Propensity Score Matching Analysis of the Influence of Waiting Time Satisfaction on Community Resident’s Satisfaction With Medical Institutions: An Extensive Survey of Outpatient Population in Shantou City of Southern China

Abstract

Objective:

This study was designed to perform a nuanced analysis of the multifaceted association between community residents’ satisfaction and their perceived satisfaction concerning the visit duration at medical facilities, that could be harnessed to enhance and streamline the process of hierarchical diagnosis and treatment, thereby augmenting healthcare outcomes and patient experiences.

Methods:

Respondents who had utilized services from medical institutions were invited to fill out questionnaires by scanning QR codes. Additionally, surveys also distributed questionnaires through WeChat groups of community residents in densely populated areas of the community, as well as WeChat groups for patients who had previously visited local hospitals. To balance differences between groups, propensity score matching was applied to analyze the contrast between residents satisfied and dissatisfied with their medical visits. After eliminating the interference of confounding factors, a comparative analysis was conducted on the relationship between resident satisfaction and medical institution experience.After eliminating the interference of confounding factors, a comparative analysis was conducted to delve deeply into the relationship between residents’ satisfaction and their experiences at medical facilities.

Results:

The study incorporated a large dataset encompassing 2356 community residents. Upon successful propensity score matching, logistic regression analysis elucidated several determinants of overall resident satisfaction. Notably, the grade of the medical institution (χ² = 8.226, P < .05), satisfaction with the time invested in the registration process (χ² = 11.04, P < .05), satisfaction with the waiting duration for consultation (χ² = 15.759, P < .05), and satisfaction with the travel time to the hospital (χ² = 45.157, P < .05) each exerted significant influence on the holistic satisfaction of residents with their medical experience.

Conclusion:

Factors such as the grade of the medical institution, satisfaction related to registration and waiting durations, and travel time to the hospital emerged as crucial determinants shaping community residents’ holistic satisfaction with their medical encounters. These findings underscore the exigency for strategic allocation and optimization of medical resources, refinement of the classification system, and enhancement of public health education on the graded diagnosis and treatment schema. The study also demonstrates the value of employing advanced propensity score matching and predictive modelling techniques in health services research.

Keywords

medical satisfaction medical duration hierarchical diagnosis and treatment tendency score matching community healthcare outcomes

Background

Visit satisfaction pertains to the expectations of medical delivery and the emotional responses to experienced visit behaviors.¹ In 2018, the World Health Organization updated a list of 100 core health indicators for global reference, including patient satisfaction as one of the metrics to measure the quality and safety of health systems. As an important component of medical services and a key indicator of health service outcomes, its significance has become a global consensus.^2
-4.

In the mid to late 20th century, spurred by medical service research and patient-centered care concepts, scholars began exploring patient satisfaction. A high-quality systematic review published in 2021 on the determinants of patient satisfaction used the convenience of health services to explain accessibility, including factors like shorter waiting times and quicker appointment scheduling.⁵ Waiting time for medical visits is not a new term. The process starts from heading to the hospital, registering upon arrival, waiting for the doctor’s consultation, being directed to specialized departments for various tests, collecting reports and returning to the clinic for the doctor’s interpretation and prescription, followed by paying at the billing counter using medical insurance/self-payment, and finally ends with collecting medication from the pharmacy. This is a typical outpatient process. In this process, several waiting periods emerge, such as travel time, waiting time for the doctor, and time spent waiting for medication, etc. Collectively, these are referred to as “visit duration.” The World Health Organization has identified the time patients wait for medical services as a key metric to measure the responsiveness of health systems.⁶

In a large-scale study, international scholars explored the relationship between waiting times for medical visits and patient satisfaction. They pointed out that longer waiting times are associated with lower levels of patient satisfaction.⁷ Scholar Anna believes that the perceived waiting time by patients is an important factor affecting patient satisfaction.⁸ Chen⁹ observed that prolonged waiting times in a hospital adversely affected patient satisfaction. Zhou³ suggested that even when patients exhibit high levels of satisfaction with medical care, shortening waiting times remains an area in need of improvement. In Xie’s et al¹⁰ 2019 survey on medical treatment satisfaction, 67.33% of outpatient patients believed that enhancing waiting times was crucial to boosting satisfaction rates. .After surveying 630 outpatient patients, researcher Ren et al¹¹ indicated that waiting duration has a significant impact on patient satisfaction with their medical visits.

