Research on the Experience of Influencing Elements and the Strategy Model of Children’s Outpatient Medical Services under the Guidance of Design Thinking

Abstract

Faced with the generally poor experience in pediatric outpatient in China, under the guidance of design thinking, based on the analysis and research of the main elements affecting child outpatients’ medical service experiences, this study proposes a set of strategic models that can improve child outpatients’ medical service experiences. Specifically, this study takes Shanghai Xinhua Hospital as a research case, combined with SPSS data statistics software, and comprehensively uses preliminary field research methods, questionnaire descriptive analysis methods, questionnaire satisfaction analysis methods, and questionnaire principal component analysis (PCA) methods as well as the structured interview method; thus, the main elements affecting child outpatients’ medical service experiences were obtained. Then, according to the main elements, a set of child outpatient medical service strategy models is proposed to improve child outpatients’ medical service experiences. Finally, the effectiveness of the strategy model is tested through satisfaction analysis and simulation case verification. The model is a people-centered, sustainable strategic model. With the support of design thinking, the strategic model takes the experience of children as the core improvement point, which is able to fully protect the rights and demands of child patients. At the same time, this strategy model can also reduce the workload of doctors, improve the operational efficiency of hospitals, promote a more equal distribution of medical resources, and reduce medical service costs. More importantly, it also encourages patients and their families to communicate and express their opinions to medical professionals, which can greatly reduce the tension between doctors and patients and effectively avoid doctor–patient conflicts. This has important implications for the sustainability of healthcare. However, this strategy model is only a guiding strategy for improving outpatient care for children. It does not provide detailed solutions around certain specific issues and specific implementations. At the same time, it is not a complex engineering design system but only provides a reference for improving children’s medical services in terms of strategic logic.

1. Introduction

There are about 249 million children under the age of 14 in China, accounting for about 17.88% of the Chinese population and 12.71% of the global child population [1]. In 2022, the annual number of pediatric outpatient and emergency visits in China was approximately 700 million, accounting for 11.6% of all emergency visits [2]. The number of children and the huge demand for children’s medical care have overwhelmed China’s pediatric medical infrastructure [3]. Although China’s children’s outpatient medical services have made great progress in research and medical technology, compared with other countries, it still has many urgent problems that need to be solved [4]. Children are a special group with unique physical and psychological characteristics, and their emotional perception of the hospital is different from that of adults [5,6,7]. They often need more professional, warm, and meticulous medical services [8]. However, at this stage, children’s medical service experience in hospitals is still poor [9]. The main reasons for this phenomenon are that hospitals ignore children’s physical and psychological characteristics and that medical institutions lack the formulation of relevant children’s medical service strategies and the layout of the medical environment [10,11,12,13,14,15].

Therefore, the question of how to improve children’s medical service experiences in hospitals, pediatric outpatient service level, and service efficiency has become a hot issue in the medical industry and society [16,17]. However, as far as children’s medical institutions in China are concerned, most children’s hospitals are still a branch of a larger hospital, providing “non-personalized” large-scale services [18,19,20]. A small number of private children’s hospitals have become aware of the importance of child outpatients’ medical service experiences and have been able to improve children’s experience from a visual perspective through changes in color and space layout [21,22]. However, few children’s medical institutions have improved child outpatient service experiences and hospital outpatient service strategies from a people-centered perspective [23,24]. Therefore, there is an urgent need for a people-centered, innovative, and sustainable concept or method to strengthen and update child outpatients’ medical service experiences and strategies to meet the current social development and the needs of children.

Design thinking in the field of healthcare is a human-centered innovative concept and solution aimed at improving human well-being [25]. It proposes keeping an open mind rather than a specific, rigid methodology [26]. Its related practices have been widely used in the innovation of products, environments, workflows, and mission statements in the medical context, and it also brings new perspectives to medical professionals [27]. Already, many healthcare institutions around the world have employed design teams to improve patient care [28,29,30]. Major companies around the world have gradually adopted design thinking as an important strategy to promote medical innovation [31,32,33,34,35,36,37,38,39]. At the same time, healthcare issues usually have strong ambiguity and uncertainty. Design thinking can help medical institutions better understand patients’ needs and expectations and incorporate these into medical service design and environmental layout to improve patient satisfaction and improve medical quality [40,41,42]. Especially for children, by applying design thinking, children’s psychological and physical characteristics can be incorporated into medical service design and environmental layout, creating a more suitable medical service environment for children, and thereby improving children’s medical experience in hospitals [31,40].

Although some hospitals in China have used some methods of design thinking to improve certain aspects of outpatient medical service experience, such as patient visiting methods [43,44,45,46,47,48,49], there is still a lack of research on relevant guiding strategies in child outpatient clinics in Chinese hospitals. Therefore, under the guidance of design thinking, this study will take the pediatric outpatient clinic of Shanghai Xinhua Hospital as a case study, and based on the research on the elements that affect child outpatients’ medical service experiences, a strategic outpatient medical service experience model will be proposed to improve the experience and quality of children’s outpatient medical services in hospitals. Specifically, first, this study will use preliminary field research analysis to describe the field situation, medical treatment process, stakeholders, and service touch points of the case study. This will define the basic content scope for follow-up research. Then, this study will conduct an overall descriptive analysis based on the questionnaire survey, which will help to identify a broad range of five dimensions that influence child outpatients’ medical service experiences. Third, based on the five dimensions, this study will further analyze the satisfaction of children and their stakeholders with the current outpatient medical service experience. Fourth, regarding the status quo of satisfaction, this study will use principal component analysis to further analyze the five dimensions, to obtain specific elements that affect child outpatients’ medical service experiences. Fifth, this study will also use structured interviews to supplement specific elements. Sixth, based on the specific influencing elements obtained, this study will propose a child outpatients’ medical service strategy model under the guidance of design thinking. Finally, through satisfaction analysis and simulation verification, this study will verify the validity of the model. It should be noted that this strategy model is only a guiding strategy not a engineering design system. In addition, it does not involve detailed solutions for specific issues.

2. Research Background

2.1. Current Situation of Pediatric Outpatient Clinics in Shanghai Urban Hospitals

Shanghai is an economically developed region in China with an area of approximately 6340 square kilometers and a permanent population of over 24 million, including more than 2.4 million children aged 0–14 [50,51]. As of 2020, there are currently 86 hospitals in Shanghai, including children’s hospitals, general hospitals of different levels, and three national medical centers for children (Figure 1) [52,53,54]. At present, the problem of seeing a doctor in pediatrics has become a hot topic of discussion in all walks of life in China [55]. Children’s medical treatment is highly concentrated in specialized tertiary hospitals [56]. Overcrowding and long waiting times have become major problems in pediatric outpatient services [57]. This phenomenon is particularly prominent in the Shanghai area (Figure 2). For example, in November 2022, the Children’s Hospital Affiliated to Fudan University had an average daily outpatient volume of more than 8600 per day, with a peak of more than 9700 per day [58]. The number of outpatient and emergency visits in a single day in Shanghai Children’s Hospital also exceeded the daily 9000 person mark [59,60,61]. In addition, there are some issues in the service quality of pediatric outpatient clinics in Shanghai [62,63,64], for example, doctor–patient communication problems [65,66,67,68]. At the same time, the medical equipment in some hospitals is outdated and inconvenient to operate, which also brings certain difficulties to doctors’ diagnosis and treatment work [69,70,71].

To improve the severe situation faced by pediatric outpatient services in Shanghai hospitals, the Shanghai Municipal Government has successively issued a series of related policies [72]. In 2017, the Shanghai Municipal Government issued the “Shanghai Children’s Health Service Plan (2017–2020)” [73]. The plan proposes to improve the level of children’s healthcare by establishing a children’s health service system, optimizing the allocation of children’s medical and health resources, and strengthening children’s health management [73]. In 2018, the Shanghai Municipal Health Commission issued the “Guiding Opinions on Further Improving Pediatric Specialized Medical Services” [74]. This opinion proposes to optimize the allocation of medical resources, improve the quality and level of medical services, and provide more convenient and efficient medical services for children [72,75,76]. In addition, the Shanghai municipal government has also increased funding and support for pediatric medical services to promote the improvement and upgrading of pediatric medical services [9]. For example, in 2019, the Shanghai municipal government invested CNY 163 million in the establishment, renovation, and expansion of children’s medical institutions [77,78,79]. The relevant policies issued by the Shanghai Municipal Government in recent years have provided positive support and guarantees for improving the medical service level of the pediatric outpatient department of the hospital [12,80,81,82]. However, the promulgation of these policies only serves to improve the level of children’s outpatient medical services in theory, and it is difficult to really improve service quality based on people’s personal experience.

2.2. Current Situation of Pediatric Outpatient Clinic in Shanghafi Xinhua Hospital

Xinhua Hospital (Figure 3), affiliated with the Shanghai Jiao Tong University School of Medicine, was founded in 1958. It is one of the older tertiary general hospitals in Shanghai and the only general hospital in Shanghai that has both perinatal and complete pediatric subspecialties [83,84]. As a general hospital encompassing pediatrics, Xinhua Hospital has established a good reputation in Shanghai and even throughout China [70,85,86,87]. However, the current medical services of the Pediatric Outpatient Clinic of Xinhua Hospital is no longer sufficient to cope with the increase in the hospital’s business volume and cannot continue to provide high-quality medical services for children. At the same time, it cannot meet the needs of patients regarding high-quality medical experiences. First, the long queuing times for seeing a doctor is a key problem in the pediatric outpatient medical service of Xinhua Hospital. According to the data released by Xinhua Hospital (as of September 2021), the average daily outpatient volume of pediatric outpatient clinics is around 400, and the average monthly outpatient volume is about 10,000 [88,89]. Especially in some specific periods, the number of patients increased dramatically, such as during the summer vacation and winter vacation [63,81,90,91,92,93,94,95]. In addition, understaffing in the hospital is also an objective reason for the long consultation times [16,96,97,98,99]. Second, with the development of the hospital, the pediatric outpatient medical services of Xinhua Hospital are also facing other increasing challenges [88,100]. For example, due to the limited number of doctors in outpatient clinics, the workload of doctors is heavy, which easily leads to doctor fatigue and service quality decline. Additionally, some patients report a lack of adequate comfort and recreational facilities in the outpatient setting, which can lead to irritable moods in children. At the same time, patients have insufficient understanding of the outpatient service process, which can easily lead to unnecessary misunderstandings and communication barriers. These elements affect patients’ medical experience and medical quality and need to be effectively resolved.

To improve this situation, Xinhua Hospital has implemented several measures to improve the quality of medical services in its pediatric outpatient clinic [72,101], for example, increasing the number of doctors and nurses and optimizing appointment scheduling. The hospital has also launched a mobile app that allows patients to view wait times and to make appointments [102,103]. According to a survey conducted by Xinhua Hospital in March 2021, these measures were slightly effective in improving patient satisfaction [104,105]. However, despite these improvements, Xinhua Hospital still faces the challenge of providing high-quality pediatric outpatient care for pediatric patients. To meet the needs of patients, Xinhua Hospital needs to continue to implement new guiding strategies to improve the quality of outpatient medical services for children.

3. Methods

This part is mainly composed of the guiding concept, research framework, and node method.

