Abstract
Background
The digital transformation of medical services promotes innovation in medical services, changes service models, optimizes medical processes, improves service quality, and ultimately enhances patient experience and satisfaction. This study aims to explore the relationship between digital transformation of medical services and patient satisfaction.
Method
A cross-sectional online-based survey was conducted with 348 respondents in January and February 2023. We employed the Patient Psychological Empowerment Scale, Patient Satisfaction Scale, and Technology Readiness Scale to measure the digital transformation of medical services, patient psychological empowerment, patient satisfaction, and technology readiness, respectively. Confirmatory factor analysis was applied using the maximum likelihood estimation method. Scale dimensions and item reliability were tested for their validity and goodness of fit. SPSS v26.0、AMOS v23.0 and the Andrew F. Hayes PROCESS macro were used for data management and analyses.
Results
The digital transformation of medical services has a significant positive impact on patient satisfaction (B = 0.387, t = 8.476, P < 0.001), and patient psychological empowerment positively moderated (B = 0.219, t = 4.650, P < 0.001). The digital transformation of medical services has a significant positive impact on patient psychological empowerment (B = 0.447, t = 9.266, P < 0.001), and patient psychological empowerment had a significant positive impact on patient satisfaction (B = 0.366, t = 7.896, P < 0.001). In addition, the results of random sampling showed that psychological capital mediated the effect of digital transformation of healthcare services on patient satisfaction. The interaction term of digital transformation of healthcare services and technology readiness has a significant positive impact on patients’ psychological empowerment (B = 0.192, t = 4.477, P < 0.001), and technology readiness plays a positive moderating role.
Conclusion
This study shows that digital transformation positively affects patient satisfaction through psychological empowerment and is moderated by technology readiness. By investigating the moderating role of technological readiness, this study fills the gap in the psychological empowerment mechanism in the relationship between digital transformation of healthcare services and patient satisfaction, and provides theoretical and practical guidance for hospital administrators to optimize digital services and health policy implementation.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12913-025-13641-5.
Keywords: Patient satisfaction, Technology readiness, Digital transformation, Digitalization of medical services, Patient psychological empowerment
Background
Digital technology has propelled the medical industry through multiple stages of integrated development, encompassing mobile health [1], Internet healthcare [2], artificial intelligence in healthcare [3], and digital health [4]. The digital transformation of the medical industry has emerged as a focal point of national policy attention and academic research. In this context, China’s digital healthcare industry has garnered significant government attention and industrial policy support, leading to a rapid expansion of its market size. From 2019 to 2020, the establishment of Internet hospitals accelerated, with the number skyrocketing to 995 by the end of 2020. As of June 2021, the figure had surpassed 1600. Simultaneously, the market size is projected to grow from 3 billion yuan in 2012 to nearly 200 billion yuan by 2026. Driven by factors such as a thriving market, an aging population, and an increasing share of residents’ medical consumption, the digital healthcare industry boasts vast prospects [5].
The digital transformation in the healthcare field has garnered widespread attention from scholars as a significant phenomenon [6]. However, existing studies focus on the macro level, such as development status [7], challenges [8], trends [9], transformation strategies [10] and their significance [11], particularly in relation to digital transformation in the manufacturing sector [12], but fail to examine individual psychological mechanism, which is what this study aims to address. Based on this, unlike previous research focusing on macro aspects, this study uniquely investigates micro-level psychological mechanisms, thereby offering new insights into patient-centered digital healthcare transformations.
Theoretical framework and hypotheses
Theoretical framework
Satisfaction stems from psychological experiences [13]. This study incorporates the theory of customer psychological empowerment [14] to delve into the psychological aspects of the intrinsic connection between the digital transformation of medical services and patient satisfaction.
Conger first proposed the concept of psychological empowerment, stating that empowerment is an internal motivation and psychological empowerment experience. Subordinates’ and employees’ perception of being ‘authorized’ can enhance work motivation, which in turn affects attitude and behavior [15]. Thomas and Velthouse extended the meaning of psychological empowerment, arguing that psychological empowerment is a complex subjective psychological experience [16]. Spreitzer further refined the concept of psychological empowerment, emphasizing that it is influenced by the work environment rather than stable and continuous personality traits [17]. Based on Spreitzer’s view [17], Li proposed that attention should be paid to the psychological experience and intuition of employees after being authorized. Subsequently, numerous scholars attempted to explore customer psychological empowerment based on the concept of psychological empowerment [18]. Wathieu proposed that customer psychological empowerment is based on the psychological changes that increase the control of the customer, emphasizing the psychological perception and subjective feelings of the customer after being authorized [19]. From the perspective of customer psychology, Len believed that customer psychological empowerment reflects the degree to which customers control the service process based on their willingness, rather than the true transfer of rights [20]. Kuchuk pointed out that customer trust perception can affect their psychological empowerment perception and behavior [21]. In terms of the outcome variables of customer psychological empowerment, they are generally divided into two dimensions: attitude and behavior [21]. In terms of attitude, Chen’s empirical research showed that customer psychological empowerment has a positive impact on service quality and satisfaction. In terms of behavior, Ramani argued that customers’ perception of being empowered enhances their purchase intention and sustains their attention to related products and services [22]. Liu also found through research that customer psychological empowerment has a positive effect on their participation behavior [23]. In summary, it can be found that customer psychological empowerment influences their attitudes and behaviors.
