Exploring the Effect of Doctor-Nurse Collaboration on Overall Satisfaction Among the Older Patients: A Moderated Mediation Model of Responsiveness and Environment

Introduction

The World Social Report 2023 indicates that the global population of older adults continues to grow, and the number of people aged 65 and over is projected to increase from 761 million in 2021 to 1.6 billion by the mid-century.¹ Alongside the increasing elderly population, multimorbidity and comorbidity will become more prevalent, and age-related declines in sensory perception will often impede older adults’ ability to access healthcare, leading to greater difficulties in interacting with medical staff.² These challenges underscore the growing demand for professional care and emotional support for older patients.

Outpatients constitute a critical component of the healthcare system worldwide.³ In China, outpatient visits reached an estimated 9.55 billion in 2023, reflecting a year-on-year increase of 13.5%, with older patients accounting for a substantial proportion of these visits.⁴ Given the specific characteristics of older patients mentioned above, to enhance the efficiency of outpatient visits, satisfaction, referring to patient assessments of care quality and the clinical encounter, serves as a critical indicator of patient experience and a driver of continuous service improvement.⁵ Prior studies suggest that the overall satisfaction of older patients is influenced by the service provided by doctors and nurses, and may also vary with their mood within the healthcare environment.^6,7 Additionally, from the patient’s perspective, whether their needs are met responsively is an important consideration as well.⁸ Understanding these interconnected factors is therefore crucial for improving overall satisfaction among older patients.

Doctor-nurse collaboration facilitates effective healthcare delivery for older patients.⁹ It comprises two key elements.⁹ Involving joint development of diagnosis, treatment, and nursing plans to promote patient recovery, interprofessional collaboration as “doctor explained why and how to use medicine” and “nurses answer the questions correctly” is a key component of doctor-nurse collaboration centered on patient-focused analysis of conditions.¹⁰ Substantial evidence underscores the role of effective interprofessional collaboration and clear explanations in delivering high-quality care, which in turn promotes patient safety and enhances satisfaction.¹¹ Encompassing “communication of diagnosis” and “behavior of nurses”, attitudinal alignment constitutes another fundamental dimension of doctor-nurse collaboration. Studies indicate that mutual respect, trust, and efficient communication between doctors and nurses improve responsibility sharing and therapeutic decision making, which entails the ability to communicate with patients in a clear and patient manner.^12,13 Additionally, research from a psychological perspective further corroborates the finding that positive communication behaviors and attitudes directly increase patient satisfaction.¹⁴ Therefore, this study examined the hypothesis that doctor-nurse collaboration (including interprofessional collaboration and attitudinal alignment) directly affects the overall satisfaction of older patients.

Responsiveness to patient needs refers to the capability of healthcare providers to address patients’ requests or complaints in a timely and effective manner, thereby achieving expected treatment goals.¹⁵ Drawing on the SERVQUAL model proposed by Parasuraman, the dimension of “responsiveness” is defined as the willingness of service providers to understand and fulfill individual needs, ultimately contributing to higher patient satisfaction.¹⁶ Within this framework, Kashfi¹⁷ found that clinical service such as doctors’ behavior, attitude of medical staff play a critical role in shaping the dimensions of “responsiveness” and “empathy”. Similar findings were reported in a study conducted in Africa.¹⁸ Furthermore, guided by the 4M framework, the internationally recognized Age-Friendly Health System encourages health care providers to identify the priorities of older patients and focus on addressing their core needs, which has been demonstrated to improve both patient experience and satisfaction.¹⁹ Consequently, based on insights from existing research, as the delivery of responsive care is largely dependent on healthcare providers, this study aimed to investigate the mediating role of responsiveness to patient needs in the relationship between doctor-nurse collaboration and overall satisfaction.

