The Impact of Expected Waiting Time on Pediatric Outpatient Satisfaction: A Behavioral Experiment Study

The Impact of Expected Waiting Time on Pediatric Outpatient Satisfaction: A Behavioral Experiment Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Impact of Expected Waiting Time on Pediatric Outpatient Satisfaction: A Behavioral Experiment Study Hui Zhang, Junhua Tian, Yu Shi, Yuping Qian, Xuan Gao, Xiaowen Zhai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4929205/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 May, 2025 Read the published version in BMC Health Services Research → Version 1 posted 4 You are reading this latest preprint version Abstract Background: Outpatient departments in tertiary children's hospitals in China are often overcrowded, with parents frequently voicing concerns about prolonged waiting times. While substantial efforts have been directed towards reducing actual waiting times (AWT), managing the expectations of parents has received limited attention. This study employs a behavioral experiment to investigate the relationship between expectations and satisfaction levels. Method: The experiment consisted of two groups: a control group and an experimental group. Initially, the baseline expected waiting times (EWT) for subjects in both groups were obtained, along with demographic information including age, education level, gender, and medical experience. Unlike the control group, subjects in the experimental group received reminders about waiting times and subsequently adjusted their EWT accordingly. This study employed non-parametric tests and variance tests to analyze the differences in satisfaction levels between the two groups of subjects. Ethical approval for this project was obtained from the hospital ethics committee. Result: Significant differences in satisfaction levels were observed between the control group and the experimental group when the AWT exceeded the EWT ( P =0.042, Z =-2.035). However, when the AWT was equal to or shorter than the EWT, no significant disparity in satisfaction levels emerged between the two groups ( P =0.230, Z =-1.200; Z =-1.416, P =0.157). Within the experimental group, a significant difference in satisfaction was noted during peak hours between subjects effectively regulated by EWT and those not effectively regulated (x 2 =24.865, P =0.000). Conversely, during off-peak hours, there was no significant distinction in satisfaction between those effectively regulated by EWT and those not effectively regulated (x 2 =0.535, P =0.765). Conclusion: When the AWT exceeds the EWT, providing advance notice of long waiting time can extend patients EWT and significantly enhance their satisfaction. However, when the AWT is equal to or less than the EWT, the impact of advance notice of long waiting time on patient satisfaction is not statistically significant. During peak visiting hours, when alerts about longer waiting times are issued, patients who effectively adjust their EWT exhibit significantly higher satisfaction levels compared to those who do not make effective adjustments to their EWT. Conversely, during non-peak visiting hours, there is no significant difference in satisfaction levels between subjects who effectively regulate their EWT and those who do not. Healthcare institutions can adjust patients' EWT by informing them in advance about potential waiting times according to the temporal patterns of outpatient visitation numbers during peak hours. This approach mitigates negative emotions associated with prolonged waiting times and represents one of the effective methods to enhance the quality of medical services. Introduction Patient satisfaction serves as a critical indicator for evaluating healthcare service quality [ 1 – 3 ]. Numerous studies have demonstrated that satisfaction levels decrease as waiting times increase[ 4 ], with patients often perceiving waiting as a wasteful use of their time [ 5 , 6 ]. This issue is particularly pronounced in the crowded outpatient clinics of China’s tertiary children's hospitals, especially during peak flu season [ 7 ]. Addressing this challenge can be approached through two primary strategies. The first involves reducing the actual waiting time (AWT) by streamlining appointment processes, such as implementing effective scheduling systems [ 8 – 11 ] and lean methodologies [ 12 ]. The second strategy focuses on managing the psychological experience of waiting [ 13 – 15 ]. Research suggests that forewarning patients about potential delays can improve their satisfaction by adjusting their expected waiting time (EWT) [ 16 ]. This insight offers a novel approach to enhancing patient satisfaction.In practice, hospital administrators may wonder to what extent adjusting patients' EWT can maximally enhance patient satisfaction. Further study show that aligning the patient's EWT more closely with the AWT has been shown to increase satisfaction [ 17 ]. This finding provides clear direction for hospital administrators in enhancing patient satisfaction through adjusting expected waiting times. However, on one hand, although the study was conducted in a hospital with patients as subjects, their satisfaction based on EWT and AWT was preset by researchers according to the needs of the study. If patients were to report their own EWT and adjusted EWT in a hospital, combined with their AWT, would their satisfaction still be altered by the updated waiting time information provided in advance? On the other hand, hospital visitation numbers are divided into peak and off-peak periods according to volume, especially in China's tertiary children's hospitals where there is a significant increase in pediatric visits during flu season, leading to correspondingly longer wait times for pediatric patients and their parents. In the face of this reality, with distinct peak and off-peak periods, can adjusting patients' EWT significantly improve the satisfaction of pediatric patients and their families? Based on the aforementioned issues, this study will explore how patient satisfaction is affected when patients report their own EWT and adjusted EWT in a hospital, combined with their AWT. Specifically, the research will comparing patient satisfaction between control and experimental groups in three scenarios: when AWT exceeds the EWT, when AWT aligns with the EWT, and when AWT falls short of the EWT duration. Furthermore, we will also investigate the variation in satisfaction levels among participants in the experimental group during peak and off-peak visiting hours, after informing them that a longer wait time might be expected due to a higher number of patients. The innovative aspects of this study are primarily reflected in three areas. First, it aims to bridge the gap between theoretical advancements and practical applications; second, it considers the joint impact of AWT and EWT on satisfaction when examining the influence of EWT. Third, the research separately studies the effects of informing waiting times in advance during peak and off-peak periods to enhance patient satisfaction, aligning more closely with the practical needs of healthcare management. Methods 2.1 Experiment Design The experiment is structured into two distinct groups: the control group and the experimental group. In the initial segment of the experiment, both groups underwent inquiries regarding the initial EWT, alongside capturing fundamental information such as age, education level, gender, and medical history. The distinguishing factor between the two groups lies in the implementation of waiting time reminders. In contrast to the control group, the experimental group receives reminders based on the AWT. Following this intervention, the experimental group provides adjusted EWT information. This segment of the experiment is completed upon the patient's entry into the consultation room. Based on the number of visits, outpatient visits in hospitals are divided into peak and off peak periods. And based on the past number of patients, the hospital's peak hours are from 8:31 to 11:00 in the morning and from 13:31 to 15:30 in the afternoon, except for off-peak hours. Referring to the AWT data from July, we utilize the median AWT of 30 minutes during peak hours and 16 minutes during off-peak hours as prognostic indicators for the EWT within the experimental group. Effective adjustment is defined as subjects aligning their expectations to approximately 30 minutes following the receipt of information indicating a 30-minute waiting time. As an illustration, if an individual initially expects a waiting time of 15 minutes, and upon receiving information adjusts the EWT to 40 minutes, then the disparity between the initial EWT and the 30-minute prompt message is 15 minutes, while the variance between the adjusted EWT and the prompt message is 10 minutes. The latter, being more closely aligned with the timing of the prompt information, is regarded as effective adjustment. The subsequent part of the experiment, applicable to both the control and experimental groups, is administered as subjects are on the verge of entering the consultation room at the designated number. This sequential approach aims to capture real-time insights into parents of pediatrics’ experiences and expectations, enabling a thorough examination of the impact of waiting time reminders on adjusted EWT and overall satisfaction. Both the control and experimental groups were assigned satisfaction scores corresponding to their AWT upon being summoned. Satisfaction was gauged on a scale ranging from 0 to 100, with "0" denoting utmost dissatisfaction and "100" indicating maximum satisfaction. A higher score signifies a heightened level of contentment. Given that a significant portion of the hospital's patients make online appointments and can settle registration fees through mobile payments, these pre-arrival tasks can be efficiently completed at home or en route to the hospital. Consequently, for the purposes of this study, the AWT encompasses the temporal interval between a parents of pediatrics’ check-in and the subsequent call time after arriving at the hospital. The implementation schedule for this study involves the control group undergoing the intervention in July, while the experimental group experiences the same in August. This strategic timing is grounded in past observations, indicating a peak period of outpatient visits in pediatric hospitals during the summer months of July and August. 