3.1. Guiding Concept: Design Thinking Models

The guiding concept adopted in this study a design thinking process created by the Stanford d.school of Stanford University. The double diamond model in design thinking is the guiding logic of this study [106,107]. The model has two phases: finding the right problem and finding the right solution [39,42,43,108,109,110,111,112,113]. It has four steps:

• Discover stage: explore, gain insights, and gather user needs.
• Define stage: problem definition and refinement, providing a framework.
• Develop stage: solution creation and exploration.
• Deliver stage: service testing and evaluation.

3.2. Research Framework

Under the guidance of design thinking, the research framework of this study is divided into four stages:

• The first stage: The preliminary investigation of the pediatric outpatient clinic in the hospital. This stage includes preliminary field research and interviews and the preliminary construction of questionnaires. Specifically, at the beginning of the first phase (empathy), the authors conducted preliminary interviews with pediatric patients and their parents in the outpatient clinic of Shanghai Xinhua Hospital, on-site, by telephone, or on WeChat (social media software). The role of this stage is to obtain a preliminary understanding of the medical environment, medical process, stakeholders, and research contact points of the pediatric outpatient department of the hospital, which can pave the way for subsequent element analysis based on questionnaire surveys.
• The second stage: The collection of elements that affect child outpatients’ medical service experiences. This stage includes drawing up questionnaires, implementing questionnaire surveys, analyzing questionnaire data, and extracting main elements. Specifically, first, the content of the questionnaire was designed according to the results of field research. Second, implement questionnaire survey. Third, SPSS software (Mac 22.0) was used to conduct the descriptive analysis, satisfaction analysis, and principal component analysis of the questionnaire data. The purpose of the descriptive analysis is to identify the main dimensions that affect child outpatients’ medical service experiences, and to provide a preliminary scope for subsequent in-depth analysis. The purpose of the satisfaction analysis was to determine whether current child outpatients and their parents were satisfied with the outpatient medical service experience based on the main dimensions. The purpose of principal component analysis is to perform dimensionality reduction analysis on the main dimensions in order to further determine the specific influencing elements. Fourth, further supplementary analysis were conducted through structured interviews to supplement the influencing elements. Fifth, through the analysis of the results, the influencing elements of child outpatients’ medical service experiences and the improvement of the strategy of children’s outpatient medical services were summarized in order to ensure an accurate and complete understanding of the needs of child patients. It should be noted that a single questionnaire analysis or questionnaire survey method cannot guarantee an accurate and comprehensive summary of elements affecting child outpatients’ medical service experiences. Therefore, this study conducted a comprehensive analysis of the questionnaire from multiple perspectives. At the same time, structured interviews are used to supplement the analysis results of the questionnaire survey and strive to achieve a comprehensive summary and analysis of influencing elements.
• The third stage: Conceptual solution development. After completing the first and second phases (definition), a strategy model for improving the outpatient medical service experience for children needed to be generated based on the influencing elements. This stage was mainly performed using the brainstorming method. Brainstorming methods include brainwriting, storyboards, and mind maps. At this stage, the evaluation of ideas is discouraged and no constraints are imposed, which can lead to increased inspiration and creativity.
• The fourth stage: Solution feedback and evaluation. The strategy model was evaluated and the effectiveness of the strategy model was tested through questionnaire satisfaction survey analyses and simulated scenarios.

3.3. Nodal Method: Initial Field Research

In August 2022, this study observed key areas, such as the outpatient hall, doctor’s offices, nurse’s stations, and the pharmacy of the Pediatric Outpatient Clinic of Xinhua Hospital and conducted preliminary interviews with patients waiting here. A total of 10 child patients and their parents were interviewed in the Pediatric Outpatient Clinic of Shanghai Xinhua Hospital in the preliminary field interviews. The content of the interviews mainly involved three stages: before medical treatment, during medical treatment, and after medical treatment. It included appointment registration, waiting time, doctor communication, examination and treatment, drug purchase, etc. The process and results of the initial fieldwork will be discussed in the results section.

3.4. Node Method: Questionnaire Survey and Analysis

The questionnaire survey was classified according to child patients, their parents, and hospital staff, and two questionnaires were designed for separate surveys. The questionnaire for parents of patients is Questionnaire A, and the questionnaire for hospital nurses and staff is Questionnaire B. Questionnaire questions include objective questions and subjective questions. The distribution period was from 1 October 2022 to 31 October 2022. The distribution method took the form of a spontaneous questionnaire, and patients were randomly selected in the outpatient department of the hospital as the survey objects. At the same time, the questionnaire was also distributed through the online platform “Questionnaire Star”. After the survey, the data were collected, analyzed, and counted using the questionnaire background system. In addition, based on the descriptive analysis of questionnaires A and B, this study also set up the satisfaction scoring Questionnaire C. This was used to further confirm the satisfaction of pediatric patients and their parents with outpatient medical service experience. Questionnaire C was created and evaluated after the descriptive analysis of Questionnaires A and B. Questionnaire C was distributed between 1 January 2023 to 31 January 2023. The format is the same as Questionnaires A and B.

Questionnaire A consisted of 37 questions about the pediatric outpatient medical service, including open-ended and closed-ended questions. These questions were designed to gather relevant opinions about the pediatric outpatient clinic. For example, the waiting times before, during, and after seeing a doctor; the pediatric doctors, environment, and medical equipment; outpatient, emergency, and nursing services; doctor–patient communication efficiency and suggestions for the development of pediatric medical services. See Supplementary Materials A for example questions. Questionnaire B consists of 13 questions about the management of outpatient services in the hospital. These questions aim to collect developmental suggestions on the mutual communication efficiency of hospital staff, doctor–patient communication efficiency, workflow, hospital working environment, facilities and equipment, medical services, etc. See Supplementary Materials B for example questions. Questionnaire C mainly comprises a satisfaction scoring table (1–5 points) based on the five dimensions obtained from the analysis of Questionnaires A and B. This questionnaire is mainly used to investigate the satisfaction of children and their parents with outpatient medical service experience. See Appendix A for sample questions. A total of 1200 questionnaires A and B were distributed. For Questionnaire A, 800 valid questionnaires were recovered. For Questionnaire B, 200 valid questionnaires were recovered. A total of 900 copies of Questionnaire C were distributed, and 848 valid questionnaires were returned. The basic information of the questionnaire respondents is shown in

Table 1. Basic Information of Respondents to Questionnaire A (N = 800).

Sex	Age	Frequency (N)	Percentage (%)
Male	under 25	67	18.2%
	25–35	123	33.4%
	35–45	97	26.3%
	over 45	81	22.1%
		Subtotal: 368	46%
Female	under 25	117	27.2%
	25–35	179	41.4%
	35–45	72	16.6%
	over 45	64	14.8%
		Subtotal: 432	54%
Total		800	100.00%

,

Table 2. Basic Information of Respondents of Questionnaire B (N = 200).

Gender	Age	Frequency (N)	Percentage (%)
Man	under 25	7	6.4%
	25–35	18	16.5%
	35–45	63	57.7%
	over 45	21	19.2%
		Subtotal: 109	54.5%
Woman	under 25	14	15.3%
	25–35	37	40.7%
	35–45	22	24.2%
	over 45	18	19.8%
		Subtotal: 91	45.5%
Total		200	100.00%

and

Table 3. Basic Information of Respondents of Questionnaire C (N = 848).

Gender	Age	Frequency (N)	Percentage (%)
Man	under 25	38	5.66%
	25–35	150	17.70%
	35–45	169	41.84%
	over 45	113	13.32%
		Subtotal: 470	78.52%
Woman	under 25	42	11.11%
	25–35	137	36.24%
	35–45	170	44.97%
	over 45	29	7.67%
		Subtotal: 378	44.57%
Total		848	100.00%

.

After completing the questionnaire survey data collection, further quantitative analysis was needed [114,115,116]. Currently, there are popular evaluation scales in medical quantitative research, such as CAHPS and pp-15 [117]. In addition, patient satisfaction with medical services consists of constituent dimensions and determinants. The comparison of patients’ expectations and reality determines the satisfaction of each dimension. Based on this, this research chooses SPSS analysis software (principal component analysis and operation matrix) to quantitatively analyze the questionnaire data according to the determinant elements and composition dimensions. This can identify in detail the elements that affect the children’s experiences of outpatient care. Questionnaire data analyses were divided into descriptive analysis (determining elements), satisfaction analysis (based on elements used to analyze satisfaction), and principal component analysis (based on elements used to analyze the specific influencing elements). Additionally, the satisfaction analysis (Questionnaire C) is based on the descriptive analysis of Questionnaires A and B. This helps to achieve better satisfaction analysis results. The analysis process and results of questionnaires A, B, and C will be analyzed and discussed in detail in the results section.

3.5. Node Method: Structured Interviews

The structured interview deepens and supplements the questionnaire survey. Interview participants were recruited at the Shanghai Xinhua Hospital. A total of 20 professionals from the sample population voluntarily participated in this study. These included physicians in the pediatric outpatient clinic, specialist nurses in the pediatric outpatient clinic, guardians of outpatient children, hospital administrators, and child psychologists (

Table 4. Basic Information 1 of Participants in Structured Interviews.

Gender	Age	Number of People	Percentage (%)
Man	20–29	3	30.00%
	30–39	4	40.00%
	40–49	2	20.00%
	over 50	1	10.00%
		Subtotal: 10	50.00%
Woman	20–29	2	20.00%
	30–39	3	30.00%
	40–49	3	30.00%
	over 50	2	20.00%
		Subtotal: 10	50.00%
Total		20	100.00%

and

Table 5. Basic Information 2 of Participants in Structured Interviews.

Occupation	Number of People	Percentage (%)
Doctor	5	25.00%
Nurse	4	20.00%
Parents of Children	2	10.00%
Hospital Administrator	2	10.00%
Designer	5	25.00%
Child psychologist	2	10.00%
Total	20	100.00%

). The preset 10 open-ended questions are shown in Appendix B. Interview participants all signed written informed consent (their identities will remain anonymous). The interview period was from December 2022 to January 2023. The interview data collection method was audio recording. The interviews were conducted in a ZOOM chat room over two consecutive weekends, lasting about ten hours in total. The interviews were conducted in the specific form of a design thinking workshop. Participants were divided into four groups (five people each). Each group included a design thinking expert, two medical practitioners, a child psychologist, and a parent or guardian. The interview process and results will be specifically analyzed and discussed in the results section.

3.6. Node Methods: Strategy Model Evaluation

The strategy model evaluation was carried out using Questionnaire D (satisfaction survey analysis). It should be noted that Questionnaire D was implemented based on a simulation. Simulation refers to the process of allowing patients to imagine the experience of seeing a doctor based on the specific content of the policy model and then provide an evaluation. Therefore, the evaluation part belongs to hypothesis and simulation verification. The method is the same as that of Questionnaire C. A total of 370 copies of Questionnaire D were distributed, and 200 valid questionnaires were recovered. The basic information of the patients is shown in

Table 6. Basic Information of Respondents of Questionnaire D (N = 200).

Gender	Age	Frequency (N)	Percentage (%)
Man	under 25	21	26.92%
	25–35	35	44.87%
	35–45	13	16.66%
	over 45	9	11.53%
		Subtotal: 78	39%
Woman	under 25	39	31.96%
	25–35	47	38.52%
	35–45	22	18.03%
	over 45	14	11.47%
		Subtotal: 122	61%
Total		200	100.00%

. Questionnaire D was distributed between 1 March 2023 to 31 March 2023. Questionnaire D mainly comprises a satisfaction scoring table (1–5 points) based on the five dimensions obtained from the analysis of Questionnaires A and B. The question examples are the same as in Questionnaire C. The process and results will be discussed in detail in the discussion section.