From the above, it can be understood that visit duration, as an indicator for assessing patient satisfaction with healthcare services,¹² is a topic of extensive research at the present stage, with many studies focusing on the exploration of specific durations. Due to perceptual differences, individuals experience the same duration differently. Excluding confounding factors, research specifically addressing patient satisfaction with visit duration remains limited.

This study investigates the most recent medical visit experiences of community residents in Shantou City, Guangdong Province. It applies propensity score matching to reduce the interference of confounding factors, enabling a more precise exploration of the relationship between residents’ satisfaction with various consultation durations and their overall satisfaction with medical visits. The study aims to refine the research on factors influencing satisfaction with medical services. It provides policy-making references for improving the quality of medical services and for health care departments to develop and optimize policies regarding medical consultation services.

Historically, the nexus between patient satisfaction and waiting times has been a focal point of research, reflecting its importance in patient experience. Studies have delineated the multifaceted nature of waiting times - from registration, consultation, to receiving medication - underscoring its significance in the patient care continuum.^7,13,14 However, while numerous studies have broached the subject of waiting times, a scant few have rigorously controlled for confounding variables to isolate the pure effect of waiting times on patient satisfaction. This oversight muddles the clarity of the relationship between waiting times and patient satisfaction, leaving a gap ripe for exploration.

In this study, we define “visit waiting time” as the total amount of time a patient spends waiting from the moment they leave for the healthcare facility, through registration, to finally receiving consultation or treatment from a doctor.This study aims to bridge this gap by employing a rigorous methodological framework, leveraging propensity score matching to meticulously control for confounding factors, thus providing a clearer depiction of how different aspects of visit duration influence overall satisfaction with healthcare services. This research endeavors to offer nuanced insights into the dynamics of patient satisfaction, specifically illuminating how waiting times across various stages of the medical visit impact the patient’s perception and satisfaction with healthcare services. This endeavor not only enriches the existing body of knowledge but also furnishes policymakers and healthcare providers with actionable data to refine and enhance patient care protocols, ultimately aiming to elevate patient satisfaction and healthcare outcomes.

Data and Methods

Participants

This study selected community residents in Shantou City, Guangdong Province, from January to February 2023. Inclusion criteria were as follows: (1) over 18 years old; (2) possessing prior medical experience; (3) demonstrating adequate listening, speaking, reading, and writing abilities with no cognitive impairments; and (4) voluntarily providing informed consent to participate in the study.

Survey Method and Analysis of Principal Variables

The survey employed QR code distribution and convenient sampling to administer questionnaires. The collection of questionnaires for this study was based on-site in communities and hospitals within the Shantou area. Respondents who had utilized services from medical institutions were invited to fill out questionnaires by scanning QR codes. Additionally, surveys were distributed through WeChat groups of community residents in densely populated areas of the community, as well as WeChat groups for patients who had previously visited local hospitals, to invite them to participate in the survey, thereby collecting data on the residents’ most recent medical visit experiences. Residents with previous medical experiences were invited to recall and detail their last medical visit for data collection purposes.

The outpatient satisfaction questionnaire featured items such as “overall patient satisfaction” as well as various waiting times associated with different stages of the visit, including: “Satisfaction with transportation time to the hospital,” “Satisfaction with registration time,” “Satisfaction with waiting time for consultation,” “Satisfaction with doctor communication length,” “Satisfaction with waiting time for physical examination,” “Satisfaction with time needed to obtain medication,” and “Satisfaction with time required to process medical insurance payments.”

The outpatient satisfaction questionnaire followed the revised scale development procedures provided by DeVellis Scholars.¹¹ A 5-point Likert scale was utilized for scoring, ranging from 1 (dissatisfied) to 5 (satisfied). For “satisfaction rate” analysis, “satisfied” and “more satisfied” were categorized as satisfied, while “neutral,” “less satisfied,” and “dissatisfied” were classified as unsatisfied.¹²

A total of 50 pre-experiment questionnaires were collected. Six experts with deputy high-level titles or higher used the Likert 4-level scoring method (1 = irrelevant, 2 = weakly related, 3 = strongly related, 4 = extremely related). The content validity index (I-CVI) for each item ranged from 0.83 to 1, and the scale-level content validity index (S-CVI/UA) was 0.95, which is greater than 0.9. These results indicate that the participating experts considered the questionnaire items to have a strong correlation with visit satisfaction concepts[10].When measuring construct validity, the factor analysis results revealed a KMO value of 0.915 and a Bartlett’s sphericity test with P < .001. Using principal component analysis, 2 common factors were extracted: “satisfaction with transportation time to the hospital” as an independent common factor and the remaining items as another common factor. The cumulative variance contribution was 72.35%, and factor load values for each item ranged from 0.80 to 1. The reliability analysis results showed an overall Cronbach’s ɑ coefficient of 0.873 for the questionnaire, indicating good internal consistency among the 7 items (Table 1).