For hospitals, patients are like ‘customers’ [24], and medical services belong to special products [25]. For patients, patient psychological empowerment shares similarities with employee psychological empowerment and customer psychological empowerment. Whether the transfer of resources to the authorized object has an impact depends on the psychological response of the patient after authorization. Based on this, this study focuses on the psychological empowerment of patients when using digital medical services, defined as the perception and utilization of hospital granted powers (such as autonomy) by patients in using digital and smart medical services, and the use of intelligent devices to control the diagnosis and treatment process, resulting in a sense of control and self-efficacy in the medical service experience (Fig. 1).
Fig. 1.
Research theoretical model
Hypotheses development
Digital transformation of medical services and patient satisfaction
In terms of improving service quality, the digital transformation of medical services can optimize patient experience, enhance service quality and improve patient satisfaction. The application of digital technology in the medical field has significantly improved the quality of medical services [26], thereby promoting an increase in patient satisfaction [27]. In terms of improving service experience, the traditional medical model has caused serious problems such as long registration time, long waiting time, long medication collection time, and short consultation time, which seriously affect service efficiency and patient satisfaction [28]. However, the digital transformation of medical services has effectively improved the medical environment, enhanced service quality and efficiency, and brought a new medical experience to patients [29]. van Engen V et al. believed that technological experience, medical convenience, and value recognition are important factors affecting patient satisfaction [30], and improving the medical experience can enhance patient satisfaction [31]. At the level of service innovation, Berry pointed out that service innovation in medical institutions, such as process innovation, model innovation, and technological innovation, has a significant impact on patient satisfaction [32]. In short, the digital transformation of medical services affects patient satisfaction from three perspectives: service quality, service experience, and service innovation. Thus, we propose:
Hypothesis 1:
The digital transformation of medical services is positively related to patient satisfaction with medical treatment.
Digital transformation of medical services and patient psychological empowerment
Based on the theory of customer psychological empowerment, ‘autonomy’ refers to the ability of customers to independently decide on relevant elements such as time, location, method, and content of consumption, while ‘choice’ refers to the perceived degree of freedom of choice in services. Osamor P E et al. researched the impact of women’s autonomy in decision-making on their health in developing countries showing that autonomous choices and health care decisions made through resource control and information manipulation were positively associated with health outcomes [33].
Overall, patient psychological empowerment refers to the psychological perception and state of patients towards digital medical services when seeking medical treatment. The authorization measures such as the right to choose and autonomy in digital medical services will strengthen patients’ perception of autonomy, enhance their sense of control and self-efficacy, and ultimately increase the degree of psychological empowerment. Therefore, we propose:
Hypothesis 2:
The digital transformation of medical services is positively related to patients’ psychological empowerment.
Patient psychological empowerment and patient satisfaction
Sunmee Choi’s research findings indicated that perceived control ability and service expectations can influence customer responses [34]. Li et al. revealed the direct effects of operational autonomy and decision-making autonomy of healthcare service robots on customer satisfaction through an experimental scenario approach, as well as the positive mediating role of applicable functionality in the effects of healthcare service robot autonomy on customer satisfaction [35]. Len et al. found that enhancing customer psychological empowerment can improve their satisfaction [20]. For experiential consumption, authorizing customers, encouraging them to participate in the consumption process, providing diverse products and services, and allowing customers to choose their own service personnel and methods can enhance their sense of control over consumption and ultimately improve customer satisfaction [36]. With the increasing degree of digital transformation of medical services, patients have gained more autonomy in receiving medical services. Through mobile apps, services such as registration, payment, diagnosis and treatment, and drug delivery can be realized, enhancing patients’ self-efficacy. Based on the theory of customer psychological authorization, patients experience a sense of control when they perceive authorization, and as the level of psychological authorization increases, their satisfaction with seeking medical treatment also increases. Therefore, we propose:
Hypothesis 3:
Patient psychological empowerment is positively related to patient satisfaction.
Hypothesis 4:
Patient psychological empowerment partially mediates the relationship between the digital transformation of medical services and patient satisfaction.