The outpatient environment encompasses multiple dimensions such as ventilation, temperature, cleanliness, and maintenance of medical order, representing a comprehensive physical and humanistic environment that integrates space, processes, facilities, services, and management.²⁰ A comfortable environment has been shown to improve the patient experience, increase tolerance for waiting times, and positively influence emotional responses and satisfaction.^21,22 A study based on the annual National Public Hospital Patient Satisfaction Survey (NPHPSS) indicated that outpatient satisfaction lags behind inpatient satisfaction, with environmental quality being identified as a significant moderating factor.²³ Research from Jiangxi Province found that the environment and facilities positively affect patient satisfaction, exhibiting a positive synergistic effect with quality-of-care indicators such as communication regarding diagnosis, explanation of examination reports, and respect for privacy.²⁴ This relationship is also corroborated by a study from the United States, which emphasizes that collaborative healthcare teams depend on an effective care environment to support patient health and satisfaction.²⁵ Accordingly, this study explored the moderating role of the outpatient environment. In this study, although the outpatient environment is conceptually multidimensional, this study employed a single-item global measure (eg, “satisfaction with the outpatient environment”) to represent the broader construct. This approach is supported by prior studies,^23,26 which indicate that global assessments adequately represent overall environmental quality and enable a holistic examination of its role in patient satisfaction.

Although previous studies have examined the independent effects of doctor-nurse collaboration, responsiveness to patient needs, and outpatient environment on patient satisfaction, there remains a scarcity of research on the interactions between these three factors.²⁴ Additionally, the synergistic effects of interpersonal interactions (ie, between doctors and nurses) and the influence of environmental factors during this period have been mentioned less frequently.²⁷ Moreover, moderated mediation analyses targeting older patients, a group potentially facing healthcare access challenges for outpatients, have not been fully explored. Accordingly, this study innovatively integrated both mediating and moderating variables to conduct an in-depth analysis of the impact of doctor-patient collaboration on satisfaction among older patients into a path model. Based on the above, this study examines the following hypotheses:

Hypothesis 1: Doctor-nurse collaboration (interprofessional collaboration and attitudinal alignment) directly affects overall satisfaction.

The findings of this study not only provide a novel perspective for outpatient satisfaction but also offer practical guidance for understanding the healthcare needs of older patients. The results will assist hospital managers in considering that doctor-nurse collaboration enhances patient satisfaction by promptly addressing the needs of older patients, while a favorable clinical environment helps strengthen this effect. The hypothesized path model is depicted in Figure 1: “a1” and “a2” denote the hypothesized paths from the independent variables (X1, X2) to the mediator (responsiveness of patient needs); “b” signifies the path from the mediator to the dependent variable (overall satisfaction), while “c1” and “c2” represent the direct paths from the independent variables to the dependent variable; “w1” and “w2” correspond to the hypothesized moderating effects of the outpatient environment on the “a1” and “a2” paths, respectively.

Figure 1 Research hypothesis model diagram. Notes: X1 represents interprofessional collaboration, and X2 represents attitudinal alignment. Paths a1 and a2 denote the hypothesized effects from the independent variables (X1, X2) to the mediator (responsiveness to patient needs). Path b signifies the effect from the mediator to the dependent variable (overall satisfaction), while c1 and c2 represent the direct effects from the independent variables to the dependent variable. Labels w1 and w2 correspond to the hypothesized moderating effects of the outpatient environment on paths a1 and a2, respectively.

Methods

Study Sample

This study utilized a simple random sampling method based on Wenjuanxing, a prominent online questionnaire service in China, to conduct a cross-sectional study from June to July 2025 in the outpatient department of a tertiary hospital to assess patient satisfaction among older patients. Collected data were retrieved from an online analysis system. Altogether 9520 patients consented to participate in this study, and we successfully collected 9133, yielding a high response rate of 95.93%. According to the inclusion criteria, each patient (1) was 60 years or older, (2) was cognitively able to complete the questionnaire, (3) was admitted to the outpatient department, and (4) provided informed consent and willing to participate in the investigation. We excluded patients with severe conditions and/or infectious diseases that may cause danger to themselves or others.

This study has certain limitations. Self-reported measures may introduce bias, as participants could overstate positive experiences. Furthermore, the online survey design may have excluded older patients without internet access, limiting the generalizability of the findings. Future research should aim to incorporate objective measures and employ broader recruitment strategies.

Measures

Instrument and Scaling

Building on prior validated instruments in this field,^27,28 the questionnaire was not newly devised; its items were refined by incorporating expert feedback and aligning with the established questionnaires (Supplementary Material 2). And the original Chinese version is available in Supplementary Material 1. This questionnaire includes 33 items grouped into three sections: (1) demographic characteristics; (2) core healthcare process; (3) satisfaction evaluation system of medical experience. Patient satisfaction was rated on a 5-point Likert scale (1  =  “very dissatisfied”, 5  =  “very satisfied”) with higher scores indicating higher level of satisfaction. The duration of filling out this online questionnaire was approximately five – eight minutes. In this study, Cronbach’s alpha was 0.969, the KMO measure was 0.963, and the χ² value of Bartlett’s test of sphericity was 6,717,92.782 (p < 0.001), indicating excellent internal consistency.