4. Results

This section is divided into a preliminary field research analysis, questionnaire descriptive analysis, questionnaire satisfaction analysis, questionnaire principal component analysis, structured interview analysis, and influencing elements summary. Preliminary field research provided the content for the questionnaire design. The descriptive analysis of the questionnaire provides an analytical dimension for the satisfaction analysis. Satisfaction analysis identified children’s and their guardians’ satisfaction with the outpatient care experience according to the dimensions of the analysis. Depending on the degree of satisfaction, this study carried out a further principal component analysis on the questionnaire and confirmed the elements that affect children’s outpatient experiences in detail. The structured interview analysis is a further supplement to the questionnaire analysis elements. Finally, according to the comprehensive analysis results, the influencing elements for improving the medical treatment experiences of child outpatients are summarized.

4.1. Preliminary Field Research and Analysis

The preliminary field research was analyzed from four perspectives: outpatient survey, medical treatment process, stakeholders, and service contact points. The results of this analysis are mainly described qualitatively. The preliminary field research mainly utilized the following steps: observation, participation, and listening.

4.1.1. Preliminary Investigation and Analysis of the Pediatric Outpatient Clinic

In terms of field research in outpatient clinics, this study focused on observing key areas such as the outpatient hall, doctor’s offices, nurse stations, and pharmacy and conducted preliminary communication with patients waiting there (Figure 4). The results showed that child patients and their parents displayed dissatisfaction and demanded improvements of the outpatient services. Specifically, in the outpatient hall, patients need to register, pay fees, and receive medicine from different windows, which increases the time and cost for patients to see a doctor. In addition, patients need to wait in line, often for a long time. During the morning peak and afternoon peak especially, patients need to wait for a long time, which makes patients feel impatient and anxious. At the same time, the ambient noise in the outpatient hall is loud, which aggravates the nervousness of the patients. In the doctor’s office, the time available for patients to see a doctor is relatively short (less than 5 min on average), which may lead to a patient’s medical needs and concerns not being fully discovered and satisfied by doctors. Some patients reported that doctors would quickly ask for a diagnosis and prescribe medicines and would not inquire about the patient’s condition and needs in detail. On the other hand, some patients also expressed that the language communication is not smooth enough, which may lead to communication barriers between doctors and patients. At the nurses’ station, the number of nurses is insufficient to cope with the peak patient flow, which results in patients waiting for a long time to receive care services. In the pharmacy, patients need to go to different windows to go through the formalities of obtaining medicine, which increases the time and cost of patients’ medical treatment. In addition, some patients reported that the stock of medicines was insufficient, and they had to wait for a specific time period to receive the medicines they needed.

4.1.2. Analysis of Medical Treatment Process for Child Patients

The patient visit process includes inquiry, appointment, queuing, registration, going to the department, waiting at the triage office, triage, and consultation. As shown in Figure 5, a child’s medical journey consists of three main phases: before, during, and after. The different behaviors of the three phases have distinct touchpoints, and pediatric patient’s mood changes depending on how effectively they experience each touchpoint. Research has shown that mood changes are more prominent during the “treatment” phase than during the “before” and “after” phases, with a defined mood low. Thus, on the timeline, the “treatment” phase is the most challenging part of the service engagement.

The research results of the medical treatment process show that the biggest problem before seeking medical treatment is the difficulty of registration, as patients need to make an appointment several days in advance to register with an expert professional. Patients and parents are also dissatisfied with the registration channels and information transparency provided by the hospital. In addition, in seeking medical treatment, most patients and parents are relatively satisfied with doctors’ communication and attitudes. However, there are also a small number of patients who reflect that doctors are busy and lack patience and meticulous diagnosis and good treatment attitude. In addition, some parents are not satisfied with the examination and treatment environment of pediatric outpatient clinics and believe that the cleanliness and comfort of the environment could be further improved. After seeking medical treatment, patients are dissatisfied with the process of obtaining medicine and follow-up visits and believe that the hospital needs to provide more convenient and efficient services.

4.1.3. Stakeholder Analysis

In a holistic children’s healthcare system, stakeholders are divided into two parties: service recipients (children and their parents) and service providers (hospitals). The corresponding responsibilities are shown in Figure 6. They all go through the same service process and play similar roles in a wider context. However, they also have some differences. Due to the special nature of children and their still undeveloped cognitive abilities, steps such as registration, payment, and finding departments are all completed by parents. In the chain of services, the accompanying parent is the direct contact point for the various services. Therefore, in this study, the child patients and their guardians will be regarded as a single unit in the relevant discussion, that is to say, they are all the subjects of this study—patients. Provider stakeholders are primarily hospital staff, such as doctors, nurses, and service personnel. Physicians, as the main service providers, provide consultation services to patients and provide treatment plans according to their conditions. Service personnel or instructors mainly provide business guidance to patients and help patients to see a doctor smoothly either online or offline, according to different issues.

4.1.4. Service Touchpoint Analysis

Design thinking considers three broad categories of touchpoints in services: physical, digital, and human touchpoints [118]. They all play an important role in the service process and are an important indicator of service experience [119]. As shown in Figure 5, in this study, service contact points can be summarized as: registration, information transparency, doctor communication and attitude, inspection and treatment environment, medicine collection and follow-up procedures, etc.

4.2. Descriptive Analysis of Questionnaire Survey

Based on preliminary field research and analysis, this study designed different questionnaires for children, accompanying parents, and medical service personnel. Through the data collation of the questionnaire, the following overall descriptive analysis results on child outpatients’ medical experiences were obtained.

4.2.1. Children and Their Parents (Questionnaire A)

As shown in

Table 7. Descriptive Analysis Results of Questionnaire A.

Questions	Options	Frequency	Percentage (%)	Cumulative Percentage (%)
Have you received pediatric outpatient care within the past 12 months?	Yes	602	75.16	75.16
	No	199	24.84	100
How many children are there in the family?	1	583	72.78	72.78
	2	144	17.98	90.76
	2 or more	74	9.24	100
Transportation to the hospital	Walk	42	5.24	5.24
	Bike	46	5.74	10.99
	Motorcycle/battery car	85	10.61	21.6
	Bus/Subway/Light Rail	140	17.48	39.08
	Taxi/online car-hailing	105	13.11	52.18
	Private car	383	47.82	100
Is the waiting area comfortable?	Very comfortable	194	24.22	24.22
	More comfortable	286	35.71	59.93
	General	93	11.61	71.54
	Not very comfortable	128	15.98	87.52
	Very uncomfortable	100	12.48	100
The importance of the outpatient facility area	Unimportant	131	16.35	16.35
	Somewhat important	262	32.71	49.06
	Very important	408	50.94	100
How long did you wait before receiving treatment?	Less than 10 min	139	17.35	17.35
	10–30 min	329	41.07	58.43
	30–60 min	243	30.34	88.76
	1 h or more	90	11.24	100
Are you receiving timely information about your child’s visit process?	Yes	528	65.92	65.92
	No	273	34.08	100
Do you support the hospital’s introduction of a digital management system to improve the efficiency of outpatient management?	Support	527	65.79	65.79
	Do not support	89	11.11	76.9
	Uncertain	185	23.1	100
Total		801	100	100

, most of the parents or guardians had taken their children to receive pediatric outpatient treatment in the past 12 months, accounting for 75.16%. The majority had only one child at home, namely 72.78%. From the perspective of transportation for medical purposes, private cars were the main transportation mode, accounting for 47.82%; other modes of transportation were used less frequently and their percentage values were divided fairly similarly. For example, 17.48% of patients had arrived by public transport and had arrived 13.11% in taxis. In terms of the comfort of the waiting area, around 50% of the people thought the area was very comfortable or relatively comfortable; regarding the importance of outpatient facilities, 50.94% and 32.71% of the people surveyed considered them very important and somewhat important, respectively. In regard to waiting time, the longest waiting time was 10–30 min, accounting for 41.07%, followed by 30–60 min, accounting for 30.34%. Regarding access to medical information, 65.92% of parents believed that information could be obtained in a timely manner. In terms of improving the efficiency of outpatient management based on the introduction of hospital digital management systems, 65.79% of people would welcome this change.

4.2.2. Hospital (Questionnaire B)

As shown in

Table 8. Descriptive Analysis Results of Questionnaire B.

Questions	Options	Frequency	Percentage (%)	Cumulative Percentage (%)
What is your position at the hospital?	Doctor	28	13.93	13.93
	Nurse	36	17.91	31.84
	Radiologic Technologist	18	8.96	40.8
	Pharmacist	28	13.93	54.73
	Physiotherapist	20	9.95	64.68
	Social worker	53	26.37	91.04
	Administration staff	18	8.96	100
In your work, have you ever been complained about for medical errors or other reasons?	Wrong or inaccurate diagnosis	15	7.46	7.46
	The treatment plan is not suitable	13	6.47	13.93
	Poor experience, improper use of equipment	25	12.44	26.37
	Poor communication with patients	87	43.28	69.65
	Fees are opaque or exorbitant	23	11.44	81.09
	Long waiting time and disorganized management	15	7.46	88.56
	The treatment effect is not good or the cycle is too long	15	7.46	96.02
	Other medical accidents	8	3.98	100
Do you think the current workflow needs to be improved?	Needs improvement	108	53.73	53.73
	Needs a little improvement	79	39.3	93.03
	No need to improve	14	6.97	100
Do you think it is necessary to establish a doctor-patient communication platform?	Necessary	145	72.14	72.14
	Unnecessary	19	9.45	81.59
	Do not know	37	18.41	100
Have you ever encountered a situation where you did not understand, distrusted, or refused to accept the treatment advice?	Yes	110	54.73	54.73
	No	36	17.91	72.64
	Do not know	55	27.36	100
Total		201	100	100

, regarding the number of hospital service personnel, there is little difference in the distributions of the number of people in different positions. For instance, the number of social workers is relatively large, accounting for 26.37%, followed by nurses, accounting for 17.91%. This reflects that care and care services are the most important in medical services. In terms of complaints due to medical errors or other reasons, poor communication with patients is the main problem, and the percentage of complaints was 43.28%. In regard to the necessity of establishing a doctor–patient communication platform, hospital staff generally believe that it is necessary, at 72.14%. Therefore, the necessity for a hospital communication platform is very urgent, as children and parents have encountered situations where they do not understand, distrust, or refuse to accept doctor’s treatment recommendations; most patients have encountered such situations, accounting for 54.73%.

4.2.3. Five Dimensions That Affect Children’s Medical Service Experiences

Based on the preliminary research analysis and descriptive analysis of the questionnaire, this study concluded that there are five main dimensions affecting the experience of children’s outpatient medical services at Xinhua Hospital, namely the waiting dimension, the waiting facilities dimension, the outpatient booking dimension, the outpatient management system dimension, and the outpatient visitor flow dimension. Therefore, the further analysis of the five dimensions is required to confirm the specific satisfaction of current patients. If the satisfaction is low, a further dimensionality reduction analysis of the five dimensions is required to determine the specific unsatisfactory elements. The understanding of the detailed elements can better provide a reference for the strategy model.