2.2 Experiment subjects The subjects of this study were patients and their parents who visited the pediatric hospital of the endocrine clinic in July and August 2023. Our investigation is anonymous and self-managed by the patients and their parents. The formula of sample size is expressed as follows: \(\:\:n={\text{Z}}^{2}\text{P}(1-\text{P})/{\text{E}}^{2}\) , where \(\:n\) is the minimum sample size; Z is the normal standard deviation at a 95% confidence level, which is 1.96, and P is the prevalence of the factor in the study, which was determined to be 80% based on previous studies [ 13 ]. 2.3 Experiment implementation The experiment implementation consists of two sequential steps. The initial segment is undertaken as soon as the patients and their parents arrives in the waiting room, while the second part comprises a single question that is addressed when the patients and their parents is summoned to the consultation room. In the initial phase, during the administration of the control group, patients and their parents are presented with the first segment of the questionnaire upon signing in and entering the waiting area. The entire procedure, encompassing the explanation of informed consent, clarification of research objectives, and notification of completion guidelines, spans approximately 6 minutes. When the patients or their parents completes this part of the questionnaire, the staff records their registration code, date of visit, and check-in time on the back. This part of the questionnaire is temporarily stored in the patients and their parents’ hands. The implementation of the experimental group is basically the same as that of the control group, with the only difference being that for patients visiting during peak hours and off-peak hours, patients and their parents will receive information about possible waiting times during peak and off-peak periods when filling out the questionnaire, and patients and their parents will be given corresponding adjusted EWT. During the second phase, as patients and their parents on the verge of entering the clinic reach the entrance, staff members collect the initial segment of the questionnaire from the patients and their parents. Subsequently, the staff calculates the disparity between the call time and check-in time, representing the AWT. Inquiring about the patients and their parents’ satisfaction score in light of this AWT scenario, the staff promptly records the response. This segment of the questionnaire can be concluded efficiently within a minute. 2.4 Variables Demographic variables The demographic variables included gender, age, hospital history, and education level. EWT Initial EWT: EWT that is not affected by time information interference. Adjusted EWT: In the experimental group, the EWT adjusted after obtaining information about waiting time. AWT The AWT was defined as the difference between the time of entering the clinic and the time of sign in. Satisfaction level For the evaluation of satisfaction, the patients and their parents was asked to choose a score from“0” to “100” randomly to represent their views on the time of the visit, in which “0” means very dissatisfied, and “100” means very satisfied. 2.5 Ethical consideration The research was performed in accordance with the Declaration of Helsinki. The experimental has been approved by the Research Ethics Committee of Children’s Hospital of Fudan University [No. (2023) 152]. Informed consent was obtained from all study participants before the implementation of the experiment. 2.6 Statistical methods The data analysis was carried out utilizing IBM SPSS Statistics 22.0 software. Given the non-normal distribution of waiting time, Kolmogorov-Smirnov tests and Mann-Whitney tests for two samples were employed to scrutinize potential significant differences in initial waiting time, AWT, or satisfaction scores between the control group and the experimental group. A p -value below 0.05 is indicative of a significant difference. Furthermore, the Chi-square test was employed to assess variations in satisfaction levels between peak and off-peak hours, complemented by the application of descriptive statistical methods. Results 3.1 Baseline characteristics A total of 2020 subjects participated in the experiment, and one subject in the experimental group had missing key data. The effective sample size was 2019, with 1013 in the control group and 1006 in the experimental group. There were 666 (66.2%) and 729 (72.0%) females in the experimental and control groups, respectively (Table 1 ). The experimental and control groups had 637 (63.3%) and 677 (66.8%). The subjects aged between 18 and 36 years, respectively. 708 (69.9%) and 773 (76.8%) of the subjects in the experimental and control groups received college or undergraduate education, respectively. Most of the two groups of subjects have visited this hospital for treatment, with 891 (88.0%) subjects in the control group and 769 (76.4%) subjects in the experimental group. Table 1 Basic information of the subjects. Control group Experimental group Total 1013 1006 Gender Male 284(28.0%) 340(33.8%) Female 729(72.0%) 666(66.2%) Age Below 18 years old 4(0.0%) 0(0.0%) Between 18–36 years old 677(66.8%) 637(63.3%) Above 36 years old 332(32.8%) 369(36.7%) Education High school or below 191(18.9%) 178(17.7%) Diploma or undergraduate 708(69.9%) 773(76.8%) Postgraduate 114(11.3%) 55(5.5%) Visit history Yes 891(88.0%) 769(76.4%) No 122(12.0%) 237(23.6%) 3.2 The impact of adjustment of EWT on satisfaction Categorization of subjects within the control group and experimental group was performed by comparing their AWT with the initially anticipated waiting time. This classification yielded three distinct categories: a. Subjects with Longer AWT than EWT (T 0 -T a < 0): This group comprises subjects whose AWT exceeded the initially expected duration. b. Subjects with Equal EWT and AWT (T 0 -T a = 0): The second category encompasses subjects for whom the anticipated waiting time aligned precisely with the AWT. c. Subjects with Shorter AWT than EWT (T 0 -T a > 0): The third group includes subjects whose AWT proved shorter than initially projected. As depicted in Table 2 , a notable disparity in satisfaction levels emerged between the control group and the experimental group when the AWT exceeded the EWT ( P = .042, Z =-2.035). Conversely, when subjects experienced an AWT equal to the initially expected duration, no significant difference in satisfaction levels was observed between the two groups ( P = .230, Z =-1.200). Similarly, when the AWT proved shorter than the EWT, no significant difference in satisfaction levels was detected between the two groups ( Z =-1.416, P = .157). Table 2 Satisfaction scores in the control and experimental groups Conditions Control group Experiment group Case Scores Case Scores T 0 -T a 0 602 100.0 [100.0, 100.0] 599 100.0 [100.0, 100.0] T 0 , before updated waiting time information (UI) was given; T a , AWT a Scores in the control group vs. scores in the experimental group, P < 0.05 When comparing cases where the AWT surpassed the initial EWT, no significant disparities emerged between the experimental and control groups concerning both the AWT ( Z =-0.478, P = 0.632) and the initially EWT ( Z =-0.003, P = 0.998) [15.0, 30.0] vs. [15.0, 35.0]. However, within the experimental group, a noteworthy distinction was identified between the initially EWT and the adjusted EWT ( Z = 6.226, P = 0.000) [15.0, 35.0] vs. [30.0, 40.0]. Specifically, as shown in Table 3 , when the AWT is greater than the EWT, there is a significant difference in satisfaction between the control group and the experimental group when the satisfaction score is at 0–60 points (χ 2 = 13.252, P < 0.001). The proportion of subjects in the control group in this area is 31.6%, while the proportion in the experimental group is 20.3%, a decrease of 11.3 percentage points. There was no significant difference in satisfaction between the control group and the experimental group in the other two regions with satisfaction scores of 61 or above (χ 2 = 2.301, p = 0.129; χ 2 = 3.195, p = 0.074), but in these two high satisfaction regions, the proportion of patients in the experimental group was higher than that in the control group. In the 61–80 points range, the experimental group exhibited a 5.1 percentage point advantage over the control group. Furthermore, in the satisfaction score bracket of 81–100 points, the experimental group comprised 42.0% of responses, surpassing the control group's representation of 35.8%. Table 3 The distribution of satisfaction between the experimental group and the control group when the AWT is greater than the EWT 0–60 61–80 81–100 Control group 126(31.6%) 130(32.6%) 143(35.8%) Experimental group 80(20.3%) * 149(37.7%) 166(42.0%) 3.3 The impact of EWT adjustment on subjects’ satisfaction during peak and off-peak hours Table 4 Adjustment of EWT during peak and off-peak hours Conditions Effective Invalid Peak hours (n = 689) 513(74.5%) 176(25.5%) off-peak hours (n = 317) 224(70.7%) 93(29.3%) As is shown in Table 4 , during peak hours, among 689 subjects, 513 subjects effectively adjusted their EWT after receiving information about potential waiting times, constituting 74.5% of the total. In off-peak hours, among 317 subjects, with 224 subjects successfully adjusting their EWT after receiving relevant information, representing a percentage of 70.7%. Within the experimental group, subjects who successfully adjusted their EWT exhibited significantly higher satisfaction levels compared to those who did not achieve effective adjustment ( P < .001, Z = 2.024, 100.0 [75.0-100.0] vs. 100.0 [85.0-100.0]). As is shown in Table 5 , during peak hours, there was a significant difference in satisfaction between subjects who were effectively regulated by EWT and