Table 1.

The Factor Load Matrix of Each Visit Length Satisfaction Questionnaire.

	Ingredient
	1	2
The time required for registration	0.817	−0.030
The time required for waiting for consultation	0.810	−0.067
The length of communication with doctors	0.815	−0.045
The time required for waiting for physical examination	0.831	−0.057
The time required to get the medicine	0.830	−0.040
The length of time required to pay for medical insurance	0.827	−0.047
The length of transportation to the hospital	0.242	0.970
Rotation method: Caesar normal maximum variance method.

Quality Control

This study received approval from the Ethics Committee of Shantou University Medical College. The trained investigators provided uniform explanations to the residents, introducing the study’s purpose, content, and questionnaire completion method. Participants were instructed to complete the questionnaire, following their experience within 30 min. Electronic questionnaires were utilized in this study, with residents scanning the QR code and completing the form based on their most recent medical experience within the given time frame. After submission, the investigators conducted checks for duplication, omissions, logical inconsistencies, and suspicious responses to ensure the validity of the collected data. Double checking was employed during the data entry process to guarantee data accuracy. The primary research procedure is outlined in Figure 1.

Figure 1.

Research flow chart.

Variable Selection

In this study, the outcome variable was “overall medical satisfaction” among community residents. According to the satisfaction measurements, “satisfactory” and “relatively satisfactory” were considered as satisfied and assigned a value of 1. “Average,” “less satisfied,” and “dissatisfied” were deemed as dissatisfied and assigned a value of 0. The control variables selected included sociodemographic characteristics, socioeconomic features, social support, social security, and the level of medical institutions. Detailed variable assignments can be found in Table 2.

Table 2.

Variable Sssignments.

Variable	Assignment
Dependent variable
Overall satisfaction	1 = satisfied and 0 = dissatisfied
Controlled variable
Sociodemographiccharacteristics
Age/year	1 = below 30 years old, 2 = 30-50 years, and 3 = above 50 years old
Gender	1 = male and 2 = female
Education level	1 = high school diploma or below, 2 = associate’s or bachelor’s degree, and 3 = master’s degree or higher
Employment status	1 = employed and 2 = unemployed
Socioeconomic factors
Annual income per capita	1 = within 30 000, 2 = above 30 000, 3 = unclear
Primary expenditure items	1 = medical expenses, 2 = necessities and transportation, and 3 = unclear
Social support
Marital status	1 = married, 2 = single, 3 = divorced, and 4 = widowed
Hospital level	1 = tertiary hospitals, 2 = secondary hospitals, and 3 = primary hospitals
Social security
Payment type	1 = employee medical insurance, 2 = resident medical insurance, 3 = new rural cooperative medical care, 4 = other medical insurance, 5 = self-funded

Propensity score matching improves group comparability by adjusting for confounding factors to the greatest extent possible. By using matching, regression adjustment, and stratification techniques, it demonstrates a “pseudo-randomization” effect, often referred to as “post-hoc randomization.”¹⁵ This study employed 1:1 propensity score matching based on the medical satisfaction and dissatisfaction groups. Predictive variables included sex, age, marital status, educational status, occupational status, annual income, primary expenditure items, payment type, and medical institution grade. A matching tolerance of 0.02 was set.

Statistical Methods

This study utilized bias score matching methods to balance differences between overall clinic visit satisfaction and dissatisfaction. Propensity score matching is a statistical technique that compares treatment group samples with a balanced distribution of confounding factors, based on propensity scores (PS), to evaluate between-group effects.¹⁶ The distribution of last visit experience-related variables among participating community residents followed normal distribution and homoscedasticity assumptions. Group comparisons were conducted using chi-square tests or analyses of variance, with a significance level of α = .05.

We employed the Pearson correlation test to assess the linear relationship between waiting times for medical visits and overall patient satisfaction. This analysis helped us determine the strength and direction of the correlation between these 2 continuous variables. Additionally, to explore in depth the specific impact of waiting times on patient satisfaction, we conducted a linear regression analysis. This regression model was used to evaluate the influence of satisfaction with duration (as the independent variable) on overall medical satisfaction (as the dependent variable), considering other potential control variables. In both of these statistical methods, we set a significance level of α = .05, meaning that results are considered statistically significant only if the P-value is less than .05, indicating that the correlation or difference is credible.