4.3. Descriptive Analysis of Questionnaire Survey

After the preliminary survey analysis and the questionnaire descriptive analysis, this study aimed to confirm the satisfaction degree regarding the outpatient medical services experiences of children and parents. The weight of each dimension and the overall satisfaction score in the satisfaction analysis will provide a reference for the subsequent analyses of the main elements. Questionnaire satisfaction analysis includes the weight analysis of each dimension as well as satisfaction calculation and analysis based on dimension weight and questionnaire scoring. It should be noted that since the satisfaction analysis results need to be calculated in combination with satisfaction scores [120], before calculation, Questionnaire C (satisfaction scoring questionnaire) was designed according to the weight analysis results of each dimension in order to collect scoring data. Please refer to Appendix A for the contents of Questionnaire C.

4.3.1. The Weight of Each Dimension

By analyzing the dimension weights, we can obtain the proportions of different dimensions in the overall satisfaction, which can help this research to understand the impact of each dimension on the overall satisfaction. Specifically, the higher the weight of a dimension, the greater the impact of this dimension on overall satisfaction, which indicates that people pay more attention to the performance of this dimension in the process of their medical service experiences. On the other hand, the lower the weight of a dimension, the smaller the impact of this dimension on overall satisfaction, and improving this dimension may not have a significant impact on overall satisfaction. Taking Xinhua Hospital as an example, dimensional weight analysis can help hospital managers understand to what extent patients pay attention to different aspects of the hospital in order to improve service quality in a targeted manner. As shown in

Table 9. Based on the weight analysis results of each dimension of Questionnaires A and B.

Name	Factor 1	Factor 2	Factor 3	Factor 4	Factor 5	Comprehensive Score Coefficient	Weight Factor	Dimension	Weight
Eigenvalue (after Rotation)	4.374	4.348	4.276	4.064	3.91
Percentage of Variance Explained	10.93%	10.87%	10.69%	10.16%	9.77%
Manual guidance	0.0434	0.1197	0.0491	0.0696	0.0344	0.1017	2.17%	Waiting dimension	16.01%
Waiting time	0.0504	0.0703	0.1203	0.04	0.1033	0.1085	2.31%
Clear instructions for visiting doctors	0.0257	0.0897	0.1135	0.0592	0.096	0.1164	2.48%
Timely information communication	0.0718	0.1676	0.0957	0.0636	0.0404	0.1134	2.41%
Privacy protection	0.0643	0.1586	0.0803	0.0546	0.0172	0.1099	2.34%
Diagnosis and treatment speed	0.1213	0.0588	0.0444	0.0396	0.0747	0.1008	2.15%
Various medical expenses	0.0843	0.0579	0.0455	0.0495	0.1001	0.101	2.15%
Number of seats	0.0654	0.0561	0.0253	0.0943	0.0883	0.1013	2.16%	Waiting Facilities	24.49%
Seat comfort	0.1186	0.0562	0.025	0.0613	0.055	0.1021	2.18%
Entertainment facilities	0.0695	0.0609	0.033	0.1251	0.0503	0.1071	2.28%
Sanitation status	0.0786	0.3041	0.0795	0.0686	0.0804	0.1254	2.67%
Ventilation and lighting	0.0429	0.3982	0.0467	0.0805	0.0615	0.1153	2.46%
Noise	0.0305	0.4006	0.0593	0.0719	0.0502	0.1177	2.51%
Barrier-free facilities	0.0344	0.4068	0.0806	0.0667	0.0644	0.1183	2.52%
Whether the system and method of message notification are intelligent	0.0446	0.3939	0.0692	0.0497	0.0814	0.1199	2.55%
Consultation service facilities	0.04	0.1829	0.1008	0.0635	0.0508	0.1207	2.57%
Guidance system	0.0729	0.1156	0.0936	0.0467	0.0631	0.1215	2.59%
Number of registrations available	0.0493	0.1293	0.1178	0.0645	0.0479	0.1177	2.51%	Outpatient appointment	20.18%
Lack of appointment information or guidelines	0.0424	0.1347	0.1176	0.0424	0.081	0.1172	2.50%
Convenience of Appointment	0.0575	0.0773	0.1155	0.08	0.143	0.1221	2.60%
Lack of appointment information or guidance	0.1026	0.0268	0.0638	0.3105	0.13	0.1209	2.57%
Appointment system or equipment failure	0.0597	0.0714	0.0746	0.4236	0.0984	0.1182	2.52%
Attitude of doctors or hospital staff towards appointments	0.0716	0.0845	0.0536	0.4273	0.0978	0.1184	2.52%
Appointment sequence or results	0.1353	0.0663	0.0751	0.3596	0.117	0.1147	2.44%
Trouble caused by the epidemic	0.0813	0.0723	0.0646	0.4298	0.0993	0.1185	2.52%
Children’s exclusive system	0.1074	0.0786	0.0926	0.1287	0.3659	0.1329	2.83%	Outpatient management system	21.67%
Doctor’s clinic hours settings	0.112	0.0713	0.0921	0.1324	0.3909	0.1328	2.83%
Appointment channel settings	0.0989	0.0702	0.0993	0.1023	0.3987	0.1292	2.75%
Real-time message alert system settings	0.1158	0.0851	0.112	0.1377	0.4021	0.1335	2.84%
Multilingual system settings	0.1118	0.0608	0.1262	0.1241	0.3656	0.1324	2.82%
Children’s medical safety system settings	0.0908	0.0738	0.3996	0.0798	0.0954	0.1214	2.58%
Field service system settings	0.0623	0.0748	0.3746	0.0525	0.0487	0.1132	2.41%
Hospital staff service training system	0.113	0.0765	0.3663	0.0469	0.1362	0.1225	2.61%
Access efficiency	0.088	0.0518	0.4022	0.077	0.0854	0.1194	2.54%	Outpatient Visiting Process	17.64%
Multiple forms and documents are filled out by patients in different processes	0.0777	0.0605	0.396	0.0758	0.1001	0.1247	2.65%
Medical treatment process guidance and guidance	0.4032	0.0322	0.075	0.0881	0.0919	0.1174	2.50%
Communication between doctors and nurses	0.3881	0.0284	0.0594	0.0742	0.0939	0.1128	2.40%
Lack of efficient interoperability in each link of the process	0.384	0.0593	0.0957	0.0852	0.0954	0.1184	2.52%
The process setting is not convenient enough	0.3951	0.0513	0.0907	0.0871	0.0846	0.1183	2.52%
Process setup is highly repeatable	0.3933	0.043	0.0892	0.08	0.0844	0.118	2.51%

, based on the questionnaire survey, the weight analysis results of the dimensions that affect the service satisfaction at Xinhua Hospital show that the weight factor of the waiting facilities dimension is the highest, accounting for 24.49%, indicating that the comfort and convenience of the waiting facilities affect the comprehensive satisfaction of patients the most. In addition, the weights of outpatient management system and outpatient appointment dimension are relatively large, namely 21.67% and 20.18%, respectively. The visiting process dimension and waiting during outpatient visits dimension are relatively small, accounting for 17.64% and 16.01%, respectively. Overall, the weights of the dimensions are relatively average, with little difference between them. This means that all dimensions have a relatively balanced impact on the comprehensive satisfaction of patients, and no dimension is too prominent or neglected. They are all equally important to a child’s outpatient medical service experience.

4.3.2. Satisfaction Calculation and Analysis Based on the Weight of Each Dimension and Questionnaire Score

To obtain accurate results of patient satisfaction with the medical service experience, this study based on the weight analysis results of each dimension and the results of Questionnaire C and used the operation matrix to carry out detailed calculations. As shown in

Table 10. Scoring Results of Questionnaire C.

Dimension	Weights	Score
Waiting	16.01%	1.5
Waiting facilities	24.49%	2.0
Outpatient appointments	20.18%	2.1
Outpatient management system	21.67%	1.7
Outpatient visiting process	17.64%	2.0

In this table, the weight of each dimension and the number of people are fixed, while each person’s score for each dimension is random.

, the respondents gave satisfaction ratings for the current situation of each dimension on a scale of 1 to 5, according to their own situation. According to the score distribution, the average score and standard deviation of each dimension can be calculated, and then the standardized score of each dimension can be calculated. Finally, the comprehensive satisfaction of the hospital can be calculated using the calculation formula of the comprehensive satisfaction index.

The calculation is as follows:

$$Comprehensive satisfaction index=\sum \left(Dimension weight\times Dimension score\right)$$

the detailed calculation formula is expressed as follows:

$$\mathit{C}\mathit{S}\mathit{I}={{\displaystyle \sum }}_{i=1}^n{\mathit{W}}_i\times {\mathit{S}}_i$$

(1)

where CSI represents the comprehensive satisfaction index, $W_i$ represents the weight of the i-th dimension, $S_i$ represents the satisfaction index of the i-th dimension, and n represents a total of n dimensions. According to the data in Table 9, substituting the above formula can obtain:

$$\mathit{C}\mathit{S}\mathit{I}=\mathbf{0.1601}\times {\mathit{S}}_{\mathbf{1}}+\mathbf{0.2449}\times {\mathit{S}}_{\mathbf{2}}+\mathbf{0.2018}\times {\mathit{S}}_{\mathbf{3}}+\mathbf{0.2167}\times {\mathit{S}}_{\mathbf{4}}+\mathbf{0.1764}\times {\mathit{S}}_{\mathbf{5}}$$

(2)

where S₁ represents the satisfaction index of the waiting dimension, S₂ represents the satisfaction index of the waiting facilities dimension, S₃ represents the satisfaction index of the outpatient dimension, S₄ represents the satisfaction index of the outpatient management system, and S₅ represents the satisfaction index of the outpatient visiting process dimension index. Waiting time is scored at 1.5 points, facilities are scored at 2.0 points, outpatient queuing appointment is scored at 2.1 points, outpatient management system is scored at 1.7 points, and outpatient visiting process is scored at 2.0 points. Substituting these values into the formula gives:

$$\mathit{CSI}=\mathbf{0.1601}\times \mathbf{1.5}+\mathbf{0.2449}\times \mathbf{2.0}+\mathbf{0.2018}\times \mathbf{2.1}+\mathbf{0.2167}\times \mathbf{1.7}+\mathbf{0.1764}\times \mathbf{2.0}\approx \mathbf{1.87592}$$

(3)

The overall satisfaction score is around 1.87592, which is converted into a percentage, which is:

$$\mathbf{1.87592} ÷ \mathbf{5}\times \mathbf{100}\% \approx \mathbf{37.51}\%$$

(4)

Since the CSI ranges from 1 to 5, the percentages should range from 20% to 100%. This shows that the higher the satisfaction score, the higher the patient’s satisfaction with the evaluated service. According to the calculation results, the satisfaction rate of outpatients in the Pediatric Outpatient Department of Xinhua Hospital is around 37.51%. The results show that in the Pediatric Outpatient Clinic, a considerable number of patients are dissatisfied with the hospital’s services. Based on this phenomenon of dissatisfaction, this study will further confirm the detailed influencing elements through the principal component analysis of the questionnaire.