Data Coding

Gender was a binary variable and categorized into two groups: 1 = female and 0 = male. Respondents aged 60 years and above: 60–80 = 0, > 80 = 1. Education status was an ordinal variable and was delineated as follows: 0 = high school and below, 1 = undergraduate/junior college, and 2 = postgraduate and above. Registered physician’s title had three categories: senior resident = 0; associate chief physician = 1; and chief physician = 2. Patient source: non-local patients = 0; local patients = 1. Payment method: 0 = no medical insurance; 1 = medical insurance. Type of outpatient registration had two categories: general outpatient = 0; VIP outpatient = 1. Satisfaction items, for example, doctor explained why and how to use medicine, doctor diagnosed diseases, responsiveness of patient needs, outpatient environment, behaviour of nurses, and nurses answer the questions correctly were all measured on the 5-point Likert scale.

Variable Definitions

To test the hypotheses proposed in our moderated mediation model, key variables were operationalized as follows. Specifically, the interaction terms were constructed by multiplying the corresponding items derived from the questionnaire (eg, doctor explained why and how to use medicine, nurses answer the questions correctly).

1. Independent Variables (X): it is represented by two distinct latent interaction terms, X1 and X2.
2. X1 (interprofessional collaboration): doctor explained why and how to use medicine × nurses answer the questions correctly. This pairing captures the dynamic interaction inherent in interprofessional collaboration, where effective professional service arises from doctor’s clear explanations and nurse’s accurate responses to clinical inquiries, thereby serving as a metric for the synergistic effect of the professional level delivered by healthcare providers.
3. X2 (attitudinal alignment): how doctors communicate a diagnosis × satisfaction with behaviour of nurses. It reflects the attitudinal dimension, shaped by the doctor’s communication style and the nurse’s etiquette norms, thereby capturing the synergistic effect of healthcare providers’ communication skills and overall demeanor.
4. Mediator (M): responsiveness to patient needs. This term indicates whether patient needs are acknowledged and addressed in a timely manner by the healthcare staff.
5. Moderator: outpatient environment. It mainly refers to the physical facilities and service environment experienced by patients in the hospital. In this study, outpatient environment functions as a moderator between doctor-nurse collaboration (X1, X2) and patient satisfaction (Y). Specifically, a supportive environment is likely to strengthen this positive relationship, whereas a poor environment may weaken it.
6. Dependent Variable (Y): overall satisfaction. This term captures the patient’s overall assessment of the outpatient visit.

Control Variables

The control variables in this study included gender, educational status, and registered physician’s title.

Statistical Analyses

Descriptive statistics using SPSS (version 27.0) were applied to present the characteristics of the 9133 respondents. Data analysis was conducted using Stata 18.0, with a significance level set at 0.05. We employed path analysis with maximum likelihood estimation to test the hypothesized moderated mediation model. The significance of indirect effects was evaluated using bias-corrected bootstrap confidence intervals based on 5000 samples. We also assessed potential multicollinearity by examining variance inflation factors (VIFs). Finally, robustness tests were conducted to examine the stability of the model when the sample size was reduced to 8000 or when additional control variables were included.

Quality Control

In this study, data were collected by a team of six researchers, all of whom had received standardized training before conducting the survey. To verify the reliability and stability of the results, this study examined the robustness of the model by reducing the sample size to 8000 and incorporating additional moderating variables. Prior to the questionnaire, participants were informed of the research objectives and completed the informed consent process to assure data confidentiality.

Results

Demographic Characteristics of Older Patients in Outpatient

In total, 9133 older patients were included in this study (Table 1). Of the participants, 51.6% were male. Nearly half of the respondents had an undergraduate or junior college degree (56.7%). A total of 38.3% sought care from an associate-chief physician. Most of the participants (86.5%) had health insurance.