those who were not effectively regulated (χ 2 = 24.865, p = .000). During off-peak hours, there was no significant difference in satisfaction between subjects who were effectively regulated by EWT and those who were not effectively regulated (χ 2 = 0.535, p = 0.765). In particular, within peak hours, the satisfaction of subjects who successfully adjusted their EWT is notably evident in two key aspects. Firstly, there is a significant reduction in low satisfaction levels among them, with a decrease of 10.4 percentage points compared to the ineffective adjustment group. Secondly, the high satisfaction segment witnessed a notable increase of 19.2 percentage points. This indicates a substantial improvement in the satisfaction of subjects experiencing peak hours who effectively adjusted their expectations, contrasting with those in the ineffective adjustment group. There is a significant difference in satisfaction levels between subjects during peak hours and those during off-peak hours. ( p = .000, z = 3.481; 100 [80, 100] vs. 100 [100, 100]). Table 5 Distribution of satisfaction score during peak and off-peak hours in experimental group 0–60 points 61–80 points 81–100 points Peak hours Effective (n = 513) 43(8.4%) 89(17.3%) 381(74.3%) Invalid (n = 176) 33(18.8%) 46(26.1%) 97(55.1%) Off-peak hours Effective (n = 224) 4(1.8%) 17(7.6%) 203(90.6%) Invalid (n = 93) 2(2.2%) 5(5.4%) 86(92.5%) Discussion Long waiting times in outpatient settings often lead to patient dissatisfaction and can even trigger conflicts between patients and healthcare providers [ 18 ]. It is precisely because of the critical role that AWT plays in affecting patient satisfaction that researchers and healthcare managers strive to improve overall satisfaction by reducing AWT [ 19 , 20 ]. This study experimentally explored the impact of adjusting patients' EWT through updated waiting time information intervention on their satisfaction under different relationships between AWT and EWT. The results show that when participants' actual waiting time exceeded their expected waiting time, compared to the control group, adjusting patients' EWT through updated waiting time information intervention significantly increased patient satisfaction. This indicates that patient satisfaction is comprehensively influenced by both actual circumstances and expectations, with residents' satisfaction in public service sectors proven to be related to the discrepancy between expected and actual service quality [ 21 ]. Similarly, in consumer services, both AWT and EWT are key determinants of customer satisfaction [ 22 ]. Since consumers form expectations about waiting times during the queuing process, the perceived quality of service is affected not only by AWT but also by the degree of discrepancy between AWT and EWT [ 23 ]. Research in these fields reveals that individual satisfaction assessment depends on the difference between EWT and AWT, which is fundamentally consistent with the conclusions drawn from this study. Human behavior is influenced by internal reference points [ 24 ], which are easily modified by information and environmental factors [ 25 ]. Therefore, individuals' evaluations and decision-making tendencies are accordingly adjusted. Before seeking medical treatment, patients' EWT based on personal experience or situational factors become the foundational reference point. In the control group, when AWT exceeds EWT, patients may feel disappointed because they fail to meet their set "anchor" point. Conversely, in the experimental group, informing patients about potentially longer waiting times upon entering the waiting area affects their initial EWT, leading to the formation of a new EWT. This new waiting time serves as their updated "anchor," thereby changing their satisfaction assessment with the extended EWT. In the experimental group, we found that by informing patients about longer waiting times in advance, those who effectively adjusted their EWT had significantly higher satisfaction than those who did not effectively adjust their EWT. During peak periods, patients often feel anxious and impatient due to uncertainty about potentially long waits. However, once specific waiting time information is provided, certainty increases despite the time loss, thus significantly improving patient satisfaction. In contrast, during non-peak periods, there was no significant difference in satisfaction between participants who effectively adjusted their EWT and those who did not under the influence of updated waiting time information. This is because, during non-peak times, fewer patients are seen, waiting times are shorter, and satisfaction is generally higher. This emphasizes that informing patients about potential waiting times in advance and adjusting their EWT during peak outpatient periods helps to enhance overall satisfaction levels. Limitations The data utilized in this study originates from a pediatric hospital, and as such, the general ability of these research findings to other healthcare institutions remains uncertain. Typically, when hospitals embark on initiatives to enhance patient satisfaction by adjusting EWT, it becomes imperative to strategically implement such adjustments during peak visitation periods to achieve more substantial and impact results. The unique characteristics and operational dynamics of individual hospitals may influence the applicability and effectiveness of similar interventions, necessitating a nuanced approach tailored to the specific context of each healthcare facility. Conclusion Patient satisfaction with outpatient services is a key indicator for assessing medical service capabilities[ 26 ]. Overall, this study offers new perspectives for enhancing patient satisfaction. It highlights the importance of not only focusing on the impact of AWT but also recognizing the significant effect of EWT on patient satisfaction. By considering both factors, medical service providers can develop more comprehensive strategies to improve the overall experience of patients seeking outpatient services. When EWT is greater than AWT, providing updated waiting time information can significantly enhance patient satisfaction. Conversely, when EWT is equal to or exceeds AWT, the impact of updated waiting time information on improving patient satisfaction is less pronounced. During peak periods, there is a significant difference in satisfaction between patients who successfully adjust EWT and those who do not. Specifically, patients who effectively improve EWT exhibit higher levels of satisfaction compared to their peers who fail to make effective adjustments. In contrast, during non-peak periods, there is no significant difference in satisfaction between patients who have had EWT effectively adjusted and those who have not. This study represents an important advancement in practice by using updated waiting time information prompts about prolonged waiting times to adjust EWT and thereby improve patient satisfaction. Future research could explore patient satisfaction at various stages of waiting during hospital visits, providing valuable insights for a more comprehensive understanding of the patient experience. Abbreviations AWT: actual waiting time; EWT: expected waiting time. Declarations Ethics approval and consent to participate The experimental has been approved by the Research Ethics Committee of Children’s Hospital of Fudan University [No. (2023) 152]. Informed consent was obtained from all study participants before the implementation of the questionnaire. Consent for publication Not applicable. Data availability statement The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper. Funding No funding. Authors’ contributions All authors contributed to the design of this study. The data was prepared by GX and SY. Statistical analysis was performed by ZH, TJH and QYP. The first draft of the manuscript was prepared by ZH. XW, GX and ZH reviewed and edited the manuscript. All authors read and approved the final version of the manuscript submitted for publication. Clinical trial number Not applicable Acknowledgments We thank the chief editor and anonymous reviewers for their valuable comments. References Ferreira D C , Marques R C , Nunes A M ,et al. Patients' satisfaction: The medical appointments valence in Portuguese public hospitals.Omega.2018;80(10):58-76. Danawala S , Boutry M, Tate P. Impact of Effective Physician Communication and Parental Understanding of Care on Parental Satisfaction with Hospitalization for Bronchiolitis. Pediatrics. 2015; 137. Bahammam FA. Satisfaction of Clinical Waiting Time in Ear, Nose & Throat Departments of the Ministry of Health in Jeddah, Saudi Arabia. Health Services Insights. 2023; 16. Li X, Tian D, Li W, et al. Using artificial intelligence to reduce queuing time and improve satisfaction in pediatric outpatient service: A randomized clinical trial. Frontiers in Pediatrics. 2022; 10. Sun J, Lin Q, Zhao P, et al. Reducing waiting time and raising outpatient satisfaction in a Chinese public tertiary general hospital-an interrupted time series study. BMC Public Health. 2017;17(1). Conner-Spady BL, Sanmartin C, Johnston GH, et al.The importance of patient expectations as a determinant of satisfaction with waiting times for hip and knee replacement surgery. Health Policy. 2011; 101(3):245-252. Alle YF, Akenaw B, Seid S, et al. Parental satisfaction and associated factors in pediatric outpatient clinics: A cross-sectional study. BMC Health Services Research. 2022; 22(1). Munavalli JR, Rao SV, Srinivasan A, et al. Integral patient scheduling in outpatient clinics under demand uncertainty to minimize patient waiting times. Health Inf J. 2020; 26(1): 435–448. Liu N, van Jaarsveld W, Wang S, et al. Managing outpatient service with strategic walk-ins. Manage. Sci. 2023; 69 (10): 5904-5922. Tunçalp F, Güneş ED, Örmeci EL. Modeling strategic walk-in patients in appointment systems: Equilibrium behavior and capacity allocation. European Journal of Operational Research. 2024; 313(2):587-601. Golmohammadi D, Zhao L, Dreyfus D: Using machine learning techniques to reduce uncertainty for outpatient appointment scheduling practices in outpatient clinics. Omega. 