4.4. Principal Component Analysis of Questionnaire Survey Data

Based on SPSS software, a further dimensionality reduction analysis can be performed on the data of Questionnaires A and B to obtain the specific main elements affecting child outpatients’ medical service experiences (analysis result elements are given in the form of factor naming). For the main steps of principal component analysis, the related indicators, and the meanings of the statistics, please refer to reference [120]. Based on the dataset of five dimensions, the specific process of the principal component analysis of the questionnaire in this study is as follows:

• Standardize the data: use each element to subtract the average value of each element’s score. Then, divide by the standard deviation to ensure that each element is statistically comparable.
• Calculate the correlation coefficient matrix: conduct a correlation analysis between each element and the other elements to obtain a 5 × 5 correlation coefficient matrix. On the diagonal of this matrix is each element’s own variance.
• Perform eigenvalue decomposition: decompose the correlation coefficient matrix into eigenvectors and eigenvalues. The eigenvectors describe the principal directions of the dataset. The eigenvalues represent the variance in each direction. Since the eigenvectors are unit vectors, their sum of squares is 1.
• Select the number of factors: determine the number of factors to retain based on the eigenvalues. In general, studies select factors with eigenvalues greater than 1 because they explain the dataset better.
• Calculation of factor loading coefficient and commonality: the factor loading coefficient represents the proportion of each variable in each factor. The degree of commonality refers to the proportion of variance that each variable can be explained via common factors. By analyzing these, one can determine what each factor represents and which variables are most correlated with which factors.
• Named factors: according to the loading coefficient and commonality of each factor, the significance represented by each factor can be determined and named. Naming can be numbered with letters or numbers. For example, the variables with a high-loading coefficient of factor A1 include “waiting time”, “comfort of medical treatment place”, “friendliness of doctor’s attitude”, and “completeness of medical facilities”. Therefore, this factor could be named “waiting environment and information flow”. It represents a common theme described by these variables. By naming the factors, this study can better understand the potential factors in the dataset, making it easier to explain and apply them. Through the analysis of this factor, the improvement in the patient’s medical experience and satisfaction can be achieved.

4.4.1. Questionnaire A Principal Component Analysis

The principal component analysis of Questionnaire A is carried out according to the five main dimensions obtained from the descriptive analysis and satisfaction analysis of the questionnaire.

• Waiting dimension.

As shown in

Table 11. Waiting Dimension Principal Component Analysis.

Name	Factor Loading Coefficient		Communality
	A1	A2
Long wait	0.719	−0.23	0.569
Lack of clear medical instructions, resulting in wasted time	0.64	0.238	0.466
Lack of manual guidance, resulting in wasted time	0.717	0.219	0.562
Lack of timely information delivery	0.661	0.118	0.451
Lack of privacy	0.779	−0.035	0.608
Diagnosis and treatment of children’s diseases	0.138	0.817	0.686
Various medical expenses	−0.001	0.857	0.735
Eigenvalue (before rotation)	2.597	1.482	-
Percentage of variance explained (before rotation)	37.094%	21.175%	-
Cumulative percentage of variance explained (before rotation)	37.094%	58.269%	-
Eigenvalue (after rotation)	2.505	1.574	-
Percentage of variance explained (after rotation)	35.779%	22.490%	-
Cumulative percentage of variance explained (after rotation)	35.779%	58.269%	-
Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy	0.693		-
Bartlett’s test of sphericity	1216.331		-
Degrees of freedom (DF)	21		-
p-value	0		-

Note: “after rotation” indicates that the factor has been rotated.

, in this dimension, the elements that affect the experience of seeing a doctor can be classified into two factors. In factor A1, elements such as a long waiting time, a lack of clear instructions for seeing a doctor, a lack of timely information communication, and a lack of privacy protection have a large load and have a significant impact on the experience of seeing a doctor. Factor A1 can be named “waiting time and information flow”. In factor A2, elements such as the speed of diagnosis and treatment of children’s diseases, as well as various medical expenses, have a significant impact on the experience of seeing a doctor and their load is relatively large. Factor A2 can be named “problem of speed and cost of diagnosis and treatment”. Therefore, the main influencing elements in the waiting dimension are waiting time and information flow (A1) and diagnosis and treatment speed and cost (A2).

2.

Waiting Facilities Dimension

As shown in

Table 12. Waiting Facilities Dimension Principal Component Analysis.

Name	Factor Loading Coefficient			Communality
	A3	A4	A5
Not enough seats in the waiting area	0.964	0.122	0.843	0.729
Seats in the waiting area are not comfortable enough	0.955	0.049	0.844	0.739
Lack of recreational facilities in the waiting area	0.161	0.849	0.26	0.566
Poor hygiene in waiting area	0.115	0.895	0.107	0.886
Ventilation and lighting need to be improved	0.108	0.826	0.064	0.881
Excessive noise in the waiting area	0.135	0.818	0.041	0.924
The environment is not suitable for children to wait for a long time	0.125	0.203	0.758	0.901
Barrier-free facilities are not in place	0.301	0.785	0.732	0.708
Notification system is not advanced enough	0.165	0.884	0.721	0.824
Insufficient consulting services	0.183	0.864	0.766	0.784
There is no effective medical guidance system	0.203	0.758	0.779	0.622
Eigenvalue (before rotation)	5.43	1.781	1.355	-
Percentage of variance explained (before rotation)	49.367%	16.187%	12.321%	-
Cumulative percentage of variance explained (before rotation)	49.367%	65.554%	77.875%	-
Eigenvalue (after rotation)	4.051	2.977	1.538	-
Percentage of variance explained (after rotation)	36.828%	27.068%	13.979%	-
Cumulative percentage of variance explained (after rotation)	36.828%	63.896%	77.875%	-
Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy	0.857			-
Bartlett’s test of sphericity	7175.166			-
Degrees of freedom (DF)	55			-
p-value	0.000			-

Note: “after rotation” indicates that the factor has been rotated.

, in factor A3, elements such as an insufficient number of seats in the waiting area and poor seat comfort have large loads; in factor A4, the waiting area lacks entertainment facilities as well as poor sanitation, ventilation, and lighting. The need for the improvement of these, as well as excessive noise and other elements, has a large load. In factor A5, the elements with a large load include the following: the environment is not suitable for children to wait for a long time, the barrier-free facilities for special needs groups are not in place, the systems and methods of message notification are not advanced enough, the consultation service is not in place, and there is no effective guidance system. Factors A3, A4, and A5 can be named comfort, comprehensiveness of facilities, and convenience, respectively. Therefore, the main influencing elements in the dimension of waiting facilities are comfort (A3), comprehensiveness of facilities (A4) and convenience (A5).

3.

Outpatient Appointment Dimension.

As shown in

Table 13. Principal Component Analysis of the Outpatient Appointment Dimension.

Name	Factor Loading Coefficient			Communality
	A6	A7	A8
Bookable quantity	0.911	0.184	0.146	0.919
The appointment process is not clear	0.827	0.207	0.399	0.886
Convenience of appointment	0.867	0.187	0.369	0.922
Lack of appointment information or guidelines	0.839	0.284	−0.025	0.785
Appointment system or device malfunction	0.873	0.192	0.351	0.921
Doctors or hospital staff have a poor attitude towards making appointments	0.194	0.881	0.185	0.848
Unfair appointment sequence or results	0.246	0.916	0.059	0.902
Trouble caused by the epidemic	0.184	0.191	0.804	0.839
Eigenvalue (before rotation)	4.911	1.605	1.505	-
Percentage of variance explained (before rotation)	61.393%	20.067%	6.309%	-
Cumulative percentage of variance explained (before rotation)	61.393%	81.460%	87.769%	-
Eigenvalue (after rotation)	3.2	2.638	1.183	-
Percentage of variance explained (after rotation)	40.006%	32.975%	14.788%	-
Cumulative percentage of variance explained (after rotation)	40.006%	72.980%	87.769%	-
Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy	0.852			-
Bartlett’s test of sphericity	6349.277			-
Degrees of freedom (DF)	28			-
p-value	0.000			-

Note: “after rotation” indicates that the factor has been rotated.

, in factor A6, the elements with a large load are the following: the number of available reservations, unclear queuing process, poor reservation convenience, a lack of reservation information or guidance, no reservation system, or equipment failure. Factor A6 can be named “reservation experience”. In factor A7, the elements with a large load are doctors or hospital staff having a bad attitude towards making appointments and the order of appointments or results being unfair. Factor A7 can be named “Medical staff service reservation attitude and fairness”. In factor A8, the elements with a large load are the problems caused by the epidemic prevention and control measures. Factor A8 can be named “issues related to epidemic prevention and control”. Therefore, the main influencing elements of outpatient queuing and appointment dimensions are appointment experience (A6), medical staff appointment service attitude and fairness (A7), and issues related to epidemic prevention and control (A8).

4.

Outpatient Management System Dimension

As shown in

Table 14. Principal Component Analysis of the Outpatient Management System Dimension.

Name	Factor Loading Coefficient		Communality
	A9	A10
Children’s exclusive system settings	0.939	0.097	0.892
Pediatrician Clinic Time Setting	0.81	0.034	0.657
Appointment Route and System Settings	0.894	0.125	0.816
Timely information prompt system settings	0.892	0.109	0.807
Multilingual system settings	0.937	0.103	0.888
Medical Safety System Settings	0.066	0.957	0.921
On-site service system settings	0.027	0.899	0.809
Setting up a training system for hospital staff	0.106	0.898	0.817
Eigenvalue (before rotation)	4.247	2.359	-
Percentage of variance explained (before rotation)	53.092%	29.486%	-
Cumulative percentage of variance explained (before rotation)	53.092%	82.578%	-
Eigenvalue (after rotation)	4.026	2.58	-
Percentage of variance explained (after rotation)	50.331%	32.247%	-
Cumulative percentage of variance explained (after rotation)	50.331%	82.578%	-
Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy	0.816		-
Bartlett’s test of sphericity	5880.694		-
Degrees of freedom (DF)	28		-
p-value	0.000		-

Note: “after rotation” indicates that the factor has been rotated.

, in factor A9, the elements with larger loads are as follows: child-specific system settings, pediatrician outpatient time settings, appointment channels and system settings, implementation information reminder system settings, and multilingual system settings. Factor A9 can be named “easy access to medical care”. In factor A10, the elements with larger loads are field service system settings, medical security system settings, and hospital staff training system settings. Factor A10 can be named “quality of service”. Therefore, the main impact elements of the outpatient management system dimension are as follows: convenient medical treatment (A9) and service quality (A10).

5.

Outpatient Visiting Process Dimension

As shown in

Table 15. Principal Component Analysis of the Outpatient Visiting Process Dimension.

Name	Factor Loading Coefficient			Communality
	A11	A12	A13
Access Process Efficiency	0.937	0.129	0.115	0.999
Patients need to fill in multiple forms, wasting time and energy	0.937	0.178	0.115	0.924
Patients are not clear about the process of seeing doctors, lacking guidance and guidance	0.848	0.2	0.09	0.767
Physician–nurse communication is unclear, leading patients to misinterpret doctor’s advice	0.929	0.129	0.16	0.906
Lack of efficient communication between access processes, requiring patients to repeatedly provide the same information and materials	0.145	0.955	0.141	0.938
The access process is not convenient enough, and patients need to spend a lot of time and effort making appointments for treatment	0.161	0.816	0.136	0.772
The hospital lacks flexible ways of seeing a doctor, such as remote consultations, evening outpatient services, and weekend outpatient services	0.222	0.965	0.833	0.998
Eigenvalue (before rotation)	4.641	1.957	1.706	-
Percentage of variance explained (before rotation)	66.306%	13.670%	10.083%	-
Cumulative percentage of variance explained (before rotation)	66.306%	79.976%	90.059%	-
Eigenvalue (after rotation)	4.177	1.073	1.054	-
Percentage of variance explained (after rotation)	59.669%	15.334%	15.056%	-
Cumulative percentage of variance explained (after rotation)	59.669%	75.003%	90.059%	-
Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy	0.912			-
Bartlett’s test of sphericity	5293.311			-
Degrees of freedom (DF)	21			-
p-value	0			-

Note: “after rotation” indicates that the factor has been rotated.