The mediating role of technical readiness
Parasuraman proposed the concept of technology readiness, which represents the psychological trend of users to use new technologies to achieve their goals. Users have an optimistic attitude towards innovative services, a good user experience, and a strong sense of control, while low technology readiness users have the opposite [37]. Van indicated that consumers with high technological readiness are more receptive to cutting-edge technological changes and service experiences [38]. Kim pointed out that technology readiness is high, that is, users with rich technical experience and innovative spirit will be the first to use technology services and experience service value [39]. In short, the level of technological readiness leads to differences in patients’ attitudes and experiences when receiving digital medical services. Patients with high technological readiness have a more positive evaluation of new service models in the smart service experience and feel the sense of control brought by information technology. Thus, we propose:
Hypothesis 5:
Technical readiness positively mediates the relationship between the digital transformation of medical services and patient psychological empowerment.
Methods
Sample and procedure
Research methods and sample design
The current study employed confirmatory factor analysis and structural equation modeling as the primary statistical methods for data analysis. As indicated in the extant literature, the sample size for structural equation modeling should generally not be less than 200 cases [40]. Additionally, it is recommended that the sample size should be at least 10 times the number of observed variables [41], or the ratio of the same size to the number of free parameters in the model should be exceed 10 [42], in order to achieve optimal model fit and more reliable results. In this study, 4 concepts were examined, with an estimated total of 20 observed variables. Considering the aforementioned guidelines and the effectiveness of the questionnaire, the sample size was ultimately determined to be 350.
The sample adopts a combination of quota sampling and convenience sampling methods, allocating quotas according to three indicators: region, gender, and age. The regions were divided into the eastern, central, western, and northeastern parts; Gender is divided into male and female; The age range is 18–29 years old, 30–39 years old, 40–49 years old, and over 50 years old. Due to the lack of authoritative data on the region, age, and gender of medical service digital user groups, this study referred to the China Census Yearbook 2020 and the Report on China’s Internet Medical Industry Market Panorama Assessment and Development Strategy Planning 2022–2027 to determine the proportion of each control item in the sample. (1) Regional proportion. According to the data of the number of households, population and gender ratio in 1.1 of the China Census Yearbook 2020, the population of the eastern, central, western and northeastern regions accounted for 40%, 25.9%, 27.2% and 7% of the total population. In combination with the characteristics of this study, the team discussed and adjusted the proportion of regions in the sample to 50%, 20%, 20% and 10%. (2) Gender proportion. According to the gender ratio of men and women across the country in the number of households, population and gender ratio in 1.1 of China Census Yearbook 2020, the overall gender ratio of men and women across the country (women = 100) is 104.80. The Report on the Market Panorama Assessment and Development Strategy Planning of China’s Internet Medical Industry 2022–2027 pointed out that among the user groups of China’s Internet medical industry, men are slightly higher than women, accounting for 54.7% and 45.3% respectively. Combining the feasibility of operation, quota sampling was carried out with a ratio of men to women of 1:1 in terms of gender. (3) Age proportion. According to 1.5 in the China Census Yearbook 2020, the population aged 18–29, 30–39, 40–49, and over 50 in all regions by age and sex accounted for 19%, 20%, 18%, and 43%. The proportion of the first three age groups is about 1:1:1. In combination with the Report on the Overall Market Assessment and Development Strategy Planning of China’s Internet Medical Industry 2022–2027, it showed that the users of digital medical services are mainly young and middle-aged people, while the users over 45 account for only 5.2%. Based on the above data and considering the characteristics of the research subjects, the research team ultimately decided to reduce the sample size of those over 50 years old, while maintaining the proportion of the first three age groups at approximately 1:1:1. Finally, the proportion of each age group was set at 30%, 30%, 30%, and 10% (Table 1).
Table 1.
Sample quota distribution table
| Region | Eastern | Central | Western | Northeastern | Age proportion | Sample unit | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Gender | Male | Female | Male | Female | Male | Female | Male | Female | |||
| Gender proportion | 50% | 50% | 50% | 50% | 50% | 50% | 50% | 50% | |||
| Age | 18–29 | 26 | 26 | 11 | 11 | 11 | 11 | 5 | 5 | 30% | 105 |
| 30–39 | 26 | 26 | 11 | 11 | 11 | 11 | 5 | 5 | 30% | 105 | |
| 40–49 | 26 | 26 | 11 | 11 | 11 | 11 | 5 | 5 | 30% | 105 | |
| > 50 | 10 | 9 | 2 | 2 | 2 | 2 | 3 | 2 | 10% | 35 | |
| Sample unit | 88 | 87 | 35 | 35 | 35 | 35 | 18 | 17 | |||
| Regional proportion | 50% | 20% | 20% | 10% | 100% | ||||||
| Sample unit | 175 | 70 | 70 | 35 | 350 | ||||||
Data collection and processing
The research collected data through an online questionnaire star method, and the survey period was from January 15th to February 16th, 2023. The survey targeted users from different regions, genders, and age groups across the country who have recently experienced digital healthcare services. A total of 420 questionnaires were collected, and 72 invalid questionnaires (including random answers, logical inconsistencies, and multidimensional scale issues) were excluded. 348 valid questionnaires were obtained, with a recovery rate of 82.86%. (Due to the exclusion of one sample each from males and females aged 30–39 in the central region who did not meet the requirements, the final number of valid samples decreased by two compared to the predetermined quota). This study was approved by the Research Ethics Review Committee of Beijing University of Chinese Medicine.