Table 1 Descriptive Characteristics of the Respondents (n = 9133)

Correlation Analysis

Before conducting the moderated mediation analysis, we assessed the Pearson correlation coefficients among the variables to examine their interrelationships. This preliminary step focused on the key variables (defined in Section “2.2.3 Variable Definitions”) outlined in the proposed model. Specifically, we tested the correlations between: (1) interprofessional collaboration and responsiveness to patient needs; (2) attitudinal alignment and responsiveness to patient needs; (3) responsiveness to patient needs and overall satisfaction; (4) outpatient environment and responsiveness to patient needs; (5) outpatient environment and overall satisfaction.

As shown in Table 2, all core variables demonstrated significant positive correlations, detailed as follows: (1) interprofessional collaboration was significantly positively correlated with responsiveness to patient needs (r  =  0.689, p  <  0.01); (2) attitudinal alignment was strongly correlated with responsiveness to patient needs (r  =  0.688, p  <  0.01); (3) responsiveness to patient needs exhibited a strong positive correlation with overall satisfaction (r  =  0.486, p  <  0.01); (4) outpatient environment was also positively correlated with both responsiveness to patient needs (r  =  0.745, p  <  0.01) and overall satisfaction (r  =  0.384, p  <  0.01).

Table 2 Spearman Rank-Order Correlations Among Key Variables

Testing the Direct Effect of X1 and X2 on Overall Satisfaction

The first research question aimed to establish a direct-effect model for X1 and X2 on overall satisfaction. After controlling for gender, educational status, and title of registered doctor, the results (Table 3) indicated that the path coefficients of X1 and X2 were 0.647 (p  <  0.001) and 0.648 (p  <  0.001), respectively, demonstrating that the direct effects of X1 and X2 on overall satisfaction were statistically significant and suitable for the mediating effect analysis. This finding supports Hypothesis 1.

Table 3 The Regression Analyses Predicting Overall Satisfaction (n = 9133)

Mediating Role of Responsiveness to Patient Needs

Table 4 indicates that X1 is significantly relationship to overall satisfaction (β = 0.065, p < 0.001), and responsiveness to patient needs partially mediates a positive relationship between X1 and overall satisfaction (β = 0.029, p = 0.005), indicating that professional collaboration between doctors and nurses leads to higher patient satisfaction, both directly and indirectly, through the mediating pathway of response to patient needs. The direct influence of X2 on overall satisfaction, coupled with its indirect influence mediated through responsiveness to patient needs, was assessed using bootstrap 95% confidence intervals, which were (0.063 and 0.068) for the direct effect, and (0.027 and 0.031) for the indirect effect, respectively. Significantly, both intervals notably exclude 0, thereby confirming that X2 impacts overall satisfaction not only in a direct manner but also indirectly through responsiveness to patient needs. These results are consistent with Hypothesis 2. X1 and X2 were positively related to responsiveness to patient needs (Î ² = 0.114, p < 0.001). Responsiveness to patient needs was positive associated with overall satisfaction (Î ² = 0.253, p < 0.001).

Table 4 Multilevel Path Analysis Results of Responsiveness to Patient Needs

Testing the Moderated Effects of Outpatient Environment

To test this hypothesis, overall satisfaction was used as a dependent variable, whereas X1 and X2 were treated as independent variables. As shown in Table 5, after controlling for gender, educational status, and title of registered doctor, none of the interactions were significant except in the outpatient environment. Although educational status did not show statistically significant effects on overall satisfaction (p > 0.05), we retained it in the model based on the absence of new evidence demonstrating a lack of correlation between them. The analysis demonstrated that the outpatient environment had a significant and positive (β = 0.288, p < 0.001, X1; β = 0.289, p < 0.001, X2) moderating effect on the relationship between X1/X2 and outcome. Thus, Hypothesis 3 is supported. The model also showed a good fit for each moderated independent variable.

Table 5 Moderating Effects of Outpatient Environment on the Outcome

Multicollinearity Analysis Among the Independent Variables

Multicollinearity is used to identify excessive intercorrelations among independent variables within a multiple linear regression framework, thereby avoiding estimator perturbations and instability. Prevalent scholarly conventions posit that a VIF exceeding five necessitates caution, and a VIF exceeding 10 is diagnostic of pronounced multicollinearity, mandating rectifying actions. A tolerance statistic falling below 0.1 is conventionally interpreted as evidence of substantial collinearity, prompting the elimination or consolidation of the affected variables. A mean VIF value of 2.08 is significantly lower than the threshold of 5, is typically used to indicate substantial multicollinearity concerns. Similarly, the calculated tolerance level of 0.48 is considerably higher than the conventional minimum standard of 0.10. These results suggest that there are no multicollinearity issues in our model.