Result

Baseline Data Comparison for Propensity Score-Matched Subjects

A total of 2629 questionnaires were collected, with 2356 deemed valid, yielding an effective recovery rate of 89.6%. Overall, 2141 patients expressed satisfaction with their last hospital visit, resulting in an overall satisfaction rate of 90.9%. Following bias score matching, 13 pairs of exact matching were obtained, where both residents shared the same covariates, and 202 pairs of fuzzy matching were found, denoting covariate “similarity” between residents. The study selected 215 successfully matched pairs of residents, as illustrated in Figure 2.

Figure 2.

Flow chart of propensity score matching.

Prior to matching, statistically significant differences were observed between the satisfied and unsatisfied groups in terms of age, marital status, educational status, payment type, and occupational status (P < .05). However, post-matching analyses revealed no significant statistical differences in age, marital status, payment type, and occupational status, while remaining educational levels and medical institution grades continued to show no significant differences. This highlights the substantial improvement in the balance between both groups after propensity score matching, as outlined in Table 3.

Table 3.

Comparison of the Baseline Data Between the Satisfied and Unsatisfactory Groups.

Project	Before PSM processing				After PSM processing
Project	2141 patients were satisfied	215 patients were unsatisfactory	χ²	P	215 patients were satisfied	215 patients were unsatisfactory	χ²	P
Sex			0.227	.634			1.862	.172
Female	811	85			99	85
Male	1330	130			116	130
Age			33.405	.000			4.378	.112
Under 30 years old	1411	178			162	178
31-50 years old	720	34			51	34
Over 50 years old	10	3			2	3
Marital status			63.746	.000			3.365	.339
Married	1165	58			64	58
Unmarried	896	150			144	150
Divorce	65	6			3	6
Widowed	15	1			4	1
Educational level			7.795	.020			6.106	.047
Below senior high school and technical secondary school	760	56			70	56
Junior college and undergraduate	1277	148			125	148
Master’s degree or above	104	11			20	11
Payment type			16.224	.003			7.486	.112
Employee medical insurance	771	56			35	56
Residents’ medical insurance	687	75			80	75
New rural cooperation medical treatment	512	55			66	55
Other health insurance	112	15			22	15
Self-pay	59	14			12	14
Medical institution grade			2.179	.336			7.472	.024
Tertiary hospitals	1034	115			87	115
Secondary hospital	850	78			97	78
Primary hospitals	257	22			31	22
Employment status			66.619	.000			0.000	1.000
Employed	2021	171			171	171
Unemployed	120	44			44	44
Per capita annual income			14.626	.001			0.062	.969
Within 30 000	1360	152			152	152
More than 30 000	798	53			54	53
Unsure	46	10			9	10
Major expenditures			4.585	.101			0.202	.904
Medical treatment	1	25			1	1
Food	211	2109			212	211
Unsure	3	7			2	3

After propensity score matching (PSM), we observed changes in the associations and significance levels of specific variables between the satisfied and dissatisfied patient groups. This reinterpreted analysis takes into account statistical values from Table 3 to provide a more comprehensive understanding.

Sex, which had chi-square (χ²) value of 1.862 and P value (P) of 0.172, along with age, with χ² of 4.378 and P of 0.112, and marital status, with χ² of 3.365 and P of 0.339, were no longer significant factors in determining patient satisfaction after PSM.

However, educational level (χ² = 6.106, P = .047) and medical institution grade (χ² = 7.472, P = .024) became significant factors in patient satisfaction post-PSM. These results suggest that patients with higher education levels may have specific expectations regarding the quality of healthcare services, giving importance to the grade of the medical institution.

The payment type, with χ² of 7.486 and P of 0.112, lost its significance after PSM processing but remains a potential factor to explore further. Employment status (χ² = 0.000, P = 1.000) and per capita annual income (χ² = 0.062, P = .969) retained their non-significant associations with patient satisfaction. While major expenditures revealed no significant effect (χ² = 0.202, P = .904), it is still essential to consider potential socioeconomic factors that could influence patient satisfaction.

The results of Table 3 reveal that, after controlling for confounding factors through propensity score matching, educational level and grade of the medical institution emerge as significant determinants of patient satisfaction. This highlights the importance of providing high-quality, patient-centered healthcare services, particularly in medical institutions attended by patients with diverse educational backgrounds.