, in factor A11, the elements with larger loads are as follows: interview process efficiency, the repeated filling out of forms and materials, a lack of interview process guidance, and insufficient communication between doctors and patients. In factor A12, the elements with heavy loads are a lack of efficient intercommunication between access processes and inconvenient appointments. In factor A13, the factor with a large load is that the hospital lacks flexible ways of seeing a doctor. Factors A11, A12, and A13 can be named “Visit Process Efficiency and Experience”, “Visit Process Information Sharing and Convenience of Appointment”, and “Flexible Diagnosis Mode”. Therefore, the main impact elements of the visiting process dimension can be summarized as: visiting process efficiency and experience (A11), visiting process information sharing and appointment convenience (A12), and flexible visiting methods (A13).

4.4.2. Questionnaire B Principal Component Analysis

The principal component analysis of Questionnaire B is also divided into five aspects: waiting facilities dimension, waiting dimension, outpatient queuing appointment dimension, outpatient management system dimension, and outpatient visiting process dimension.

• Waiting Facilities Dimension

As shown in

Table 16. Principal Component Analysis of Waiting Facilities Dimension (Questionnaire B).

Name	Factor Loading Coefficient			Communality
	B1	B2	B3
Number of seats in the waiting area	0.775	0.225	0.095	0.721
Seat comfort in the waiting area	0.718	0.252	0.193	0.777
Entertainment facilities in the waiting area	0.204	0.238	0.605	0.755
Sanitation in the waiting area	0.201	0.305	0.627	0.691
Ventilation and lighting	0.289	0.193	0.750	0.851
Noise in the waiting area	0.881	0.562	0.25	0.864
Barrier-free facilities for special needs populations	0.896	0.622	0.253	0.882
The system and method of message notification is intelligent	0.874	0.754	0.258	0.854
Medical consultation services	0.310	0.155	0.836	0.891
Visiting guidance system	0.285	0.210	0.884	0.907
Eigenvalue (before rotation)	5.658	1.637	1.098	-
Percentage of variance explained (before rotation)	56.577%	16.370%	8.979%	-
Cumulative percentage of variance explained (before rotation)	56.577%	72.947%	81.925%	-
Eigenvalue (after rotation)	3.960	2.373	1.860	-
Percentage of variance explained (after rotation)	39.602%	23.727%	18.597%	-
Cumulative percentage of variance explained (after rotation)	39.602%	63.329%	81.925%	-
Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy	0.887			-
Bartlett’s test of sphericity	2362.076			-
Degrees of freedom (DF)	45			-
p-value	0			-

Note: “after rotation” indicates that the factor has been rotated.

, in factor B1, the elements with larger loads are: the number of seats in the waiting area and the comfort of the seats in the waiting area. B1 can be named “comfort of waiting environment”. In factor B2, the elements with a large load are the noise in the waiting area, the layout of barrier-free facilities for special needs groups, and the system and method of message notification are intelligent. B2 can be named “Intelligent Level of Facilities and Information in the Waiting Area”. In factor B3, the elements with a large load are the entertainment facilities in the waiting area, sanitary conditions in the waiting area, ventilation and lighting, consultation services for medical consultation, and a guidance system for medical consultation. B3 can be named “waiting area facilities and service quality”. Therefore, the main influencing elements of the dimension of waiting facilities are the comfort of the waiting environment (B1), the level of facilities and intelligent informatization in the waiting area (B2), and the quality of waiting facilities and services (B3).

2.

Waiting Dimension

As shown in

Table 17. Principal Component Analysis of the Waiting Dimension (Questionnaire B).

Name	Factor Loading Coefficient		Communality
	B4	B5
Manual guidance	0.833	0.024	0.694
Waiting time	0.699	0.377	0.631
Clear medical instructions to avoid wasting time	0.797	0.283	0.715
Timely information communication to avoid wasting time	0.814	0.173	0.693
Privacy protection	0.864	0.046	0.749
Diagnosis and treatment of children’s diseases	0.17	0.881	0.805
Various medical expenses	0.122	0.889	0.805
Eigenvalue (before rotation)	3.739	1.353	-
Percentage of variance explained (before rotation)	53.415%	19.327%	-
Cumulative percentage of variance explained (before rotation)	53.415%	72.742%	-
Eigenvalue (after rotation)	3.271	1.821	-
Percentage of variance explained (after rotation)	46.733%	26.009%	-
Cumulative percentage of variance explained (after rotation)	46.733%	72.742%	-
Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy	0.824		-
Bartlett’s test of sphericity	992.918		-
Degrees of freedom (DF)	21		-
p-value	0.000		-

Note: “after rotation” indicates that the factor has been rotated.

, according to the analysis of factor loadings, the two main factors of the waiting dimension are waiting experience (B4) and speed and cost of medical care (B5). Within the waiting experiences factor, the main contributing elements are manual guidance, waiting time, clear instructions for seeing a doctor, timely information communication, and privacy protection. Regarding the medical treatment speed and cost factor, the main contributing elements are the diagnosis of diseases in children and the treatment speed and various medical expenses.

3.

Outpatient appointment dimension

As shown in

Table 18. Principal Component Analysis of Outpatient Appointment Dimension (Questionnaire B).

Name	Factor Loading Coefficient			Communality
	B6	B7	B8
Bookable quantity	0.685	0.129	0.038	0.828
Clear appointment process	0.733	0.104	0.086	0.843
Convenience of Appointment	0.177	0.842	0.261	0.808
Lack of appointment information or guidance	0.503	0.678	0.196	0.964
Appointment system or device malfunction	0.877	0.783	0.268	0.875
Appointment attitude of doctors or hospital staff	0.887	0.201	0.825	0.89
Appointment sequence or results	0.89	0.128	0.741	0.81
Difficulties caused by epidemic prevention and control measures	0.892	0.211	0.824	0.898
Eigenvalue (before rotation)	4.791	1.705	1.419	-
Percentage of variance explained (before rotation)	59.893%	21.310%	5.243%	-
Cumulative percentage of variance explained (before rotation)	59.893%	81.203%	86.445%	-
Eigenvalue (after rotation)	3.48	2.531	0.904	-
Percentage of variance explained (after rotation)	43.505%	31.638%	11.302%	-
Cumulative percentage of variance explained (after rotation)	43.505%	75.143%	86.445%	-
Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy	0.886			-
Bartlett’s test of sphericity	2023.304			-
Degrees of freedom (DF)	28			-
p-value	0.000			-

Note: “after rotation” indicates that the factor has been rotated.

, in factor B6, the elements with heavy load are: bookable quantity and clear appointment process. In factor B7, the elements with heavy loads are as follows: appointment convenience, a lack of appointment information or guidance, and appointment system or device malfunction. In factor B8, the elements with larger loads are as follows: negative attitude of doctors or hospital staff when making appointments, appointment sequence or results, and the distress caused by epidemic prevention and control measures. Factors B6, B7, and B8 can be named queuing waiting time and process clarity, queuing environment comfort and information guidance, and doctor or staff attitude. Therefore, the main influencing elements of outpatient queuing appointment dimension are bookable quantity and clear appointment process (B6), convenience of appointment and lack of appointment information or guidance (B7), and the attitude of doctors or hospital staff (B8).

4.

Outpatient Management System Dimension

As shown in

Table 19. Principal Component Analysis of Outpatient Management System (Questionnaire B).

Name	Factor Loading Coefficient		Communality
	B9	B10
Children’s exclusive system settings	0.847	0.29	0.802
Pediatrician clinic time setting	0.9	0.244	0.87
Appointment route and system settings	0.884	0.275	0.857
Real time information prompt system settings	0.908	0.282	0.904
Multilingual system settings	0.858	0.301	0.827
Medical safety system settings	0.274	0.892	0.871
On-site service system settings	0.182	0.881	0.81
Setting up a training system for hospital staff	0.337	0.848	0.833
Eigenvalue (before rotation)	5.426	1.348	-
Percentage of variance explained (before rotation)	67.821%	16.853%	-
Cumulative percentage of variance explained (before rotation)	67.821%	84.674%	-
Eigenvalue (after rotation)	4.092	2.682	-
Percentage of variance explained (after rotation)	51.153%	33.522%	-
Cumulative percentage of variance explained (after rotation)	51.153%	84.674%	-
Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy	0.901		-
Bartlett’s test of sphericity	2348.467		-
Degrees of freedom (DF)	28		-
p-value	0.000		-

Note: “after rotation” indicates that the factor has been rotated.

, in factor B9, the elements with higher loads are: exclusive system settings for children and outpatient time settings for pediatricians, appointment routes and system settings, real-time information prompt system settings, and multilingual system settings. In factor B10, the elements with higher load include medical safety system settings, on-site service system settings, and hospital staff training system settings. Factors B9 and B10 can be named “pediatric exclusive system settings” and “on-site service system settings”, respectively. Therefore, the main influencing elements of the outpatient management system dimension are convenient medical treatment (B9) and service quality (B10).

5.

Outpatient Visiting Process Dimension

As shown in

Table 20. Principal component analysis of outpatient visits process dimension (Questionnaire B).

Name	Factor Loading Coefficient		Communality
	B11	B12
Access process efficiency	0.217	0.934	0.919
Patients fill out multiple forms and materials	0.209	0.936	0.92
Access process guidance and guidance	0.908	0.219	0.873
Communication between doctors and nurses	0.871	0.193	0.797
Lack of efficient interoperability between access processes	0.862	0.274	0.818
The access process settings are not convenient enough	0.893	0.226	0.849
High repeatability of access process settings	0.88	0.268	0.846
Eigenvalue (before rotation)	4.752	1.269	-
Percentage of variance explained (before rotation)	67.885%	18.134%	-
Cumulative percentage of variance explained (before rotation)	67.885%	86.019%	-
Eigenvalue (after rotation)	3.99	2.031	-
Percentage of variance explained (after rotation)	57.005%	29.014%	-
Cumulative percentage of variance explained (after rotation)	57.005%	86.019%	-
Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy	0.866		-
Bartlett’s test of sphericity	1944.137		-
Degrees of freedom (DF)	21		-
p-value	0.000		-

Note: “after rotation” indicates that the factor has been rotated.

, in factor B11, the main contribution elements are as follows: the guidance and guidance of the visit process, effective communication between doctors and nurses, efficient intercommunication between visit processes, the convenience of visit process settings, and flexible ways to see a doctor. Factor B11 can be named “access process orientation”. In factor B12, the main contributing elements are waiting times and patients filling out multiple materials. Factor B12 may be titled “Ease of Access Process”. Therefore, the main influencing elements of the visit process dimension are visiting process orientation (B11) and visit process convenience (B12).

4.5. Structured Interview Analysis

Based on preliminary field research analysis, questionnaire descriptive analysis, satisfaction analysis, and questionnaire principal component analysis, this study has obtained detailed analysis results on the elements that affect children’s medical service experience according to five dimensions. However, at its core, design thinking is human-centered. This study still lacks detailed personal interview accounts to create a more in-depth supplement to the already obtained results of the influencing elements. Therefore, this study needed to supplement the results of the detailed analysis with structured interviews. The interview results are divided into a result analysis and a user journey map.

