR (2002) Performance-only measurement of service quality: A replication and extension. J Bus Res. 55(1):17-31.

    [Crossref] [Google Scholar]

34. Petter S, Fruhling A (2011) Evaluating the success of an emergency response medical information system. Int J Med Inform. 80(7):480-489.

    [Crossref] [Google Scholar]

35. Reidenbach RE, Sandifer-Smallwood B (1990) Exploring perceptions of hospital operations by a modified SERVQUAL approach. J Health Care Mark. 10(4):47-55.

    [Google Scholar]

36. Andaleeb SS (2001) Service quality perceptions and patient satisfaction: A study of hospitals in a developing country. Soc Sci Med. 52(9):1359–1370.

    [Crossref] [Google Scholar]

37. Geertzen JHB, Gankema HGJ, Groothoff JW, Dijkstra PU (2002) Consumer satisfaction in prosthetics and orthotics facilities. Prosthet Orthot Int. 26(1):64-71.

    [Crossref] [Google Scholar]

38. Lee MA, Yom YH (2007) A comparative study of patients’ and nurses’ perceptions of the quality of nursing services, satisfaction and intent to revisit the hospital: A questionnaire survey. Int J Nurs Stud. 44(4):545-555.

    [Crossref] [Google Scholar]

39. Yang-Kyun K, Chul-Ho C, Seo-Kyu A, In-Ho G, Han-Joong K (2008) A study on medical services quality and its influence upon value of care and patient satisfaction-Focusing upon outpatients in a large-sized hospital. Total Qual Manag Bus Excell. 19(11):1155-1171. 

    [Crossref] [Google Scholar]

40. Akter S, D’Ambra J, Ray P, Hani U (2013) Modelling the impact of mHealth service quality on satisfaction, continuance and quality of life. Behav Inf Technol. 32(12):1225-1241.

    [Crossref] [Google Scholar]

41. Akter S, D ’ambra J, Ray P (2013) Development and validation of an instrument to measure user perceived service quality of mHealth. Inf Manag. 50(4):181-195.

    [Crossref] [Google Scholar]

42. Aliman NK, Mohamad WN (2013) Perceptions of service quality and behavioral intentions : A mediation effect of patient satisfaction in the private health care in Malaysia. Int J Mark Stud. 5(4):15-29.

    [Google Scholar]

43. Panchapakesan P (2015) Customer satisfaction in Indian hospitals: Moderators and mediators. J Qual Manag. 22(1):10-29.

    [Crossref] [Google Scholar]

44. Meesala A, Paul J (2016) Service quality, consumer satisfaction and loyalty in hospitals: Thinking for the future. J Retail Consum Serv. 40:1-9.

    [Crossref] [Google Scholar]

45. Kim CE, Shin JS, Lee J, Lee JY, Me-Riong K, et al. (2017) Quality of medical service, patient satisfaction and loyalty with a focus on interpersonal-based medical service encounters and treatment effectiveness: A cross-sectional multicenter study of complementary and alternative medicine (CAM) hospitals. BMC Complement Altern Med. 17(1):174-181.

    [Crossref] [Google Scholar]

46. Faria N, Mendes L (2013) Organizational image’s partial mediation role between quality and users’ satisfaction. Serv Ind J. 33(13-14):1275-1293.

    [Crossref] [Google Scholar]

47. Johnson DM, Russell RS (2015) SEM of service quality to predict overall patient satisfaction in medical clinics: A case study. Qual Manag J. 22(4):18-36.

    [Crossref] [Google Scholar]

48. Joshi A, Kale S, Chandel S, Pal DK (2015) Likert scale: Explored and explained. Br J Appl Sci Technol. 7(4):396-403.

    [Google Scholar]

49. Jen WY, Chao CC, Hung MC, Li YC, Chi YP (2007) Mobile information and communication in the hospital outpatient service. Int J Med Inform. 76(8):565-574.

    [Crossref] [Google Scholar]

50. Babakus E, Mangold WG (1992) Adapting the SERVQUAL scale to hospital services: An empirical investigation. Health Serv Res. 26(6):767-786.

    [Google Scholar]

51. Panchapakesan P, Rajendran CLPS (2009) A conceptual framework of service quality in healthcare: Perspectives of Indian patients and their attendants. Benchmarking An Int J. 16:157-191.

    [Google Scholar]

52. Itumalla R, Acharyulu GVR, Shekhar BR (2014) Development of hospitalqual: A service quality scale for measuring in-patient services in Hospital. Oper Supply Chain Manag An Int J. 7(2):54-63.

    [Crossref] [Google Scholar]

53. Rakhmawati T, Sumaedi S, Astrini N (2014) ISO 9001 in health service sector: A review and future research proposal. Int J Qual Serv Sci. 6(1):17-29.

    [Crossref] [Google Scholar]

54. Lonial S, Menezes D, Tarim M, Zaim S (2010) An evaluation of SERVQUAL and patient loyalty in an emerging country context. Total Qual Manag Bus Excell. 21(8):813-827.

    [Crossref] [Google Scholar]

55. Anbori A, Ghani SN, Yadav H, Daher AM, Su TT (2010) Patient satisfaction and loyalty to the private hospitals in Sana’a, Yemen. Int J Qual Heal Care. 22(4):310-315.

    [Crossref] [Google Scholar]

56. Spreng RA, MacKenzie SB, Olshavsky R W (1996) A reexamination of the determinants of consumer satisfaction. J Mark. 60(3):15-32.

    [Crossref] [Google Scholar]

57. Cegarra-Navarro J G, Wensley A K P, Sánchez-Polo MT (2011) Improving quality of service of home healthcare units with health information technologies. Heal Inf Manag J. 40(2):30-37.

    [Crossref] [Google Scholar]

58. Chang MY, Pang C, Michael Tarn J, Tai-Shun L, Yen DC (2015) Exploring user acceptance of an e-hospital service: An empirical study in Taiwan. Comput Stand Interfaces. 38:35-43.

    [Crossref] [Google Scholar]