2023; 120. Fu S, Wu X-G, Zhang L, et al.Service Quality Improvement of Outpatient Blood Collection by Lean Management. Patient Preference and Adherence. 2021; 15:1537-1543. Zhang H, Ma W, Zhou S, et al. Effect of waiting time on patient satisfaction in outpatient: An empirical investigation. Medicine. 2023; 102(40). Campos Sousa E, Freire L. The effect of brief mindfulness‐based intervention on patient satisfaction and loyalty after waiting. Journal of Consumer Affairs. 2022; 57(2):906-942. Nair RD, Mohammadnezhad M. “It’s a waste of time coming here, better go to private clinics with wider options for treatment”: patient’s perception on dental services provided in Fiji. BMC Health Services Research. 2022; 22(1). Ma W-M, Zhang H, Wang N-L. Improving outpatient satisfaction by extending expected waiting time. BMC Health Services Research. 2019; 19(1). Zhang H, Ma W-M, Zhu J-J, et al.How to adjust the expected waiting time to improve patient’s satisfaction? BMC Health Services Research. 2023; 23(1). Zhang H, Wang WH, Haggerty J, et al. Predictors of patient satisfaction and outpatient health services in China: evidence from the WHO SAGE survey. Family Practice. 2020; 37 (4): 465-472. Ataman MG, Sarıyer G. Predicting waiting and treatment times in emergency departments using ordinal logistic regression models. The American Journal of Emergency Medicine. 2021; 46:45-50. Chen J, Alturas B: Improvement of outpatient service processes: a case study of the university of Hong Kong-Shenzhen hospital. Health and Technology. 2023; 13(6):971-985. Bellé N, Belardinelli P, Cucciniello M, et al.Experimental evidence on the determinants of citizens' expectations toward public services. Public Administration Review. 2023. Davis M M , Heineke J .How disconfirmation, perception and actual waiting times impact customer satisfaction.International Journal of Service Industry Management. 1998; 9(1):64-73. Jiang T, Gao L, Chai X. Equilibrium Queueing Strategies in M/G/1 Queues with the Reference Time Effect. Methodology and Computing in Applied Probability. 2023; 25(4). Li ZH, Zhong SF. Reference Dependence in Intertemporary Preferences.Manage. Sci.2023; 69 (1) , pp.475-490 Willis R, Evandrou M, Pathak P, et al. Problems with measuring satisfaction with social care. Health & Social Care in the Community. 2016;24(5):587-595. Shen J, Zhang J, He Q, et al. “Without the need for a second visit” initiative improves patient satisfaction with updated services of outpatient clinics in China. BMC Health Services Research. 2021; 21. Additional Declarations No competing interests reported. Supplementary Files Questionnaire.docx Cite Share Download PDF Status: Published Journal Publication published 14 May, 2025 Read the published version in BMC Health Services Research → Version 1 posted Editorial decision: Revision requested 29 Aug, 2024 Editor assigned by journal 27 Aug, 2024 Submission checks completed at journal 27 Aug, 2024 First submitted to journal 17 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4929205","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":346624783,"identity":"fb556a22-a81c-4f3b-a47f-12f2cd292cf2","order_by":0,"name":"Hui Zhang","email":"","orcid":"","institution":"Shanghai Business School","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Zhang","suffix":""},{"id":346624784,"identity":"fd6cf0fa-8fe8-46e8-a5b8-920c0b359faa","order_by":1,"name":"Junhua Tian","email":"","orcid":"","institution":"National Children's Medical Center, Children's Hospital of Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Junhua","middleName":"","lastName":"Tian","suffix":""},{"id":346624785,"identity":"9a4c46ef-f61e-46fc-88a1-5385088cb828","order_by":2,"name":"Yu Shi","email":"","orcid":"","institution":"National Children's Medical Center, Children's Hospital of Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Shi","suffix":""},{"id":346624786,"identity":"b67ef0f0-17b0-47d8-8524-4bce6a0acde2","order_by":3,"name":"Yuping Qian","email":"","orcid":"","institution":"National Children's Medical Center, Children's Hospital of Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Yuping","middleName":"","lastName":"Qian","suffix":""},{"id":346624787,"identity":"f5baa9c5-a667-44ca-bbf3-cf219c4eaad0","order_by":4,"name":"Xuan Gao","email":"","orcid":"","institution":"National Children's Medical Center, Children's Hospital of Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Xuan","middleName":"","lastName":"Gao","suffix":""},{"id":346624788,"identity":"20e68483-8347-4ae0-8ff0-d90d8aa302d1","order_by":5,"name":"Xiaowen Zhai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYHACNgYGAxt+BoYEEIeZaC1pkg0kamE4TIIWgxvpzx78KDgvYXA8+dkDhgrrxAb2swfwapGckZBu2GNwW8LgzDNzA4Yz6YkNPHkJeLXwSycck+AxuF1ncCPBTIKx7XBiA5CL3yPSiW2SfwzOSQBd+E2C8R8RWvilk9mkeQwOALXkAG1pIEKL5PxnbNIyBskSkmfelEkkHEs3buPJwa/F4MzxZ5Jv/thJ8B1P3ybxocZatp/9DH4tqCCBARJNo2AUjIJRMAooBADZMEEMmElJFAAAAABJRU5ErkJggg==","orcid":"","institution":"National Children's Medical Center, Children's Hospital of Fudan University","correspondingAuthor":true,"prefix":"","firstName":"Xiaowen","middleName":"","lastName":"Zhai","suffix":""}],"badges":[],"createdAt":"2024-08-17 09:59:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4929205/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4929205/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12913-025-12714-9","type":"published","date":"2025-05-14T15:57:28+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83067911,"identity":"87dea4ec-a7b2-496c-b7a9-1a9f5f3933aa","added_by":"auto","created_at":"2025-05-19 16:07:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":828855,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4929205/v1/659dc8a8-bf06-4ee1-a94f-10612cc99116.pdf"},{"id":65347404,"identity":"0e9c944f-5e8a-4817-ae76-973076a444db","added_by":"auto","created_at":"2024-09-26 10:01:12","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18945,"visible":true,"origin":"","legend":"","description":"","filename":"Questionnaire.docx","url":"https://assets-eu.researchsquare.com/files/rs-4929205/v1/2fe7da746923259a9abbff96.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Impact of Expected Waiting Time on Pediatric Outpatient Satisfaction: A Behavioral Experiment Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePatient satisfaction serves as a critical indicator for evaluating healthcare service quality [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Numerous studies have demonstrated that satisfaction levels decrease as waiting times increase[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], with patients often perceiving waiting as a wasteful use of their time [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This issue is particularly pronounced in the crowded outpatient clinics of China\u0026rsquo;s tertiary children's hospitals, especially during peak flu season [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAddressing this challenge can be approached through two primary strategies. The first involves reducing the actual waiting time (AWT) by streamlining appointment processes, such as implementing effective scheduling systems [\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and lean methodologies [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The second strategy focuses on managing the psychological experience of waiting [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Research suggests that forewarning patients about potential delays can improve their satisfaction by adjusting their expected waiting time (EWT) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This insight offers a novel approach to enhancing patient satisfaction.In practice, hospital administrators may wonder to what extent adjusting patients' EWT can maximally enhance patient satisfaction. Further study show that aligning the patient's EWT more closely with the AWT has been shown to increase satisfaction [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This finding provides clear direction for hospital administrators in enhancing patient satisfaction through adjusting expected waiting times.\u003c/p\u003e \u003cp\u003eHowever, on one hand, although the study was conducted in a hospital with patients as subjects, their satisfaction based on EWT and AWT was preset by researchers according to the needs of the study. If patients were to report their own EWT and adjusted EWT in a hospital, combined with their AWT, would their satisfaction still be altered by the updated waiting time information provided in advance? On the other hand, hospital visitation numbers are divided into peak and off-peak periods according to volume, especially in China's tertiary children's hospitals where there is a significant increase in pediatric visits during flu season, leading to correspondingly longer wait times for pediatric patients and their parents. In the face of this reality, with distinct peak and off-peak periods, can adjusting patients' EWT significantly improve the satisfaction of pediatric patients and their families?\u003c/p\u003e \u003cp\u003eBased on the aforementioned issues, this study will explore how patient satisfaction is affected when patients report their own EWT and adjusted EWT in a hospital, combined with their AWT. Specifically, the research will comparing patient satisfaction between control and experimental groups in three scenarios: when AWT exceeds the EWT, when AWT aligns with the EWT, and when AWT falls short of the EWT duration.\u003c/p\u003e \u003cp\u003eFurthermore, we will also investigate the variation in satisfaction levels among participants in the experimental group during peak and off-peak visiting hours, after informing them that a longer wait time might be expected due to a higher number of patients.\u003c/p\u003e \u003cp\u003eThe innovative aspects of this study are primarily reflected in three areas. First, it aims to bridge the gap between theoretical advancements and practical applications; second, it considers the joint impact of AWT and EWT on satisfaction when examining the influence of EWT. Third, the research separately studies the effects of informing waiting times in advance during peak and off-peak periods to enhance patient satisfaction, aligning more closely with the practical needs of healthcare management.