Measures
Demographic characteristics
Gender, age, educational level, type of occupation, insurance types, monthly income, and area were all recorded.
Digital transformation of medical services
The measurement of the degree of digital transformation in medical services is designed based on the Guttman scale [43]. This is a single dimensional scale arranged in logical order, used to measure specific concepts or things. The scale itself has a certain logical relationship in structure, presenting an order from weak to strong or from strong to weak. When participants agree with a statement during the survey, they usually also agree with other statements before (or after) that statement.
The aim of this study is to analyze the impact of digital transformation of medical services on patient satisfaction. As the degree of digitization gradually increases, from online auxiliary queries of hospital doctor information, online registration, etc. (low degree of digitization), to online payment, examination reports, etc. (further increased degree of digitization), to remote diagnosis and treatment, chronic disease follow-up, etc. (high degree of digitization), this study explores whether the patient experience and satisfaction will correspondingly improve. The research divides the digital transformation of medical services into five levels, from low to high: level 1 (whether you have learned hospital information through mobile APP, official account, website, etc., such as hospital address, expert visits, department characteristics, etc.), level 2 (whether you have used online appointment registration, online consultation and other services), level 3 (whether you have used mobile terminal to query inspection report), level 4 (whether you have used mobile payment and other services), and level 5 (whether you have used online re diagnosis, remote diagnosis and treatment and other services). Quantify the number of items agreed by the subjects through a scale to determine the degree of digital transformation. Each question is assigned a value of 1, with 1 point for “yes” and 0 point for “no”. All negative samples are excluded, and the range of values is 1–5. The higher the score, the higher the degree of digital transformation.
Patient psychological empowerment
The Patient Psychological Empowerment Scale, developed by Spreitzer [44] and subsequently adapted for the Chinese population by scholar Han [45], was set up with the theme of the present study to reflect patients’ perceptions of the right to choose, the right to make autonomous decisions, and self-efficacy with five question items: “With the improvement of digitalization in medical services, I am able to make appointments, registrations, online payments, etc. through devices such as mobile phones and self-service machines, making my medical process more efficient and convenient”, “I can decide for myself whether to use the digital medical services provided by the hospital during the medical treatment process”, “I have great independence and autonomy in deciding when, where, and how to use digital healthcare services”, “The digital medical services provided by the hospital allow me to choose and experience different services (such as online appointment, mobile payment, online consultation, online medical treatment.)”, “The digital medical services provided by the hospital enable me to choose different ways to receive medical services (such as completing registration, payment, medical consultation, and information inquiry online)”. A five-point Likert scale was used to set up the answer items and assign values from 1–5. The mean score of the question items is the score of the patient’s psychological empowerment, and the higher the score represents the higher level of the patient’s psychological empowerment.
Patient satisfaction
Patient satisfaction was measured with reference to the Patient Satisfaction Scale (PSPSQ 2.0) developed by Prashant Sakharkar et al. [46]. A single dimension was set up with four question items: “I am satisfied with the digital medical services provided by the hospital”, “Compared to traditional medical service models, digital medical services make me more satisfied”, “Using digital medical services during the medical process is a wise choice”, “If necessary, I will continue to use digital medical services during my next visit to the hospital”. A five-point Likert scale was used to set up the response items and assign values from 1 to 5. The mean score of the question items is the score of patient satisfaction, and the higher the score represents the higher level of patient satisfaction.
Technology readiness
The questions for measuring technology readiness refer to the Technology Readiness Inventory (TRI2.0) developed by Parasuraman & Colby (2015). A single dimension was set up with five questions: “Technology brings convenience to your life”, “Technology has improved your efficiency in life”, “New technologies can improve people’s quality of life”, “Among the people around me, I am always the one who tries to use new information technology earlier”, “If I hear about the emergence of new information technology, I will experience it through various channels”. A five-point Likert scale was used to set up the response items and assign values from 1 to 5. The mean score of the question items is the score of technology readiness, and the higher the score represents the higher level of technology readiness of the patients.
Statistical analysis
To study the effects of digital transformation of medical services and patient satisfaction, we built a structural equation model (SEM). This model had the function of aiding hypothesis testing, whereby the interconnections of variables in complex causal relationships are subjected to analysis. Data were analyzed using descriptive statistics and structural equation modelling (SEM). The analysis was performed using the statistical package SPSS v26.0 with IBM AMOS v23.0 (IBM, Armonk, NY, USA) and the Andrew F. Hayes PROCESS macro.