Correlation Analysis of Visit Duration and Visit Satisfaction Following Propensity Score Matching

After addressing potential confounding factors through propensity score matching, a Pearson correlation analysis was performed to examine the relationship between “overall visit satisfaction” and factors such as education level, medical institution level, and satisfaction with various aspects of the visit, such as the duration of transportation to the hospital, time required for registration, waiting time for consultation, length of communication with doctors, waiting time for physical examination, time required to receive medications, and time necessary to process medical insurance payments. Results revealed a significant statistical correlation between the satisfaction of duration for all aspects and the level of medical institutions, and overall patient satisfaction post-matching. Detailed findings can be found in Table 4.

Table 4.

Pearson Correlation Test Results for Visit Satisfaction and Each Visit Duration Component.

Variables	Overall Patient Satisfaction	degree of education	Grade of medical institution	Satisfaction with Time for Registration	Satisfaction with Time for Consultation Waiting	Satisfaction with Length of Doctor Communication	Satisfaction with Time for Physical Examination Waiting	Satisfaction with Time to Get Medicine	Satisfaction with Time for Medical Insurance Payment	Satisfaction with Transportation Time to Hospital
Overall patient satisfaction	1	.021	−.125^b	−.293^b	−.216^b	−.230^b	−.238^b	−.190^b	−.210^b	−.398^b
Degree of education		1	−.084	.029	.100^a	.038	.016	.057	−.027	.049
Grade of medical institution			1	−.031	−.023	.071	−.042	.007	.033	.083
Satisfaction with time for registration				1	.691^b	.648^b	.560^b	.637^b	.540^b	.239^b
Satisfaction with time for consultation waiting					1	.626^b	.640^b	.659^b	.575^b	.194^b
Satisfaction with length of doctor communication						1	.626^b	.630^b	.586^b	.200^b
Satisfaction with time for physical examination waiting							1	.638^b	.630^b	.198^b
Satisfaction with time to get medicine								1	.643^b	.162^b
Satisfaction with time for medical insurance payment									1	.168^b
Satisfaction with the length of transportation to the hospital										1

At the .05 level (2-tailed), the correlation was significant.

At the .01 level (2-tailed), the correlation was significant.

Table 4 displays a matrix of Pearson correlation coefficients for overall patient satisfaction, degree of education, the grade of medical institution, and various aspects of visit duration. The results demonstrate several significant correlations, as indicated by ** for P < .01 and * for P < .05, offering deeper insights into factors affecting satisfaction levels.

Notably, overall patient satisfaction exhibits a strong, negative correlation with satisfaction regarding the time required for registration (r = −.293, P < .01), waiting for consultation (r = −.216, P < .01), length of communication with doctors (r = −.230, P < .01), waiting for physical examination (r = −.238, P < .01), time to get medicine (r = −.190, P < .01), time for medical insurance payment (r = −.210, P < .01), and transportation time to the hospital (r = −.398, P < .01). These negative correlations indicate that as the duration of each component increases, overall patient satisfaction decreases. It suggests that shorter durations for these components may lead to higher patient satisfaction.

Additionally, degree of education shows a weak, positive correlation with satisfaction in waiting for consultation time (r = .100, P < .05), indicating that patients with higher education might be more sensitive to waiting time for consultations.

These findings underline the importance of addressing factors such as waiting times, duration of doctor-patient communication, and access to medical facilities to improve patient experiences and satisfaction levels. Healthcare institutions should strive to develop efficient, patient-centered approaches to enhance service quality, catering to the diverse needs and expectations of patients of varying educational backgrounds and other sociodemographic factors.

Logistic Regression Analysis of Satisfaction Alongside Visit Duration Satisfaction Following Propensity Score Matching

The Pearson correlation analysis revealed that satisfaction across all time-related aspects was statistically significantly associated with overall visit satisfaction after matching. Conducting logistic regression with all time duration satisfactions and medical institution grade as independent variables, and overall visit satisfaction as the dependent variable resulted in a chi-square value of 165.605 and P < .001, suggesting a statistically significant model. With P = .171 > .05, the null hypothesis is accepted, indicating a well-fitting model and a prediction accuracy of 74%. Moreover, the VIF of all independent variables was substantially below 10, confirming no collinearity between the independent variables.

Binary logistic regression analysis results highlighted the grade of medical institutions, satisfaction with transportation duration to the hospital, waiting time for consultation, and registration processing as the main influencing factors of overall medical satisfaction. As the grade of hospital institutions rises, overall medical satisfaction is more prone to dissatisfaction. Compared to residents satisfied with registration duration, those dissatisfied with registration (β = −1.805, OR = 0.165) or generally satisfied (β = −1.012, OR = 0.363) were more likely to be dissatisfied with their overall medical experience. Furthermore, residents with general (β = −1.272, OR = 0.280) or higher satisfaction (β = −.950, OR = 0.387) were more probably dissatisfied overall. The likelihood of residents who are satisfied with transportation time was 1.049, 1.084, and 1.213 times greater than residents who were not satisfied, less satisfied, or possessed a neutral attitude. For more details, refer to Table 5.