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Experiment Design\u003c/h2\u003e \u003cp\u003eThe experiment is structured into two distinct groups: the control group and the experimental group. In the initial segment of the experiment, both groups underwent inquiries regarding the initial EWT, alongside capturing fundamental information such as age, education level, gender, and medical history. The distinguishing factor between the two groups lies in the implementation of waiting time reminders. In contrast to the control group, the experimental group receives reminders based on the AWT. Following this intervention, the experimental group provides adjusted EWT information. This segment of the experiment is completed upon the patient's entry into the consultation room.\u003c/p\u003e \u003cp\u003eBased on the number of visits, outpatient visits in hospitals are divided into peak and off peak periods. And based on the past number of patients, the hospital's peak hours are from 8:31 to 11:00 in the morning and from 13:31 to 15:30 in the afternoon, except for off-peak hours. Referring to the AWT data from July, we utilize the median AWT of 30 minutes during peak hours and 16 minutes during off-peak hours as prognostic indicators for the EWT within the experimental group.\u003c/p\u003e \u003cp\u003eEffective adjustment is defined as subjects aligning their expectations to approximately 30 minutes following the receipt of information indicating a 30-minute waiting time. As an illustration, if an individual initially expects a waiting time of 15 minutes, and upon receiving information adjusts the EWT to 40 minutes, then the disparity between the initial EWT and the 30-minute prompt message is 15 minutes, while the variance between the adjusted EWT and the prompt message is 10 minutes. The latter, being more closely aligned with the timing of the prompt information, is regarded as effective adjustment.\u003c/p\u003e \u003cp\u003eThe subsequent part of the experiment, applicable to both the control and experimental groups, is administered as subjects are on the verge of entering the consultation room at the designated number. This sequential approach aims to capture real-time insights into parents of pediatrics’ experiences and expectations, enabling a thorough examination of the impact of waiting time reminders on adjusted EWT and overall satisfaction. Both the control and experimental groups were assigned satisfaction scores corresponding to their AWT upon being summoned. Satisfaction was gauged on a scale ranging from 0 to 100, with \"0\" denoting utmost dissatisfaction and \"100\" indicating maximum satisfaction. A higher score signifies a heightened level of contentment. Given that a significant portion of the hospital's patients make online appointments and can settle registration fees through mobile payments, these pre-arrival tasks can be efficiently completed at home or en route to the hospital. Consequently, for the purposes of this study, the AWT encompasses the temporal interval between a parents of pediatrics’ check-in and the subsequent call time after arriving at the hospital.\u003c/p\u003e \u003cp\u003eThe implementation schedule for this study involves the control group undergoing the intervention in July, while the experimental group experiences the same in August. This strategic timing is grounded in past observations, indicating a peak period of outpatient visits in pediatric hospitals during the summer months of July and August.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Experiment subjects\u003c/h2\u003e \u003cp\u003eThe subjects of this study were patients and their parents who visited the pediatric hospital of the endocrine clinic in July and August 2023. Our investigation is anonymous and self-managed by the patients and their parents. The formula of sample size is expressed as follows:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:n={\\text{Z}}^{2}\\text{P}(1-\\text{P})/{\\text{E}}^{2}\\)\u003c/span\u003e\u003c/span\u003e, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n\\)\u003c/span\u003e\u003c/span\u003e is the minimum sample size; Z is the normal standard deviation at a 95% confidence level, which is 1.96, and P is the prevalence of the factor in the study, which was determined to be 80% based on previous studies [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Experiment implementation\u003c/h2\u003e \u003cp\u003eThe experiment implementation consists of two sequential steps. The initial segment is undertaken as soon as the patients and their parents arrives in the waiting room, while the second part comprises a single question that is addressed when the patients and their parents is summoned to the consultation room.\u003c/p\u003e \u003cp\u003e In the initial phase, during the administration of the control group, patients and their parents are presented with the first segment of the questionnaire upon signing in and entering the waiting area. The entire procedure, encompassing the explanation of informed consent, clarification of research objectives, and notification of completion guidelines, spans approximately 6 minutes.\u003c/p\u003e \u003cp\u003eWhen the patients or their parents completes this part of the questionnaire, the staff records their registration code, date of visit, and check-in time on the back. This part of the questionnaire is temporarily stored in the patients and their parents’ hands. The implementation of the experimental group is basically the same as that of the control group, with the only difference being that for patients visiting during peak hours and off-peak hours, patients and their parents will receive information about possible waiting times during peak and off-peak periods when filling out the questionnaire, and patients and their parents will be given corresponding adjusted EWT.\u003c/p\u003e \u003cp\u003eDuring the second phase, as patients and their parents on the verge of entering the clinic reach the entrance, staff members collect the initial segment of the questionnaire from the patients and their parents. Subsequently, the staff calculates the disparity between the call time and check-in time, representing the AWT. Inquiring about the patients and their parents’ satisfaction score in light of this AWT scenario, the staff promptly records the response. This segment of the questionnaire can be concluded efficiently within a minute.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Variables\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eDemographic variables\u003c/h2\u003e \u003cp\u003eThe demographic variables included gender, age, hospital history, and education level.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEWT\u003c/h2\u003e \u003cp\u003eInitial EWT: EWT that is not affected by time information interference.\u003c/p\u003e \u003cp\u003eAdjusted EWT: In the experimental group, the EWT adjusted after obtaining information about waiting time.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eAWT\u003c/h2\u003e \u003cp\u003eThe AWT was defined as the difference between the time of entering the clinic and the time of sign in.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSatisfaction level\u003c/h2\u003e \u003cp\u003eFor the evaluation of satisfaction, the patients and their parents was asked to choose a score from“0” to “100” randomly to represent their views on the time of the visit, in which “0” means very dissatisfied, and “100” means very satisfied.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Ethical consideration\u003c/h2\u003e \u003cp\u003e The research was performed in accordance with the Declaration of Helsinki. The experimental has been approved by the Research Ethics Committee of Children’s Hospital of Fudan University [No. (2023) 152]. Informed consent was obtained from all study participants before the implementation of the experiment.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.6 Statistical methods\u003c/b\u003eThe data analysis was carried out utilizing IBM SPSS Statistics 22.0 software. Given the non-normal distribution of waiting time, Kolmogorov-Smirnov tests and Mann-Whitney tests for two samples were employed to scrutinize potential significant differences in initial waiting time, AWT, or satisfaction scores between the control group and the experimental group. A \u003cem\u003ep\u003c/em\u003e-value below 0.05 is indicative of a significant difference. Furthermore, the Chi-square test was employed to assess variations in satisfaction levels between peak and off-peak hours, complemented by the application of descriptive statistical methods.\u003c/p\u003e "},{"header":"Results","content":"\u003ch2\u003e3.1 Baseline characteristics\u003c/h2\u003e\n\u003cp\u003eA total of 2020 subjects participated in the experiment, and one subject in the experimental group had missing key data. The effective sample size was 2019, with 1013 in the control group and 1006 in the experimental group. There were 666 (66.2%) and 729 (72.0%) females in the experimental and control groups, respectively (Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e). The experimental and control groups had 637 (63.3%) and 677 (66.8%). The subjects aged between 18 and 36 years, respectively. 708 (69.9%) and 773 (76.8%) of the subjects in the experimental and control groups received college or undergraduate education, respectively. Most of the two groups of subjects have visited this hospital for treatment, with 891 (88.0%) subjects in the control group and 769 (76.4%) subjects in the experimental group.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eBasic information of the subjects.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExperimental group\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e284(28.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e340(33.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e729(72.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e666(66.