The structured interview process is:

• Respondents brainstormed on a topic describing their experience with pediatric patients during outpatient care in the hospital.
• All questions were recorded in notes and categorized according to the topics raised during the brainstorming session.
• Simultaneously, the researcher observed and took notes on the activities of the participants. These notes were recorded and organized while reviewing the video after the meeting.
• After the interview, the interview content was combined with the observer’s notes and categorized by theme in order to analyze the large amount of unstructured oral content.
• Through the questions posed by the interviewers, the research aggregated keywords and grouped them to form themes for strategy formulation. Based on these themes, all interview participants jointly developed goals for improving the pediatric outpatient experience and strategies for addressing each goal.

4.5.1. Interview Results

The results of the information collected in the unstructured interviews are summarized in

Table 21. Organized Structured Interview Results.

Interviewee	Average Score	Process Improvement Suggestions	Suggestions for Improving Service	Convenience Issues	AI System Improvement Suggestions	Support for Artificial Intelligence Systems	Recommendations for Graded Diagnosis and Treatment
Doctor	5.4	Increase outpatient science promotion	Optimize the update speed of medical devices	Insufficient medical equipment	Improve the electronic medical record system	Support	Strengthen cooperation with community hospitals to achieve hierarchical diagnosis and treatment
Specialist nurse	5.25	Increase patient education outreach	Strengthen service attitude and communication	Unreasonable appointment times	Complete medical equipment	Support	Realize hierarchical diagnosis and treatment, and promote the family doctor system
Outpatient guardian	4.5	Reduce waiting times	Increase children’s entertainment facilities	Not enough pediatric specialists	Perfect reservation and registration system	Support	Establish hierarchical diagnosis and treatment management departments to achieve overall resource utilization
Administrative staff	6.5	Strengthen hospital management processes	Increase medical staff training	Short service life of medical devices	Improve medical device procurement management	Support	Realize hierarchical diagnosis and treatment and strengthen cooperation with primary medical institutions
Child psychologist	5	Increase children’s counseling services	Strengthen doctor–patient communication and exchange	Improper use of medical devices	Improve medical device use training	Support	Introduce more advanced medical equipment to improve medical efficiency

. The results are organized by population and element. These results can further supplement the results of the preliminary field survey analysis, the overall descriptiveness of the questionnaire, the satisfaction analysis of the questionnaire, and the principal component analysis.

4.5.2. User Journey Map

Through the analysis of structured interviews, this study also summarizes the user journey map of children’s medical experience. Using the journey map can more clearly discover the user’s emotional changes throughout the service process, which helps this research to identify influencing elements. As shown in Figure 7, pediatric patients and accompanying parents had different emotional changes during the counseling process. Therefore, the emotional needs of the two are also different. For example, the emotional fluctuations of children during the consultation process involve the fear of the treatment process and the anxiety of waiting for a consultation, but they do not have much understanding of the connections and progress of the whole process. Regarding parents, they are more sensitive to emotional expressions of “waiting” and “queuing”. Therefore, they care more about the progress of each step in the process. Their anxiety is manifested in three areas: the child’s emotional changes, the long waiting times, and the unknown steps of the consultation. This further provides a supplement to the elements research that affects children’s medical service experiences in this study.

4.6. Summary of Influencing Elements

According to the guidance of design thinking, in the first stage (empathy) and the second stage (definition), this research is based on preliminary field research, questionnaire descriptive analysis, questionnaire satisfaction analysis, questionnaire principal component analysis, a structured interview analysis, and a summary of the main elements that affect children’s medical service experiences and improve strategies for children’s medical services. These elements can be briefly summarized as follows:

• Preliminary field investigation and analysis summary: outpatient procedures are complicated, child patients have to queue throughout all aspects of treatment, child patients are emotionally uneasy during the waiting process, short times for medical treatment leads to an inability to communicate effectively with doctors, communication barriers between doctors and patients, insufficient nursing services, and insufficient drug reserves.
• Summary of questionnaire descriptive analysis: the improvement of outpatient facilities, the comfort level of waiting area, waiting time for consultation, outpatient efficiency based on digital system, medical care service, doctor–patient communication.
• Questionnaire satisfaction analysis and dimension weight summary: waiting facilities dimension, outpatient management system dimension, outpatient queuing appointment dimension, outpatient visiting process dimension, and waiting dimension.
• Summary of principal component analysis of the questionnaire: waiting dimension (waiting time, the speed of diagnosis and treatment, and cost), waiting facilities dimension (environmental comfort, facility comprehensiveness, convenience, informatization level, and facility service quality), outpatient appointment dimension (appointment experience, medical staff appointment service attitude and fairness, epidemic-related issues, appointment information guidelines, and appointment process), outpatient management dimensions (convenient medical treatment and service quality), outpatient visiting process dimensions (visit efficiency, visit process information sharing and appointment convenience, flexible ways of seeing a doctor, the orientation of the visit process, and the convenience of the visit process).

These elements represent the main concerns of pediatric patients and various stakeholders regarding the medical service experiences of pediatric outpatients. Improving and strengthening these elements can further improve child outpatients’ medical service experiences.

5. Discussion

First, under the guidance of design thinking, this section will propose a set of children’s outpatient service strategy models based on the main influencing elements. Then, the effectiveness of the model is discussed based on a satisfaction analysis and simulation validation.

5.1. Children’s Outpatient Medical Services Node Improvement Plan Based on Influencing Elements

Before establishing the children’s outpatient medical services strategy model, it is necessary to determine the specific research points involved in the children’s outpatient medical service strategies. Specifically, first, the influencing elements of children’s outpatient service experiences obtained in the above research were classified in detail according to the medical treatment process (before, during, and after seeing a doctor). Then, under the guidance of design thinking, through brainstorming and other methods, specific corresponding solutions are proposed for each element. The classification and methods are shown in

Table 22. Influencing elements of children’s medical service experiences and their corresponding solutions.

Before Seeing a Doctor	Peer-to-Peer Scheme
Orientation of the treatment process	Formulate triage process guidelines, classify, and allocate different types of patients, and divert patients. Provide official website or app to publicize information.
Medical staff service attitude and fairness	Establish an evaluation system to motivate medical staff to provide quality services to patients.
Experience of waiting in line	Provide a comfortable waiting environment and provide real-time queuing and waiting conditions through the information system.
Convenient medical treatment	Provide online appointment and registration services, reduce on-site queuing time, and set up online appointment system, diagnosis and treatment guide, and consultation system.
Information sharing and appointment convenience	Provide appointments, registration, and other services through information technology to strengthen the protection of patient information.
Appointment and consultation convenience	Provide a variety of appointment and medical treatment methods, such as online appointments, telephone appointments, self-service machines, etc.; patients can be assigned their own number, be provided with intelligent guidance, and other functions.
Facility comprehensiveness	Regularly maintain and update the environment and facilities of the clinic.
Waiting experience	Provide ample seating in waiting areas.
Whilst Seeing a Doctor	Peer-to-Peer Scheme
Speed and cost of diagnosis and treatment	Improve the efficiency of medical services, avoid unnecessary examinations and treatments, and provide multiple payment methods.
Waiting environment	Provide a comfortable waiting environment and provide entertainment facilities.
Waiting information flow	Provide real-time consultation information and doctor visit information.
Medical service quality	Improve the professional level of doctors and nurses and provide a mechanism for patient evaluation.
Medical efficiency and experience	Set up a guidance board for the treatment process to aid patients in understanding the treatment process and conditions in a timely manner.
Facility comprehensiveness	Regularly maintain and update the environment and facilities of the clinic.
Flexible ways to see a doctor	Provide a variety of ways to see a doctor, such as video consultation, online medical treatment, etc.
waiting experience	Provide ample seating in waiting areas.
After Seeing a Doctor	Peer-to-Peer Scheme
Doctor or staff attitude	Provide a variety of communication methods and channels.
Quality of pediatric outpatient services	Provide professional pediatricians and facilities to improve children’s medical experience and satisfaction.
Facility comprehensiveness	Provide elf-service medicine dispensers and payment machines.

. These methods can solve the problems faced by the influencing elements on a point-to-point basis.

5.2. Systematic Conception of Strategy Model Based on Element Analysis

After determining the solution of a single element, the systematic logic of the strategy model needs to be given. Under the guidance of methods such as design thinking and brainstorming and based on the summary and analysis of influencing elements, this study selected a hierarchical diagnosis and treatment plan [121,122] as the source of inspiration for the systematic logic (Figure 8). Hierarchical diagnosis and treatment can enable children to implement hierarchical diagnosis and treatment based on elements such as the child’s age, disease type, and severity of illness when seeking medical treatment. This ensures that patients receive the right treatment in the right place at the right time. The implementation of this systematic logic can make full use of and optimize medical resources, improve medical efficiency and quality, and improve child outpatients’ medical service experiences.

5.3. A Model of Healthcare Delivery Strategies for Improving Children’s Medical Service Experiences

Under the guidance of design thinking, based on the node promotion plan (corresponding to the influencing elements) and systematic logic, as shown in Figure 9, this study proposes a medical service side strategy model. The detailed introduction is as follows, according to the factors before, during and after seeing a doctor.

5.3.1. Before Seeing a Doctor

• Step 1: Appointment for medical treatment

This step involves the following influencing elements: medical treatment process orientation, medical staff service attitude and fairness, queuing experience, convenient medical treatment, information sharing and appointment convenience, appointment and medical treatment convenience, facility integrity, and waiting experience. There are three ways for patients to make an appointment, the details are as follows:

• Method A: Utilize the Xinhua Hospital app to make an appointment online. The patient registers an account on the application (app), enters the description of the condition, and generates an electronic medical record and appointment record. The app can provide information such as doctor’s information, choice of visit time, details of visit expenses, etc. This can help users to better understand medical services. The app uses AI technology to automatically judge the patient’s condition and classify it (level 1, level 2, and level 3 diseases). Then, the app can give appointment recommendations for various departments and the relevant doctor’s working hours and dates in order to allow patients to make the choices. At the same time, the app can also provide manual online consultation services. After completing the above process, the app can give patients an appointment number and guide the treatment process in order to help the patient arrive at the hospital for diagnosis and treatment according to the appointment time. This can effectively prevent patients from waiting in long queues in the outpatient hall. The app conceptual design scheme is shown in Figure 10.
• Method B: Make an appointment at the manual registration window. When the patient arrives at the hospital, they will queue up for registration at the manual registration window in the outpatient hall. The electronic screen of the registration window can display the registration process guidelines, cost details, types of departments and the number of people waiting in order to guide patients to see a doctor in an orderly manner and avoid chaotic and disorderly queuing conditions. The medical staff in the artificial window will enter the symptoms described by the patients into the system. The system generates case records based on the entered patient information and uses AI technology to automatically determine illness level (level 1, level 2, and level 3). Then, AI can make an appointment registration according to the relevant doctor’s work schedule and timetable on that day and generate an appointment number. Finally, the patient will go to the doctor’s department for consultation according to the guidance of the treatment process and the patient’s medical record information file provided by the outpatient window.
• Method C: Make an appointment at the self-service machine in the outpatient hall. The patient arrives at the hospital, enters personal information on the self-service machine, enters a description of the condition, and generates an electronic medical record and appointment record. Self-service machines can provide information about doctors, possible visiting times, details of visit expenses, etc., to help users better understand medical services. The other processes of the self-service machine system are the same as those of the manual registration window, the difference is that the service is provided by the self-service machine.

2.