Table 5.

Logistic Regression Model of Satisfaction of Clinic Visits and Satisfaction With Each Visit Duration After the Propensity Score Match.

	B	SE	Wald χ²	P	OR	95%CI
	B	SE	Wald χ²	P	OR	Lower limit	Superior limit
Grade of medical institution (Compared to Level I hospital)			8.226	.016*
Tertiary hospital	−1.104	0.407	7.353	.007*	0.331	0.149	0.736
Secondary hospital	−0.633	0.406	2.425	.119	0.531	0.240	1.178
Satisfaction with the time required for registration (Compared to satisfactory)			11.040	.026*
Dissatisfied	−1.839	1.234	2.223	.136	0.159	0.014	1.784
Unsatisfied	−1.805	0.670	7.264	.007*	0.165	0.044	0.611
Neutral	−1.012	0.470	4.651	.031*	0.363	0.145	0.912
Highly satisfied	−0.275	0.415	0.439	.508	0.760	0.337	1.713
Satisfaction with waiting time (compared to satisfied)			15.759	.003*
Dissatisfied	2.152	1.244	2.992	.084	8.604	0.751	98.586
Unsatisfied	0.131	0.629	0.043	.836	1.140	0.332	3.911
Neutral	−1.272	0.503	6.383	.012*	0.280	0.105	0.752
Highly satisfied	−0.950	0.431	4.854	.028*	0.387	0.166	0.900
Satisfaction with time spent communicating with the doctor (compared to satisfied)			2.734	.603
Dissatisfied	−0.193	1.408	0.019	.891	0.824	0.052	13.005
Unsatisfied	−0.182	0.689	0.069	.792	0.834	0.216	3.218
Neutral	−0.654	0.516	1.606	.205	0.520	0.189	1.430
Highly satisfied	−0.643	0.451	2.032	.154	0.526	0.217	1.273
Satisfaction with examination waiting time (compared to satisfied)			3.510	.476
Dissatisfied	−1.753	1.468	1.426	.232	0.173	0.010	3.079
Unsatisfied	−1.171	0.671	3.043	.081	0.310	0.083	1.156
Neutral	−0.569	0.492	1.334	.248	0.566	0.216	1.486
Highly satisfied	−0.443	0.428	1.071	.301	0.642	0.278	1.486
Satisfaction with the time required for medication acquisition (compared to satisfied)			6.327	.176
Dissatisfied	0.477	1.039	.211	.646	1.612	0.210	12.359
Unsatisfied	0.687	0.613	1.254	.263	1.988	0.597	6.615
Neutral	1.028	0.521	3.898	.048	2.796	1.007	7.758
Highly satisfied	1.051	0.434	5.858	.016	2.862	1.221	6.706
Satisfaction with the time required for Medicare payments (compared to satisfaction)			9.163	.057
Dissatisfied	0.435	0.890	.239	.625	1.546	0.270	8.844
Unsatisfied	0.468	0.622	.565	.452	1.596	0.472	5.403
Neutral	−0.288	0.476	.366	.545	0.750	0.295	1.906
Highly satisfied	0.641	0.439	2.133	.144	1.898	0.803	4.484
Satisfaction with transportation duration to the hospital (compared to satisfied)			45.157	.000*
Dissatisfied	−3.020	0.899	11.271	.001*	0.049	0.008	0.285
Unsatisfied	−2.476	0.545	20.676	.000*	0.084	0.029	0.244
Neutral	−1.545	0.379	16.634	.000*	0.213	0.101	0.448
Highly satisfied	−0.183	0.331	.306	.580	0.833	0.436	1.593
Constant	2.852	0.553	26.616	.000*	17.314

P < .05.

Table 5 presents the results of our logistic regression model of satisfaction with clinic visits and satisfaction with each visit duration following propensity score matching. The refined analysis provides valuable insights into the effects of waiting time satisfaction on community resident medical institution satisfaction.

Our findings highlight that factors such as the grade of medical institution, satisfaction with registration duration, satisfaction with consultation waiting time, satisfaction with time spent communicating with the doctor, satisfaction with examination waiting time, satisfaction with time required for medication acquisition, satisfaction with time required for Medicare payments, and satisfaction with transportation duration to the hospital significantly contribute to overall patient satisfaction levels. By improving these aspects, community healthcare institutions can enhance overall patient experiences and satisfaction levels, leading to a more robust healthcare system meeting the community’s needs.