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBelow 18 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBetween 18\u0026ndash;36 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e677(66.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e637(63.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbove 36 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e332(32.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e369(36.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school or below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e191(18.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178(17.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiploma or undergraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e708(69.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e773(76.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePostgraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114(11.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55(5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eVisit history\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e891(88.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e769(76.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122(12.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e237(23.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch2\u003e3.2 The impact of adjustment of EWT on satisfaction\u003c/h2\u003e\n\u003cp\u003eCategorization of subjects within the control group and experimental group was performed by comparing their AWT with the initially anticipated waiting time. This classification yielded three distinct categories:\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003ea. \u003cstrong\u003eSubjects with Longer AWT than EWT\u003c/strong\u003e (T\u003csub\u003e0\u003c/sub\u003e-T\u003csub\u003ea\u003c/sub\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0): This group comprises subjects whose AWT exceeded the initially expected duration.\u003cbr\u003e\u003c/span\u003e\u003cspan\u003eb. \u003cstrong\u003eSubjects with Equal EWT and AWT\u003c/strong\u003e (T\u003csub\u003e0\u003c/sub\u003e-T\u003csub\u003ea\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0): The second category encompasses subjects for whom the anticipated waiting time aligned precisely with the AWT.\u003cbr\u003e\u003c/span\u003e\u003cspan\u003ec. \u003cstrong\u003eSubjects with Shorter AWT than EWT\u003c/strong\u003e (T\u003csub\u003e0\u003c/sub\u003e-T\u003csub\u003ea\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0): The third group includes subjects whose AWT proved shorter than initially projected.\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eAs depicted in Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e, a notable disparity in satisfaction levels emerged between the control group and the experimental group when the AWT exceeded the EWT (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.042, \u003cem\u003eZ\u003c/em\u003e=-2.035). Conversely, when subjects experienced an AWT equal to the initially expected duration, no significant difference in satisfaction levels was observed between the two groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.230, \u003cem\u003eZ\u003c/em\u003e=-1.200). Similarly, when the AWT proved shorter than the EWT, no significant difference in satisfaction levels was detected between the two groups (\u003cem\u003eZ\u003c/em\u003e=-1.416, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.157).\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eSatisfaction scores in the control and experimental groups\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConditions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eControl group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eExperiment group\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScores\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e0\u003c/sub\u003e-T\u003csub\u003ea\u003c/sub\u003e\u0026lt;0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.0 [60.0, 90.0]\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.0 [65.0, 90.0] \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e0\u003c/sub\u003e-T\u003csub\u003ea\u003c/sub\u003e=0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.0 [100.0, 100.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.0 [92.5.0, 100.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e0\u003c/sub\u003e-T\u003csub\u003ea\u003c/sub\u003e\u0026gt;0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.0 [100.0, 100.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.0 [100.0, 100.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eT\u003csub\u003e0\u003c/sub\u003e, before updated waiting time information (UI) was given; T\u003csub\u003ea\u003c/sub\u003e, AWT\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Scores in the control group vs. scores in the experimental group, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n\u003cp\u003eWhen comparing cases where the AWT surpassed the initial EWT, no significant disparities emerged between the experimental and control groups concerning both the AWT (\u003cem\u003eZ\u003c/em\u003e=-0.478, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.632) and the initially EWT (\u003cem\u003eZ\u003c/em\u003e=-0.003, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.998) [15.0, 30.0] vs. [15.0, 35.0]. However, within the experimental group, a noteworthy distinction was identified between the initially EWT and the adjusted EWT (\u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.226, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000) [15.0, 35.0] vs. [30.0, 40.0].\u003c/p\u003e\n\u003cp\u003eSpecifically, as shown in Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e, when the AWT is greater than the EWT, there is a significant difference in satisfaction between the control group and the experimental group when the satisfaction score is at 0\u0026ndash;60 points (\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;13.252, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The proportion of subjects in the control group in this area is 31.6%, while the proportion in the experimental group is 20.3%, a decrease of 11.3 percentage points. There was no significant difference in satisfaction between the control group and the experimental group in the other two regions with satisfaction scores of 61 or above (\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;2.301, p\u0026thinsp;=\u0026thinsp;0.129; \u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;3.195, p\u0026thinsp;=\u0026thinsp;0.074), but in these two high satisfaction regions, the proportion of patients in the experimental group was higher than that in the control group.\u003c/p\u003e\n\u003cp\u003eIn the 61\u0026ndash;80 points range, the experimental group exhibited a 5.1 percentage point advantage over the control group. Furthermore, in the satisfaction score bracket of 81\u0026ndash;100 points, the experimental group comprised 42.0% of responses, surpassing the control group\u0026apos;s representation of 35.8%.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe distribution of satisfaction between the experimental group and the control group when the AWT is greater than the EWT\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;60\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e61\u0026ndash;80\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e81\u0026ndash;100\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e126(31.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e130(32.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e143(35.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExperimental group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80(20.3%)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e149(37.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e166(42.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch2\u003e3.3 The impact of EWT adjustment on subjects\u0026rsquo; satisfaction during peak and off-peak hours\u003c/h2\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eAdjustment of EWT during peak and off-peak hours\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConditions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEffective\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInvalid\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeak hours (n\u0026thinsp;=\u0026thinsp;689)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e513(74.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e176(25.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eoff-peak hours (n\u0026thinsp;=\u0026thinsp;317)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e224(70.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93(29.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAs is shown in Table \u003cspan\u003e4\u003c/span\u003e, during peak hours, among 689 subjects, 513 subjects effectively adjusted their EWT after receiving information about potential waiting times, constituting 74.5% of the total. In off-peak hours, among 317 subjects, with 224 subjects successfully adjusting their EWT after receiving relevant information, representing a percentage of 70.7%. Within the experimental group, subjects who successfully adjusted their EWT exhibited significantly higher satisfaction levels compared to those who did not achieve effective adjustment (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.024, 100.0 [75.0-100.0] vs. 100.0 [85.0-100.0]).\u003c/p\u003e\n\u003cp\u003eAs is shown in Table \u003cspan\u003e5\u003c/span\u003e, during peak hours, there was a significant difference in satisfaction between subjects who were effectively regulated by EWT and those who were not effectively regulated (\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;24.865, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.000). During off-peak hours, there was no significant difference in satisfaction between subjects who were effectively regulated by EWT and those who were not effectively regulated (\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.535, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.765).