Firstly, descriptive statistics such as frequency, mean (M), standard deviation (SD), absolute skewness (g1 < 2), and absolute kurtosis (g2 < 7). Secondly, to test the reliability and validity of the variables, we calculated Cronbach’s alpha, composite reliability (CR), and extracted mean variance (AVE). Thirdly, we calculated Pearson correlation coefficients to determine the relationship between variables. Finally, structural equation modeling (SEM) was used to examine the direct and mediating effects of potential predictive factors on outcome variables to test hypotheses. The model fitting index evaluation indicators used in this study are as follows: Tucker Lewis index (TLI) >0.90, incremental fitting index (IFI) >0.90, goodness fit index (GFI) >0.90, normative fitting index (NFI) >0.90, comparative fitting index (CFI) >0.90, root mean square error of approximation (RMSEA) < 0.08, and chi square/degree of freedom ratio (CMIN/DF) < 3.0 [47]. A mediation effect test was carried out with 5000 Bootstrap samples. For all analyses, the significance level was defined as a p-value < 0.05 [48].
Results
Participant characteristics
Table 2 shows the sociodemographic characteristics of the study participants. Among the 348 subjects, the youth group (18–29 years old) had the highest number of 107 cases (30.7%), the middle-aged group (30–39 years old) and the middle-aged and elderly group (40–49 years old) each had 104 cases (29.9%), and there were 33 cases (9.5%) aged over 50 years old. It basically reflects the age status of China’s population. In terms of gender, there were 174 males (50.0%) and 174 females (50.0%); In terms of regional distribution, there were 175 cases (50.3%) in the eastern region, 67 cases (19.3%) in the central region, 69 cases (19.8%) in the western region, and 37 cases (10.6%) in the northeastern region; In terms of insurance types, there were 28 uninsured personnel (8.0%), 161 urban employee medical insurance personnel (46.3%), 133 urban and rural resident medical insurance personnel (38.2%), and 26 public medical insurance personnel (7.5%). In terms of monthly income, there were 119 people (34.2%) with monthly income below 2000, 97 people (27.9%) with monthly income between 2000 and 4999, 95 people (27.3%) with monthly income between 5000 and 9999, and 37 people (10.6%) with monthly income above 10,000; In terms of educational level, 97 people (27.9%) have a postgraduate degree or above, 185 people (53.1%) have a bachelor’s degree, 26 people (7.5%) have a college degree, and 40 people (11.5%) have a high school degree or below.
Table 2.
Demographic characteristics of the participants (N = 348)
| Frequency | Percentage | ||
|---|---|---|---|
| Gender | Male | 174 | 50.0 |
| Female | 174 | 50.0 | |
| Age | 18–29 | 107 | 30.7 |
| 30–39 | 104 | 29.9 | |
| 40–49 | 104 | 29.9 | |
| >50 | 33 | 9.5 | |
| Educational level | Master degree and above | 97 | 27.9 |
| Bachelor | 185 | 53.1 | |
| Junior college | 26 | 7.5 | |
| High school and below | 40 | 11.5 | |
| Type of occupation | Enterprise personnel | 22 | 6.3 |
| Civil servants or public institutions | 79 | 22.7 | |
| Teacher | 51 | 14.7 | |
| Full-time student | 109 | 31.3 | |
| Other | 87 | 25.0 | |
| Insurance types | Urban employee medical insurance | 161 | 46.3 |
| Urban and rural residents’ medical insurance | 133 | 38.2 | |
| Free medical care | 26 | 7.5 | |
| Other | 28 | 8.0 | |
| Monthly income(Yuan) | <2000 | 119 | 34.2 |
| 2000–4999 | 97 | 27.9 | |
| 5000–9999 | 95 | 27.3 | |
| >=10,000 | 37 | 10.6 | |
| Area | East | 175 | 50.3 |
| Central | 67 | 19.3 | |
| West | 69 | 19.8 | |
| Northeast | 37 | 10.6 | |
| Total | 348 | 100.0 |
Reliability analysis
Table 3 shows the reliability analysis of the survey questionnaire. SPSS 26.0 was used to conduct reliability analysis on three scales: patient psychological empowerment, patient satisfaction, and technical readiness. The results showed that the Cronbach’s alpha coefficients of the three scales were all greater than 0.8, which were 0.898, 0.850, and 0.876, respectively, indicating high internal consistency among the scales and good questionnaire reliability.
Table 3.