Testing the Robustness of the Moderated Mediation Model

The robustness of the moderated mediation model employed in this study was substantiated by the following aspects; the results are shown in Table 6:

1. Regarding the main effects, the coefficients remain statistically significant at the 1% level (B = 0.095, p < 0.001, after sample reduction to 8000; B = 0.094, p < 0.001, after incorporating additional control variables), demonstrating consistency with original Model1 and Model2.
2. For the mediator, the effect estimates exhibited remarkable stability across the altered model specifications, with variations not exceeding 0.004 (p < 0.001).
3. For the control variables, all variables showed consistently small and statistically insignificant coefficients in all models, indicating a negligible influence on the dependent variable.

Table 6 Robustness Tests (Sample Reduction to 8000 or Incorporating Additional Control Variables)

(4) The indirect effect remains highly stable, with the point estimate changing from 0.029 in the baseline model to 0.024 under robustness tests, a variation of approximately 17%, which is below the 20% threshold commonly adopted to indicate robustness in mediation analyses.

The Confirmed Moderated Mediation Model

Figure 2 illustrating the proposed mediating and moderating relationships between the independent variables and the dependent variables. The detailed statistical testing of direct and indirect effects was conducted through regression analysis and bootstrap procedures, consequently, this diagram provides a visual representation of this relationship.

Figure 2 Confirmed moderated mediation model linking X1, X2, responsiveness of patient needs, outpatient environment, and overall satisfaction. Note: X1, interprofessional collaboration. X2, attitudinal alignment. ***P<0.001 (two tailed).

Discussion

This study constructs a moderated mediation model to validate how doctor-nurse collaboration enhances older patients’ overall satisfaction both directly and indirectly through improved responsiveness to their needs, while the outpatient environment critically moderates this indirect pathway. This study transcends previous research that primarily focused on interpersonal interactions by integrating the outpatient environment into the model, revealing its critical role as a moderator in the satisfaction pathway. This underscores the need to study these factors in relation to one another to fully understand patient satisfaction, identify their medical needs, and ultimately mitigate barriers to healthcare access among older patients. Based on cross-sectional survey data, this study offers several important implications for both policy and clinical practice aimed at older patients.

First, the results confirmed a direct positive effect of doctor-nurse collaboration on overall satisfaction. In both models, interprofessional collaboration and attitudinal alignment significantly and positively predicted satisfaction among older adults. Facing complex conditions and often experiencing negative emotions (eg, anxiety, depression), these patients heavily rely on healthcare professionals for support.^29,30 Therefore, when doctors and nurses respond accurately to patients’ professional inquiries, such as medication usage and precautions, their collaborative synergy helps patients to better understand their health status and treatment plans. They are more likely to perceive the medical environment as safe and trustworthy, which fosters equitable communication and enhances treatment adherence. These findings align with Maslow’s hierarchy of needs, suggesting that only when such basic needs of patients as a sense of security during medical visits are met can higher-level needs be satisfied throughout the treatment process, ultimately improving overall satisfaction.³¹ By contrast, the model proposed in this study suggests that such positive interactive effects may be compromised by inappropriate behavior by doctors or nurses. Only when patients simultaneously receive consistent and collaborative support from doctors and nurses in terms of professionalism and attitudes can they perceive the overall effectiveness of the service, leading to increased satisfaction. Consistent with the findings of Gu,³² effective interprofessional communication is crucial for accurately identifying older patients’ needs and ensuring holistic care. This finding also accords with a Greek study.³³ In summary, in addition to considering this collaborative dynamic, the positive attitude and emotional coordination of information conveyors are equally critical factors influencing healthcare services and overall satisfaction among older patients. Evidence from Portugal suggests that interprofessional and team communication is an imperative component of clinical education, in order to optimally address the diverse needs of older patients.³⁴