\u003c/p\u003e\n\u003cp\u003eIn particular, within peak hours, the satisfaction of subjects who successfully adjusted their EWT is notably evident in two key aspects. Firstly, there is a significant reduction in low satisfaction levels among them, with a decrease of 10.4 percentage points compared to the ineffective adjustment group. Secondly, the high satisfaction segment witnessed a notable increase of 19.2 percentage points. This indicates a substantial improvement in the satisfaction of subjects experiencing peak hours who effectively adjusted their expectations, contrasting with those in the ineffective adjustment group.\u003c/p\u003e\n\u003cp\u003eThere is a significant difference in satisfaction levels between subjects during peak hours and those during off-peak hours. (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.000, z\u0026thinsp;=\u0026thinsp;3.481; 100 [80, 100] vs. 100 [100, 100]).\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDistribution of satisfaction score during peak and off-peak hours in experimental group\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;60 points\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e61\u0026ndash;80 points\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e81\u0026ndash;100 points\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeak hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eEffective (n\u0026thinsp;=\u0026thinsp;513)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43(8.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89(17.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e381(74.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eInvalid\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;176)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33(18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46(26.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97(55.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOff-peak hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eEffective (n\u0026thinsp;=\u0026thinsp;224)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(7.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e203(90.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eInvalid\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(5.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86(92.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eLong waiting times in outpatient settings often lead to patient dissatisfaction and can even trigger conflicts between patients and healthcare providers [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. It is precisely because of the critical role that AWT plays in affecting patient satisfaction that researchers and healthcare managers strive to improve overall satisfaction by reducing AWT [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study experimentally explored the impact of adjusting patients' EWT through updated waiting time information intervention on their satisfaction under different relationships between AWT and EWT. The results show that when participants' actual waiting time exceeded their expected waiting time, compared to the control group, adjusting patients' EWT through updated waiting time information intervention significantly increased patient satisfaction. This indicates that patient satisfaction is comprehensively influenced by both actual circumstances and expectations, with residents' satisfaction in public service sectors proven to be related to the discrepancy between expected and actual service quality [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Similarly, in consumer services, both AWT and EWT are key determinants of customer satisfaction [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Since consumers form expectations about waiting times during the queuing process, the perceived quality of service is affected not only by AWT but also by the degree of discrepancy between AWT and EWT [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Research in these fields reveals that individual satisfaction assessment depends on the difference between EWT and AWT, which is fundamentally consistent with the conclusions drawn from this study.\u003c/p\u003e \u003cp\u003eHuman behavior is influenced by internal reference points [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], which are easily modified by information and environmental factors [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Therefore, individuals' evaluations and decision-making tendencies are accordingly adjusted. Before seeking medical treatment, patients' EWT based on personal experience or situational factors become the foundational reference point. In the control group, when AWT exceeds EWT, patients may feel disappointed because they fail to meet their set \"anchor\" point. Conversely, in the experimental group, informing patients about potentially longer waiting times upon entering the waiting area affects their initial EWT, leading to the formation of a new EWT. This new waiting time serves as their updated \"anchor,\" thereby changing their satisfaction assessment with the extended EWT. In the experimental group, we found that by informing patients about longer waiting times in advance, those who effectively adjusted their EWT had significantly higher satisfaction than those who did not effectively adjust their EWT.\u003c/p\u003e \u003cp\u003eDuring peak periods, patients often feel anxious and impatient due to uncertainty about potentially long waits. However, once specific waiting time information is provided, certainty increases despite the time loss, thus significantly improving patient satisfaction. In contrast, during non-peak periods, there was no significant difference in satisfaction between participants who effectively adjusted their EWT and those who did not under the influence of updated waiting time information. This is because, during non-peak times, fewer patients are seen, waiting times are shorter, and satisfaction is generally higher. This emphasizes that informing patients about potential waiting times in advance and adjusting their EWT during peak outpatient periods helps to enhance overall satisfaction levels.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThe data utilized in this study originates from a pediatric hospital, and as such, the general ability of these research findings to other healthcare institutions remains uncertain. Typically, when hospitals embark on initiatives to enhance patient satisfaction by adjusting EWT, it becomes imperative to strategically implement such adjustments during peak visitation periods to achieve more substantial and impact results. The unique characteristics and operational dynamics of individual hospitals may influence the applicability and effectiveness of similar interventions, necessitating a nuanced approach tailored to the specific context of each healthcare facility.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003ePatient satisfaction with outpatient services is a key indicator for assessing medical service capabilities[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Overall, this study offers new perspectives for enhancing patient satisfaction. It highlights the importance of not only focusing on the impact of AWT but also recognizing the significant effect of EWT on patient satisfaction. By considering both factors, medical service providers can develop more comprehensive strategies to improve the overall experience of patients seeking outpatient services.\u003c/p\u003e \u003cp\u003eWhen EWT is greater than AWT, providing updated waiting time information can significantly enhance patient satisfaction. Conversely, when EWT is equal to or exceeds AWT, the impact of updated waiting time information on improving patient satisfaction is less pronounced.\u003c/p\u003e \u003cp\u003eDuring peak periods, there is a significant difference in satisfaction between patients who successfully adjust EWT and those who do not. Specifically, patients who effectively improve EWT exhibit higher levels of satisfaction compared to their peers who fail to make effective adjustments. In contrast, during non-peak periods, there is no significant difference in satisfaction between patients who have had EWT effectively adjusted and those who have not.\u003c/p\u003e \u003cp\u003eThis study represents an important advancement in practice by using updated waiting time information prompts about prolonged waiting times to adjust EWT and thereby improve patient satisfaction. Future research could explore patient satisfaction at various stages of waiting during hospital visits, providing valuable insights for a more comprehensive understanding of the patient experience.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAWT: actual waiting time; EWT: expected waiting time.\u0026nbsp;\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe experimental has been approved by the Research Ethics Committee of Children\u0026rsquo;s Hospital of Fudan University [No. (2023) 152]. Informed consent was obtained from all study participants before the implementation of the questionnaire.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the design of this study. The data was prepared by GX and SY. Statistical analysis was performed by ZH, TJH and QYP. The first draft of the manuscript was prepared by ZH. XW, GX and ZH reviewed and edited the manuscript. All authors read and approved the final version of the manuscript submitted for publication.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the chief editor and anonymous reviewers for their valuable comments.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFerreira D C , Marques R C , Nunes A M ,et al. Patients\u0026apos; satisfaction: The medical appointments valence in Portuguese public hospitals.Omega.2018;80(10):58-76.\u003c/li\u003e\n\u003cli\u003eDanawala S , Boutry M, Tate P. Impact of Effective Physician Communication and Parental Understanding of Care on Parental Satisfaction with Hospitalization for Bronchiolitis. Pediatrics. 