Reliability analysis of survey questionnaire
| Dimension | Question items | Cronbach’s alpha coefficient |
|---|---|---|
| Patient psychological empowerment | A1 With the improvement of digitalization in medical services, I am able to make appointments, registrations, online payments, etc. through devices such as mobile phones and self-service machines, making my medical process more efficient and convenient | 0.898 |
| A2 I can decide for myself whether to use the digital medical services provided by the hospital during the medical treatment process | ||
| A3 I have great independence and autonomy in deciding when, where, and how to use digital healthcare services | ||
| A4 The digital medical services provided by the hospital allow me to choose and experience different services (such as online appointment, mobile payment, online consultation, online medical treatment.) | ||
| A5 The digital medical services provided by the hospital enable me to choose different ways to receive medical services (such as completing registration, payment, medical consultation, and information inquiry online) | ||
| Patient satisfaction | B1 I am satisfied with the digital medical services provided by the hospital | 0.850 |
| B2 Compared to traditional medical service models, digital medical services make me more satisfied | ||
| B3 Using digital medical services during the medical process is a wise choice | ||
| B4 If necessary, I will continue to use digital medical services during my next visit to the hospital | ||
| Technology readiness | C1 Technology brings convenience to your life | 0.876 |
| C2 Technology has improved your efficiency in life | ||
| C3 New technologies can improve people’s quality of life | ||
| C4 Among the people around me, I am always the one who tries to use new information technology earlier | ||
| C5 If I hear about the emergence of new information technology, I will experience it through various channels |
Validity analysis
Structural validity
The AMOS23.0 software was used to perform confirmatory factor analysis on the data of three scales, and the results are shown in Table 4.
Table 4.
Overall fitting coefficients of the measurement model
| X2/df | RMSEA | GFI | AGFI | CFI | IFI | TLI |
|---|---|---|---|---|---|---|
| 1.745 | 0.046 | 0.951 | 0.931 | 0.979 | 0.979 | 0.974 |
According to Table 3, the value of X2/df is 1.745, which is less than 3 and ideal for adaptation; RMSEA is 0.046, less than 0.06, which is ideal for adaptation; The values of GFI, AGFI, CFI, IFI, TLI and other indicators are 0.951, 0.931, 0.979, 0.979, and 0.974, respectively, all of which are greater than 0.9. The results are well adapted and meet reasonable standards. Overall, the overall model of patient psychological empowerment, patient satisfaction, and technology readiness is well adapted.
Convergent validity
Convergent validity is used to evaluate the degree of correlation between questionnaire indicators and their corresponding dimensions or constructs, as well as the internal consistency of the questions within that dimension. Specifically, factor loadings are obtained through confirmatory factor analysis, and combined reliability (CR) and average variance extraction (AVE) are calculated to measure them.
CR is used to measure the internal consistency of each item in latent variables. According to Fornell’s criteria, a CR value greater than 0.7 indicates good reliability, while a CR value greater than 0.3 indicates acceptability. In this study, the CR values for patient psychological empowerment, patient satisfaction, and technology readiness were 0.898, 0.854, and 0.876, respectively, all of which were higher than 0.7, indicating good reliability in the composition of these latent variables.
AVE is used to measure the average explanatory power of latent variables on a topic. According to Fornell’s criteria, an AVE value greater than 0.5 indicates a strong explanatory power of the latent variable for the problem, while a value greater than 0.36 indicates acceptability. In this study, the AVE values of patient psychological empowerment, patient satisfaction, and technology readiness were 0.639, 0.596, and 0.587, respectively, all of which were higher than 0.5, indicating that these latent variables have a strong average explanatory power for the questions.
From the perspective of CR and AVE, the AVE values of each latent variable were all greater than 0.5, and the CR values are all greater than 0.8, indicating good convergent validity and meeting the requirements of this study (Table 5).
Table 5.
Factor loading
| Path | Estimate | AVE | CR | ||
|---|---|---|---|---|---|
| SQ1 | <--- | Patient psychological empowerment | 0.792 | 0.6387 | 0.8982 |
| SQ2 | <--- | Patient psychological empowerment | 0.796 | ||
| SQ3 | <--- | Patient psychological empowerment | 0.761 | ||
| SQ4 | <--- | Patient psychological empowerment | 0.864 | ||
| SQ5 | <--- | Patient psychological empowerment | 0.779 | ||
| MY1 | <--- | Patient satisfaction | 0.690 | 0.5958 | 0.854 |
| MY2 | <--- | Patient satisfaction | 0.774 | ||
| MY3 | <--- | Patient satisfaction | 0.877 | ||
| MY4 | <--- | Patient satisfaction | 0.734 | ||
| JS1 | <--- | Technology readiness | 0.746 | ||
| JS2 | <--- | Technology readiness | 0.820 | 0.5869 | 0.8764 |
| JS3 | <--- | Technology readiness | 0.725 | ||
| JS4 | <--- | Technology readiness | 0.783 | ||
| JS5 | <--- | Technology readiness | 0.753 | ||
Descriptive statistical results
Table 6 presents the results of descriptive statistics and normality tests. The results showed that the mean scores of each variable ranged from 3 to 4.5, with the mean score of digitalization of medical services was (4.110 ± 0.962), the mean score of patient psychological empowerment was (3.567 ± 0.932), the mean score of patient satisfaction was (3.846 ± 0.836), and the mean score of technology readiness was (3.429 ± 0.974). According to the positive score of 1–5, the research subjects’ understanding and behavior levels in digitalization of medical services, psychological empowerment, and other aspects were above a moderate level. If the skewness and kurtosis of each measurement item are tested, and the absolute value of the skewness coefficient is within 3 and the absolute value of the kurtosis coefficient is within 8, it can be considered to satisfy an approximate normal distribution. According to the analysis results, the absolute values of skewness and kurtosis coefficients of each measurement item in this study were within the standard range, thus satisfying an approximate normal distribution.