Discussion

Sociodemographic Characteristics: A Pathway to Minimizing Patient Disparities

An initial unadjusted difference test revealed significant variations in age, marital status, education level, payment type, occupational status, and per capita annual income. Previous investigations indicated that elderly individuals experienced less reduction in their visits, and individuals with lower educational attainment tended to evaluate the healthcare system less favorably.¹⁷ Those with stable income were 1.25 times more satisfied than those without, and higher income individuals were better positioned to procure medical insurance.²

The disparity in sociodemographic attributes underscores the critical issue of social deprivation – an evaluation concept of regional resource equity.¹⁸ Reflecting opportunities for individuals or groups to access medical care and employment in their communities, social deprivation is assessed through 7 dimensions, including income, education, health, housing conditions, and living environment. These dimensions align with factors related to medical satisfaction, suggesting an overarching need to understand and manage sociodemographic characteristics of patients to facilitate equitable health resource distribution.^2,19,20

This also suggests that service providers in hospitals should pay attention to understanding and managing the sociodemographic characteristics of the patients. Special services should be arranged to guide and assist elderly patients and those without medical insurance, ensuring that such groups receive the necessary medical care. From the perspective of public health resource policy makers, encouraging community participation in the allocation and management of medical resources is important to ensure that the needs of marginalized groups are met. Additionally, the rational distribution of health resources should be considered a crucial element in the formulation of health development policies. Establishing a system to monitor and evaluate the distribution of medical resources is essential to ensure the fairness and effectiveness of these policies.

Enhancing Public Understanding of Hierarchical Diagnosis and Treatment

According to the General Hospital Classification Management Standards (Trial Draft) issued by the Ministry of Health, hospitals are classified based on their functions, medical services, facilities, and technical capabilities. Remarkably, over 50% of individuals opt for tertiary medical institutions for common or recurrent diseases,²¹ resonating with research by Shuyun²² that observed a protective role of medical institution grades in the research result model.

The reason for dissatisfaction in high-level hospitals is that their brand image does not align with patients’ expectations, leading to unsatisfactory medical experiences. This is related to the disparity in patients’ understanding of healthcare knowledge.²³ Moreover, due to the high patient volume and busy medical staff in high-level hospitals, personalized medical services and sufficient communication are often lacking, resulting in patients feeling insufficiently attended to. In 2015, the General Office of the State Council issued the ‘Guiding Opinions on Promoting the Construction of a Tiered Diagnosis and Treatment System,’ aiming to advance tiered medical care, balance the allocation of medical resources, improve medical efficiency, reduce the burden on high-level hospitals, and rationally divert patient flow, thereby fundamentally reducing waiting times in large hospitals. In 2022, researchers like Qing²⁴ pointed out that the current level of public awareness about tiered diagnosis and treatment is still low. This suggests that communities should strengthen the promotion of knowledge about tiered diagnosis and treatment from the public’s perspective, enhance residents’ understanding of related knowledge, reduce resistance or misunderstandings about tiered diagnosis and treatment, understand residents’ opinions and feedback, thereby optimizing medical-seeking behavior, enhancing the medical experience, and improving satisfaction with medical visits. Strengthening professional training for community hospital staff is also important so that they can better explain and promote the tiered diagnosis and treatment system to residents through lectures, health consultations, and other educational activities, increasing the dissemination of the system among the public

Enhancement of Hierarchical Diagnosis and Treatment System

Following propensity score matching to balance confounding factors, this study found the length of visits affecting community residents’ satisfaction primarily comprised transportation time to the hospital, registration duration, and waiting time for examination. This observation is consistent with studies such as that by Xin et al,¹ proposing the convenience of medical visits and waiting time as evaluative dimensions for treatment satisfaction.

Dissatisfaction with the length of medical transportation time may be interpreted as an indication of imbalanced allocation of medical resources. High-quality medical facilities, advanced medical technology, and instruments are often concentrated in secondary and tertiary hospitals and specialist facilities. These are typically located in first-tier cities, leading patients unfamiliar with the hierarchical diagnosis and treatment process, and those requiring primary medical services, to favor such facilities over community hospitals.²⁵ This choice results in extended travel times to hospitals, as demonstrated in studies by Zhang Suhua et al.²⁶

Efforts to encourage the graded diagnosis and treatment model of “minor illness in the community, major illness in the hospital, and rehabilitation back to the community,” initiated in 2009, have yet to sufficiently resolve the issue of “difficulty in seeing a doctor.” High-quality medical resources remain difficult to access, and the concept of graded diagnosis and treatment has yet to fully permeate the public consciousness.²⁷

The author believes that the use of advanced information technology, such as telemedicine consultations, online registration, and online health consultation services, can provide professional medical advice and services to residents in remote areas. Simultaneously, the promotion of the family doctor system can be encouraged. Residents can be encouraged to sign up with family doctors, providing more convenient and personalized primary healthcare services, reducing unnecessary long-distance medical treatment, thereby improving patient satisfaction.