2015; 137.\u003c/li\u003e\n\u003cli\u003eBahammam FA. Satisfaction of Clinical Waiting Time in Ear, Nose \u0026amp; Throat Departments of the Ministry of Health in Jeddah, Saudi Arabia. Health Services Insights. 2023; 16.\u003c/li\u003e\n\u003cli\u003eLi X, Tian D, Li W, et al. Using artificial intelligence to reduce queuing time and improve satisfaction in pediatric outpatient service: A randomized clinical trial. Frontiers in Pediatrics. 2022; 10.\u003c/li\u003e\n\u003cli\u003eSun J, Lin Q, Zhao P, et al. Reducing waiting time and raising outpatient satisfaction in a Chinese public tertiary general hospital-an interrupted time series study. BMC Public Health. 2017;17(1).\u003c/li\u003e\n\u003cli\u003eConner-Spady BL, Sanmartin C, Johnston GH, et al.The importance of patient expectations as a determinant of satisfaction with waiting times for hip and knee replacement surgery. Health Policy. 2011; 101(3):245-252.\u003c/li\u003e\n\u003cli\u003eAlle YF, Akenaw B, Seid S, et al. Parental satisfaction and associated factors in pediatric outpatient clinics: A cross-sectional study. BMC Health Services Research. 2022; 22(1).\u003c/li\u003e\n\u003cli\u003eMunavalli JR, Rao SV, Srinivasan A, et al. Integral patient scheduling in outpatient clinics under demand uncertainty to minimize patient waiting times. Health Inf J. 2020; 26(1): 435\u0026ndash;448.\u003c/li\u003e\n\u003cli\u003eLiu N, van Jaarsveld W, Wang S, et al. Managing outpatient service with strategic walk-ins. Manage. Sci. 2023; 69 (10): 5904-5922.\u003c/li\u003e\n\u003cli\u003eTun\u0026ccedil;alp F, G\u0026uuml;neş ED, \u0026Ouml;rmeci EL. Modeling strategic walk-in patients in appointment systems: Equilibrium behavior and capacity allocation. European Journal of Operational Research. 2024; 313(2):587-601.\u003c/li\u003e\n\u003cli\u003eGolmohammadi D, Zhao L, Dreyfus D: Using machine learning techniques to reduce uncertainty for outpatient appointment scheduling practices in outpatient clinics. Omega. 2023; 120.\u003c/li\u003e\n\u003cli\u003eFu S, Wu X-G, Zhang L, et al.Service Quality Improvement of Outpatient Blood Collection by Lean Management. Patient Preference and Adherence. 2021; 15:1537-1543.\u003c/li\u003e\n\u003cli\u003eZhang H, Ma W, Zhou S, et al. Effect of waiting time on patient satisfaction in outpatient: An empirical investigation. Medicine. 2023; 102(40).\u003c/li\u003e\n\u003cli\u003eCampos Sousa E, Freire L. The effect of brief mindfulness‐based intervention on patient satisfaction and loyalty after waiting. Journal of Consumer Affairs. 2022; 57(2):906-942.\u003c/li\u003e\n\u003cli\u003eNair RD, Mohammadnezhad M. \u0026ldquo;It\u0026rsquo;s a waste of time coming here, better go to private clinics with wider options for treatment\u0026rdquo;: patient\u0026rsquo;s perception on dental services provided in Fiji. BMC Health Services Research. 2022; 22(1).\u003c/li\u003e\n\u003cli\u003eMa W-M, Zhang H, Wang N-L. Improving outpatient satisfaction by extending expected waiting time. BMC Health Services Research. 2019; 19(1).\u003c/li\u003e\n\u003cli\u003eZhang H, Ma W-M, Zhu J-J, et al.How to adjust the expected waiting time to improve patient\u0026rsquo;s satisfaction? BMC Health Services Research. 2023; 23(1).\u003c/li\u003e\n\u003cli\u003eZhang H, Wang WH, Haggerty J, et al. Predictors\u0026ensp;of\u0026ensp;patient\u0026ensp;satisfaction\u0026ensp;and\u0026ensp;outpatient\u0026ensp;health services\u0026ensp;in\u0026ensp;China: evidence from the WHO SAGE survey. Family Practice. 2020; 37 (4): 465-472.\u003c/li\u003e\n\u003cli\u003eAtaman MG, Sarıyer G. Predicting waiting and treatment times in emergency departments using ordinal logistic regression models. The American Journal of Emergency Medicine. 2021; 46:45-50.\u003c/li\u003e\n\u003cli\u003eChen J, Alturas B: Improvement of outpatient service processes: a case study of the university of Hong Kong-Shenzhen hospital. Health and Technology. 2023; 13(6):971-985.\u003c/li\u003e\n\u003cli\u003eBell\u0026eacute; N, Belardinelli P, Cucciniello M, et al.Experimental evidence on the determinants of citizens\u0026apos; expectations toward public services. Public Administration Review. 2023.\u003c/li\u003e\n\u003cli\u003eDavis M M , Heineke J .How disconfirmation, perception and actual waiting times impact customer satisfaction.International Journal of Service Industry Management. 1998; 9(1):64-73.\u003c/li\u003e\n\u003cli\u003eJiang T, Gao L, Chai X. Equilibrium Queueing Strategies in M/G/1 Queues with the Reference Time Effect. Methodology and Computing in Applied Probability. 2023; 25(4).\u003c/li\u003e\n\u003cli\u003eLi ZH, Zhong SF. Reference Dependence in Intertemporary Preferences.Manage. Sci.2023; 69 (1) , pp.475-490\u003c/li\u003e\n\u003cli\u003eWillis R, Evandrou M, Pathak P, et al. Problems with measuring satisfaction with social care. Health \u0026amp; Social Care in the Community. 2016;24(5):587-595.\u003c/li\u003e\n\u003cli\u003eShen J, Zhang J, He Q, et al. \u0026ldquo;Without the need for a second visit\u0026rdquo; initiative improves patient satisfaction with updated services of outpatient clinics in China. BMC Health Services Research. 2021; 21. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4929205/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4929205/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eOutpatient departments in tertiary children's hospitals in China are often overcrowded, with parents frequently voicing concerns about prolonged waiting times. While substantial efforts have been directed towards reducing actual waiting times (AWT), managing the expectations of parents has received limited attention. This study employs a behavioral experiment to investigate the relationship between expectations and satisfaction levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod: \u003c/strong\u003eThe experiment consisted of two groups: a control group and an experimental group. Initially, the baseline expected waiting times (EWT) for subjects in both groups were obtained, along with demographic information including age, education level, gender, and medical experience. Unlike the control group, subjects in the experimental group received reminders about waiting times and subsequently adjusted their EWT accordingly. This study employed non-parametric tests and variance tests to analyze the differences in satisfaction levels between the two groups of subjects. Ethical approval for this project was obtained from the hospital ethics committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult:\u003c/strong\u003e Significant differences in satisfaction levels were observed between the control group and the experimental group when the AWT exceeded the EWT (\u003cem\u003eP\u003c/em\u003e=0.042, \u003cem\u003eZ\u003c/em\u003e=-2.035). However, when the AWT was equal to or shorter than the EWT, no significant disparity in satisfaction levels emerged between the two groups (\u003cem\u003eP\u003c/em\u003e=0.230, \u003cem\u003eZ\u003c/em\u003e=-1.200; \u003cem\u003eZ\u003c/em\u003e=-1.416, \u003cem\u003eP\u003c/em\u003e=0.157).\u003c/p\u003e\n\u003cp\u003eWithin the experimental group, a significant difference in satisfaction was noted during peak hours between subjects effectively regulated by EWT and those not effectively regulated (x\u003csup\u003e2\u003c/sup\u003e=24.865, \u003cem\u003eP\u003c/em\u003e=0.000). Conversely, during off-peak hours, there was no significant distinction in satisfaction between those effectively regulated by EWT and those not effectively regulated (x\u003csup\u003e2\u003c/sup\u003e=0.535, \u003cem\u003eP\u003c/em\u003e =0.765).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e When the AWT exceeds the EWT, providing advance notice of long waiting time can extend patients EWT and significantly enhance their satisfaction. However, when the AWT is equal to or less than the EWT, the impact of advance notice of long waiting time on patient satisfaction is not statistically significant. During peak visiting hours, when alerts about longer waiting times are issued, patients who effectively adjust their EWT exhibit significantly higher satisfaction levels compared to those who do not make effective adjustments to their EWT. Conversely, during non-peak visiting hours, there is no significant difference in satisfaction levels between subjects who effectively regulate their EWT and those who do not.\u003c/p\u003e\n\u003cp\u003eHealthcare institutions can adjust patients' EWT by informing them in advance about potential waiting times according to the temporal patterns of outpatient visitation numbers during peak hours. This approach mitigates negative emotions associated with prolonged waiting times and represents one of the effective methods to enhance the quality of medical services.\u003c/p\u003e","manuscriptTitle":"The Impact of Expected Waiting Time on Pediatric Outpatient Satisfaction: A Behavioral Experiment Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-26 10:01:07","doi":"10.21203/rs.3.rs-4929205/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-29T05:30:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-28T00:59:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-28T00:58:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2024-08-17T09:56:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e72a578b-3dd2-426c-a5e2-d4a830031881","owner":[],"postedDate":"September 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-19T16:03:40+00:00","versionOfRecord":{"articleIdentity":"rs-4929205","link":"https://doi.org/10.1186/s12913-025-12714-9","journal":{"identity":"bmc-health-services-research","isVorOnly":false,"title":"BMC Health Services Research"},"publishedOn":"2025-05-14 15:57:28","publishedOnDateReadable":"May 14th, 2025"},"versionCreatedAt":"2024-09-26 10:01:07","video":"","vorDoi":"10.1186/s12913-025-12714-9","vorDoiUrl":"https://doi.org/10.1186/s12913-025-12714-9","workflowStages":[]},"version":"v1","identity":"rs-4929205","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4929205","identity":"rs-4929205","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}