Table 6.
Descriptive statistical results of each core variable
| Variable | Average | Standard deviation | Skewness coefficient | Kurtosis coefficient |
|---|---|---|---|---|
| Digitalization of medical services | 4.110 | 0.962 | -1.309 | 1.721 |
| Patient psychological empowerment | 3.567 | 0.932 | -0.715 | -0.384 |
| Patient satisfaction | 3.846 | 0.836 | -0.711 | 0.004 |
| Technology readiness | 3.429 | 0.974 | -0.47 | -0.849 |
The study performed partial correlation analysis on digitalization of medical services, controlling for variables such as gender, age, education, occupation, region, basic medical insurance type, and monthly income. The results showed that, in addition to technology readiness, digitalization of medical services was significantly positively correlated with patient psychological empowerment and patient satisfaction (P < 0.05), and there was also a significant positive correlation between patient psychological empowerment, patient satisfaction, and technology readiness (P < 0.05) (Table 7).
Table 7.
Variable correlation
| Variable | M | SD | Digitalization of medical services | Patient psychological empowerment | Patient satisfaction | Technology readiness |
|---|---|---|---|---|---|---|
| Digitalization of medical services | 4.110 | 0.962 | 1 | |||
| Patient psychological empowerment | 3.567 | 0.932 | 0.452** | 1 | ||
| Patient satisfaction | 3.846 | 0.836 | 0.418** | 0.509** | 1 | |
| Technology readiness | 3.429 | 0.974 | -0.032 | 0.313** | 0.157** | 1 |
Note: *P < 0.05, **P < 0.01
Structural model validation
The theoretical model constructed by fitting with AMOS23.0 software was used to estimate the path coefficients using the input sample data. The results (Table 8) showed a chi square value of 68.627 (p=. 000), degrees of freedom of 33, and a chi square degree of freedom ratio CMIN/DF of 2.080, which was less than the critical value of 3, indicating a good fit between the model and the data. The root mean square error of approximation (RMSEA) is 0.056, which was less than the recommended value of 0.08, and the fitting index CFI was 0.980, IFI = 0.981, NFI = 0.963, GFI = 0.966, AGFI = 0.943, All were greater than the recommended value of 0.9. Overall, the values of all indicators were within the allowable range, indicating a good fit of the model.
Table 8.
Model confirmatory factor analysis
| Index | CMIN/DF | GFI | AGFI | CFI | NFI | IFI | RMSEA |
|---|---|---|---|---|---|---|---|
| Indicator value | 2.080 | 0.966 | 0.943 | 0.980 | 0.963 | 0.981 | 0.056 |
| Fitting effect | Better | Better | Better | Better | Better | Better | Acceptable |
| Reference value |
1 ~ 3(Better) <5(Acceptable) |
>0.9 | >0.85 | >0.9 | >0.9 | >0.9 |
<0.05 (Better) <0.08 (Acceptable) |
Path coefficient analysis
The path coefficient results showed that digitalization of medical services had a positive impact on patient satisfaction, with a standardized coefficient of 0.19, C.R.=3.272, P<0.05, statistically significant, assuming H1 held true; The digitization of medical services had a positive impact on patients’ psychological empowerment, with a standardized coefficient of 0.505, C.R.=9.445, P<0.05, statistically significant, assuming H2 held true; Patient psychological empowerment has a positive impact on patient satisfaction, with a standardized coefficient of 0.485, C.R.=7.186, P<0.05, statistically significant, assuming H3 held true, as shown in Table 9; Fig. 2.
Table 9.
Path test
| Path | Estimate | S.Estimate | S.E | C.R. | P-value |
|---|---|---|---|---|---|
| Digitalization of medical services→patient satisfaction | 0.147 | 0.189 | 0.045 | 3.272 | 0.001 |
| Digitalization of medical services→patient psychological empowerment | 0.473 | 0.505 | 0.050 | 9.445 | *** |
| Patient psychological empowerment→patient satisfaction | 0.404 | 0.485 | 0.056 | 7.186 | *** |
Fig. 2.