Second, the study confirms that responsiveness to patient needs plays a crucial mediating role in the transition from doctor-patient collaboration to overall satisfaction. Specifically, inter-professional collaboration and attitudinal alignment significantly enhanced responsiveness to patient needs, which in turn predicted satisfaction among older patients. This indicates that doctor-nurse collaboration is not merely communication and coordination between healthcare workers, but also an operational and patient-centered approach that delivers tangible benefits to patients by responding to patient needs in a timely manner, thereby elevating satisfaction levels. Previous studies have consistently identified timely and actionable responsiveness as one of the strongest predictors of patient satisfaction.³⁵ Additionally, the SERVQUAL model further emphasizes that responsiveness is a manifestation of service efficiency and quality.³⁶ This conclusion is further corroborated by a Nepalese study of outpatient satisfaction.³⁷ Waiting time is another critical indicator of operational efficiency, and has been widely studied in relation to patient satisfaction.³⁸ This aligns with the conclusions drawn by researchers from Africa.^39,40 In summary, responsiveness to patient needs constitutes concrete and actionable behavior that must be systematically implemented. Studies suggest that key performance indicators (KPIs), such as door-to-doctor time, request fulfillment rate, and response rates, can serve as specific and quantifiable metrics for monitoring clinical responsiveness and healthcare service quality.^41–43 Therefore, to genuinely enhance patient satisfaction, healthcare institutions should move beyond superficial gestures, such as explanations, communication, and smiles, and establishing mechanisms for doctors and nurses to collaboratively and rapidly respond to patient needs through team collaboration training is crucial.

Third, the mediating effect of a favorable outpatient environment offers a novel perspective for understanding the mechanisms through which doctor-nurse collaboration influences responsiveness to patient needs. Recent European research indicates that the outpatient environment shapes patient satisfaction indirectly, exerting its influence through a moderating effect on other key variables.⁴⁴ A well-designed environment (eg, clear signage, adequate seating) mitigates age-related burdens, enabling older patients to better concentrate on understanding medical information and, thus, more effectively establish trust and rapport with doctors and nurses.^45,46 Consequently, a favorable outpatient environment acts as both “lubricants” and “amplifiers” in enhancing the positive impact of doctor-nurse collaboration on responsiveness to patient needs, thereby improving overall satisfaction. Conversely, a poor environment can undermine even effective collaboration by increasing patient distress. Thus, this study suggests that improving patient satisfaction requires not only promoting doctor-nurse collaboration but also systematically enhancing healthcare services through environmental optimization to elevate overall satisfaction and quality of service. Tailoring environmental improvements to the needs and characteristics of older patients is referred to as age-friendly environmental design that significantly shapes their healthcare experience, and some studies indicate that the outpatient environment is one of the primary criteria influencing hospital selection among older patients.⁴⁷ For example, a study from Australia showed that bright, modern settings with clear signage and well-placed seating evoke warm and positive feelings, improve visit efficiency, and reduce discomfort, thereby supporting effective communication.⁴⁸ Finally, integrating safety features such as non-slip flooring, wheelchair ramps, and handrails in toilets constitutes a fundamental aspect of age-friendly design, facilitating responsiveness to patient needs and ultimately enhancing the satisfaction of older patients.⁴⁷

Limitation

This study had several limitations. First, this cross-sectional study constrained our capacity to establish strict causal inferences, allowing only the identification of associations among multiple variables. Therefore, future longitudinal or experimental studies could provide more robust evidence regarding the relationship between doctor-patient collaboration and satisfaction. Further exploration of other potential moderating variables, such as educational status and health literacy, and mediating variables, such as anxiety, depression, and socioeconomic status, will contribute to a more comprehensive and in-depth understanding of the underlying mechanisms. Furthermore, as the sample was drawn exclusively from Beijing, the results may not be generalizable to other regions. Consequently, future studies should seek to expand the survey coverage to a national scale to enhance the robustness and general applicability of the conclusions.

Conclusion

This study tested a hypothesized moderated mediation model. The results demonstrate that doctor-nurse collaboration not only directly improves older patients’ overall satisfaction but also exerts an indirect effect through enhanced responsiveness to their needs. Moreover, the outpatient environment positively moderates this indirect pathway. Compared to traditional studies that often examine each variable independently, we incorporated the frequently overlooked variable of the outpatient environment into the doctor-nurse collaboration on a moderated mediation model, emphasizing the necessity of treating the physical environment and interpersonal interactions as an integrated concept for a comprehensive analysis of satisfaction and its influencing factors. Through these validation analyses, we aimed to reduce the difficulties faced by older outpatients and enhance their overall satisfaction.