Guiding Patient Populations and Optimizing the Medical Process

Patients with general or higher satisfaction with waiting clinic times showed lower overall satisfaction than those with satisfied attitudes. Studies both domestically and internationally concur that prolonged waiting times can diminish patient willingness for return visits and potentially increase the risk of serious complications in consultation patients.⁴ This suggests that hospitals should enhance their hardware facilities, streamline outpatient service layouts, optimize treatment processes, and foster doctor-patient communication and training.

Multiple registration methods are currently available, including phone and online appointments. Still, traditional offline registration remains prevalent, often tied to the individual’s understanding of registration procedures.²⁸ In response to these challenges, initiatives such as Guangzhou Women and Children’s Health Care’s emergency registration comprehensive appointment service have demonstrated the potential for improvement. This approach simplifies the medical service process through online registration and payment, significantly boosting outpatient monthly booking rates.^29
-32

Hospitals and managers should promote multi-channel appointment registration and direct outpatient patients through volunteer services, guiding patients to select the appropriate appointment department. Fostering a “first general practice before specialty” appointment strategy can effectively manage patient flow, reducing waiting times, and eventually becoming the mainstream approach for residents’ medical appointments.^33
-36

Although not included in this study’s model, the remainder of the visit duration exhibited a significant correlation with visit satisfaction, emphasizing that timeliness is a key attribute in evaluating the quality of medical services. A competent medical service system should strategically manage patient waiting times to avoid protracted waiting and short consultation times, thus affecting patients’ ability to receive timely medical care.⁴

Limitations and Future Research

This study has certain limitations: First, the questionnaire was distributed using a combination of offline and online methods through QR codes, targeting only the digitally literate population. Therefore, the conclusions drawn from this study may not be extrapolated to represent the overall population. Second, unlike the relatively mature primary healthcare systems in Western countries, the trust level of Chinese residents in primary healthcare institutions is comparatively lower, and the tiered diagnosis and treatment system is still evolving. Hence, the findings of this study are influenced by the specific medical environment of China and may not reflect the patient experience in a mature and balanced healthcare system. This implies that future research needs to consider the variations in medical policies across different countries and regions more thoroughly to accurately assess patient satisfaction and to provide improvement strategies that are more aligned with the specific national conditions.The last point,as a retrospective study, there may be memory biases or effects of unique personal events on participants’ emotions. Future studies may consider real-time outpatient surveys to accurately capture visit length and eliminate the gap between memory and reality.

Conclusion

In conclusion, this study, based on propensity score matching, examined the relationship between community residents’ medical treatment satisfaction and various types of medical treatment duration satisfaction. The results revealed significant effects of baseline data and confounding factors on satisfaction. Factors such as hospital grade, satisfaction with the duration required for registration and transportation to the hospital, and satisfaction with the waiting time for medical treatment notably influenced residents’ overall medical treatment satisfaction. Therefore, improvements in the grading of diagnosis and treatment, the rational guidance of the medical population, and the optimization of the medical treatment process are necessary.

Footnotes

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: General program of Guangdong Natural Science Foundation (2022A1515012192), Medical research fund project of Guangdong Province (A2021500, A2022535), Shantou science and technology plan’s medical and health category project (190716185262435, SFK [2019] No. 106-10). Philosophy and Social Sciences Planning Foundation Discipline Co-construction Project of Guangdong Province of 2022 (GD22XXW10) and Undergraduate teaching quality and teaching reform project of Guangdong Province in 2022 (Yue Gao Jiao Han[2023]4-581). Clinical Teaching Base Teaching Reform Research Project of Guangdong Province in 2021(2021JD062). 2021 Guangdong Provincial Science and Technology Special Fund (“Major Tasks + Task List”) (210728156901595).2021 Guangdong Provincial Quality Engineering Project for University Student Practical Training Base: ‘Heart’ Practical Training Base for University Students (Yue Gao Jiao Han [2021] No. 19-18).

ORCID iD

Guanhua Fan

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