Step 2: Patient Triage

This step involves influencing elements: the orientation of the treatment process, the waiting in line experience, and the waiting experience. The details are as follows: the appointment system uses NPL and AI technology to triage patients into three levels of illness: 1, 2, and 3. Level 1 patients have milder conditions, minor illnesses and common diseases, such as colds, fever, and mild diarrhea. Grade 2 patients can be considered to have moderate diseases and frequently occurring diseases, such as pneumonia, asthma, diabetes, etc. This level requires doctors to have the ability to conduct a comprehensive physical examination, disease diagnosis, drug treatment, and basic surgical treatment. Level 3 patients suffer from severe diseases, rare diseases, and refractory diseases, such as leukemia and brain tumors. Doctors need to have high professional knowledge and skills and can carry out complex disease diagnosis and surgical treatments. Patients go to the corresponding area to wait for treatment according to the triage guidelines and consultation guidance information given at the time when the appointment was made. Some grade 1 patients had milder diseases. They can communicate with medical staff online through the app and receive treatment suggestions without having to go to the treatment site in person. Patients with grade 2 and 3 illnesses can avoid long waits by arriving in a timely manner to their appointment.

5.3.2. Whilst Seeing a Doctor

• Step 1: Waiting in line for treatment

This step involves influencing elements: waiting environment, waiting experience, waiting information flow, and comprehensiveness of facilities. The specific details are as follows: according to the appointment time, the patient arrives at the corresponding department, enters the waiting area, and waits for their number to be called. The waiting area can provide patients with necessary information before seeing a doctor, such as patient manuals and visiting guidelines, to help patients understand the visiting process and relevant regulations. Additionally, these areas can provide a comfortable, warm, safe, and interesting waiting environment, providing children and parents with a better waiting experience. For example, some children like books, toys, play facilities, etc. This keeps the children entertained while they wait. At the same time, the waiting area can provide electronic display screen prompts, text message prompts, or app pop-up window prompts. This can provide patients with clearer, easier, and more accurate information, informing patients of the queuing situation and preventing uneasiness in parents and children due to a poor information flow. Level 2 and Level 3 waiting areas can provide a more spacious, comfortable, and suitable environments and facilities for the diagnosis and treatment of patients, so that the waiting area can accommodate more children and parents. In addition, hospitals need to provide patients with corresponding psychological support and counseling services to help patients relieve anxiety and tension and better face their illness.

2.

Step 2: Inquiry and examination

This step involves the efficiency and experience of seeing a doctor, flexible ways of seeing a doctor, and quality of medical services. The specific details are as follows: first-level patients do not need to come to the hospital but can communicate with doctors through online consultations. For patients with grade 2 and 3 diseases, the doctor will conduct face-to-face consultations with the patient to understand the patient’s condition and, at the same time, perform the necessary physical examination, laboratory tests, and imaging examinations. For technical support, a Microsoft SQL Server 2022 intelligent diagnosis algorithm can be used to assist doctors to standardize clinical diagnosis, aid in future clinical decision making, recommend optimal treatment plans, or directly give referral advice and make an appointment with a specialist professional through the regional referral platform. Other technology support includes a auxiliary diagnosis and treatment system for children’s critical illness using machine learning, deep learning, and medical knowledge maps to establish a CDSS system for children’s severe disease, which can realize the early prediction of serious diseases, the real-time monitoring of a patient’s risk level, and continuously combine clinical examination and medical examination based on evidence-based tests and confirm the results in order to re-evaluate the degree of disease risk and give early warning signs to reduce the incidence of severe diseases in children.

3.

Step 3: Diagnosis and Treatment and Medical Records

This step involves influencing elements: medical service quality, medical efficiency, and experience. The specific details are as follows: according to the patient’s condition and examination results, the doctor will make a diagnosis and formulate corresponding treatment plans, such as prescribing drugs, performing surgery, and using physical therapy. At the same time, the doctor will explain the condition, treatment plan, and precautions to the patient and inform the patient of the arrangements for follow-up visits. When patients have level 2 and level 3 conditions, the hospital will provide more comprehensive and in-depth services, such as a physical examination, disease diagnosis, drug treatment, and routine surgical treatment. Doctors will formulate a personalized treatment plan according to the patient’s condition, provide more professional and detailed medical advice and guidance for the patient, and give guidance for the next step of treatment. Visiting records, prescription records, and disease records are entered into the system through the doctor’s computer, updated in the patient’s personal electronic medical records, and printed and handed over to the patient. At the same time, patients can also check their electronic medical records at any time through the online service platform provided by the hospital. Additionally, they can communicate online with medical staff through the online service platform.

4.

Step 4: Rehabilitation command

This step involves influencing element of medical service quality. The specific details are as follows: the hospital provides patients with a series of auxiliary and support services, such as rehabilitation treatment, nutritional support, psychological counseling, etc., to help patients recover as soon as possible. The hospital will also provide corresponding home care and rehabilitation guidance services to help patients and their families better cope with the disease and achieve comprehensive rehabilitation and treatment. Especially for patients in the with level 3 illnesses, the hospital also needs to provide more in-depth medical services, such as:

• Doctors and nurses making rounds: Regularly inspect the waiting area of the level 3 area, check the patient’s physical condition, identify the progress of the disease, and adjust the treatment plan in time.
• Professional nursing: Provide professional nursing services for patients, such as infusion, wound care, vital signs monitoring, etc.
• Disease knowledge and education: provide publicity and education services for patients’ diseases and help patients understand the causes, symptoms, treatment methods, and preventive measures of diseases.
• Social work services: Provide social work services to help patients solve life, economic, psychological, and other problems that may be encountered during medical treatment.
• Psychological counseling: Provide psychological counseling services to help patients alleviate negative emotions such as anxiety and fear, adjust their mentality, and enhance their disease resistance.

5.3.3. After Seeing a Doctor

• Step 1: Pay for medicine.

This step involves the following influencing elements: the attitude of doctors or staff, the quality of pediatric outpatient services, the comprehensiveness of facilities, the speed of diagnosis and treatment, and cost issues. The specific details are as follows: After the treatment, the patient goes to the pharmacy to pick up the medicine and pay the fee according to the treatment plan formulated by the doctor. At the same time, patients can pay the corresponding medical expenses in the hospital or online. There are three ways to pay and collect medicines:

• Method 1: Xinhua Hospital app payment. Patients pay according to the drug list prescribed by the doctor and go to the pharmacy to pick up the medicine.
• Method 2: Pay at the payment window of the pharmacy and pick up the medicine. When patients go to the pharmacy to queue, the doctor will use the system to make an appointment for the patient to take a number in advance and inform the pharmacy to arrange and pack the medicines in advance, so that the patients can receive their medication directly when they arrive at the pharmacy. This prevents long queues and improves the efficiency of the pharmacy.
• Method 3: Pay for and collect medicine at the pharmacy self-service machine. After paying at the self-service machine in the pharmacy, the patient goes directly to the window to pick up the medicine.
• Method 4: Purchase medication at pharmacies by themselves. Patients go to the hospital pharmacy or a cooperative pharmacy to purchase and collect medicines by themselves and receive the prescribed medicines issued by doctors.

2.

Step 2: Follow-up.

This step involves the following influencing elements: doctor or staff attitudes and pediatric outpatient service quality. The doctor will follow up and review the patient according to the treatment plan. Patients should go to the hospital for follow-up and review on time and cooperate with the doctor’s treatment plan.

3.

Step 3: Evaluation of medical services.

This step involves the following influencing factors: the quality of pediatric outpatient services. After the consultation, the patient’s medical service evaluations and suggestions are collected online and offline to improve the quality of medical services.

5.4. Strategy Model Evaluation

The evaluation of the strategy model is divided into the evaluation of the completeness of the medical service process and the evaluation of satisfaction (effectiveness) of the strategy model.

First, as shown in Figure 11, this study first takes the hypothetical individual Xiaoming as an example (age: 8 years old; gender: male; medical needs: symptoms such as cough and fever) and inputs his data into the medical service strategy to carry out simulation process verification. The verification results show that the process model can completely meet the medical needs of child patients in terms of service processes.

Secondly, based on element analysis, after proposing a strategy model that can improve children’s outpatient service experiences, this study needs to evaluate the effect of the strategy model to verify whether the strategy model proposed in this study is effective. This study analyzed patients’ satisfaction with the strategic model of children’s outpatient medical services in Xinhua Hospital through the Questionnaire D satisfaction survey. The scores setting and calculation methods of Questionnaire D are the same as those of Questionnaire C.

Table 23. Questionnaire D score results.

Dimension	Weight	Score
Waiting	16.01%	4.2
Waiting facilities	24.49%	3.8
Outpatient queuing appointment	20.18%	3.7
Outpatient management system	21.67%	3.5
Outpatient visiting process	17.64%	4.4

and

Table 24. Compare the results of Questionnaire D and Questionnaire C.

Dimension	Weight	Questionnaire C	Questionnaire D
Waiting	16.01%	1.5	4.2
Waiting facilities	24.49%	2.0	3.8
Outpatient queuing appointment	20.18%	2.1	3.7
Outpatient management system	21.67%	1.7	3.5
Outpatient visiting process	17.64%	2.0	4.4

show the scoring results and their comparison with the survey satisfaction rating results.

According to the scoring data in Table 22, substituting Formula (2) can obtain:

$$CSI=0.1601\times 4.2+0.2449\times 3.8+0.2018\times 3.7+0.2167\times 3.5+0.1764\times 4.4\approx 3.6386$$

(5)

The overall satisfaction score is around 3.6396, which is converted into a percentage, namely:

$$3.6386 ÷ 5\times 100\% \approx 72.77\%$$

(6)

According to the calculation results, the satisfaction rate of child outpatients’ medical service experiences based on the strategy model proposed in this study is about 72.77%. Compared with the original satisfaction score of 37.51%, it has been significantly improved. The results show that the strategic model proposed in this study has a significant effect on improving children’s experiences of the outpatient clinic of Xinhua Hospital.

6. Conclusions

Under the guidance of design thinking, based on the preliminary field research on the pediatric outpatient clinic of Shanghai Xinhua Hospital, questionnaire descriptive research, questionnaire satisfaction research, questionnaire principal component analysis research, and structured interview research, this study summarizes the impact main influencing elements of children’s outpatient healthcare experiences. Based on element analysis, a set of medical service strategy models is proposed. The verification found that under the guidance of this strategy model, the experience of children’s outpatient services can be effectively improved. Additionally, it should be clarified that this strategy model is only a guiding strategy for improving outpatient care for children. It does not provide detailed solutions around some specific issues and specific implementations. At the same time, it is not a complexly engineered system design but only provides a reference for improving children’s medical services in terms of strategic logic. However, there are still many problems in this research. For example, this study only conducted a case study around a single hospital, and the sample may not be universally representative; the strategy proposed in this study only focused on the elements that affect children’s outpatient service experiences, and the research results may be one-sided because the strategy model has not been used. Therefore, the effectiveness of results obtained via simulation verification also have certain shortcomings. Its actual effect still needs to be verified and adjusted in the practice test phase. Since it is only a guiding strategy, this study does not propose a corresponding solution to improve child outpatients’ medical service experiences in terms of actual system design. In conclusion, the results of this study can serve as a reference for improvement in other medical institutions, as well as in pediatrics and other medical fields, and have important implications for the sustainable development of people-centered outpatient care in China. Furthermore, by integrating the perspective of multidisciplinary teams and feedback from stakeholders, design thinking can provide a more holistic solution that helps healthcare organizations improve patient experience and satisfaction while delivering services.