Path coefficient diagram of the model
Intermediary effect test
Using gender, age, education level, occupation, region, basic medical insurance type, and monthly income as control variables, digitalization of medical services as independent variable, and patient satisfaction as dependent variable, the mediating effect of patient psychological empowerment was tested using Model 4 in SPSS. The results are shown in Table 10. The digitalization of medical services had a significant positive impact on patient satisfaction (B = 0.387, t = 8.476, P < 0.01). After introducing the mediating variable of patient psychological empowerment, the positive impact remained significant (B = 0.219, t = 4.650, p < 0.01). Moreover, digitalization of medical services had a significant positive impact on patient psychological empowerment (B = 0.447, t = 9.266, p < 0.01) and patient satisfaction (B = 0.366, t = 7.896, p < 0.01). In addition, the bootstrap method was further tested using 5000 random samples, and the 95% confidence interval of the bootstrap did not include 0 (Table 11), indicating that digitalization of medical services can both intuitively predict patient satisfaction and predict patient satisfaction through the mediating effect of patient psychological empowerment. Hypothesis 4 was valid. The direct effect (0.219) and mediating effect (0.168) account for 57% and 43% of the total effect (0.387), respectively.
Table 10.
Mediation model test
| Regression equation(N = 348) | Fitting indicators | Coefficient significance | ||||
|---|---|---|---|---|---|---|
| Outcome | Predictive variables | R | R 2 | F | B | t |
| Patient satisfaction | 0.440 | 0.194 | 10.199*** | |||
| Gender | 0.092 | 1.152 | ||||
| Age | 0.092 | 1.125 | ||||
| Educational background | 0.080 | 1.482 | ||||
| Career | -0.029 | -1.485 | ||||
| Area | 0.008 | 0.204 | ||||
| Basic medical insurance types | -0.026 | -0.468 | ||||
| Monthly income | -0.059 | -1.262 | ||||
| Digitalization of medical services | 0.387 | 8.476*** | ||||
| Patient psychological empowerment | 0.459 | 0.247 | 13.867*** | |||
| Gender | 0.055 | 0.626 | ||||
| Age | -0.018 | -0.329 | ||||
| Educational background | 0.027 | 0.470 | ||||
| Career | -0.031 | -1.442 | ||||
| Area | -0.039 | -0.892 | ||||
| Basic medical insurance types | 0.046 | 0.770 | ||||
| Monthly income | -0.102 | -2.028* | ||||
| Digitalization of medical services | 0.447 | 9.266*** | ||||
| Patient satisfaction | 0.565 | 0.320 | 17.634*** | |||
| Gender | 0.072 | 0.954 | ||||
| Age | 0.082 | 1.803 | ||||
| Educational background | 0.070 | 1.409 | ||||
| Career | -0.018 | -0.993 | ||||
| Area | 0.023 | 0.604 | ||||
| Basic medical insurance types | -0.043 | -0.838 | ||||
| Monthly income | -0.022 | -0.498 | ||||
| Digitalization of medical services | 0.219 | 4.650*** | ||||
| Patient psychological empowerment | 0.366 | 7.896*** | ||||
Note: *P < 0.05, **P < 0.01, *** P < 0.001, all variables are bilateral, and standardized variables are used to input them into the regression equation
Table 11.
Decomposition table of total effect, direct effect, and mediating effect
| Effect | Effect value | Boot standard error | Boot CI lower limit | Boot CI upper limit | Relative effect value |
|---|---|---|---|---|---|
| The overall effect of digital transformation of medical services on patient satisfaction | 0.387 | 0.045 | 0.299 | 0.476 | |
| direct effect | 0.219 | 0.050 | 0.124 | 0.316 | 57% |
| The mediating effect of patient psychological empowerment | 0.168 | 0.031 | 0.113 | 0.232 | 43% |
Moderation effect test
We conducted a test using Model 7 in SPSS, with gender, age, education, occupation, region, basic medical insurance type, and monthly income as control variables; medical service digitalization as independent variables; patient satisfaction as the dependent variable; patient psychological empowerment as the mediating variable; and technology readiness as the moderating variable. The results showed that the product term of digitalization of medical services and technological readiness had a significant positive impact on patients’ psychological empowerment (B = 0.192, t = 4.477, p < 0.01) (Table 12), indicating that technological readiness played a moderating role in the impact of digitalization of medical services on patients’ psychological empowerment. That is, the higher the technological readiness was, the stronger the impact of digitalization of medical services on patients’ psychological empowerment was. Hypothesis 5 held true.
Table 12.
Moderated mediation model test
| Regression equation(N = 348) | Fitting indicators | Coefficient significance | ||||
|---|---|---|---|---|---|---|
| Outcome | Predictive variables | R | R 2 | F | B | t |
| patient psychological empowerment | 0.621 | 0.385 | 21.102*** | |||
| Gender | 0.042 | 0.526 | ||||
| Age | -0.022 | -0.455 | ||||
| Educational background | 0.032 | 0.599 | ||||
| Career | -0.033 | -1.720 | ||||
| Area | -0.031 | -0.763 | ||||
| Basic medical insurance types | 0.051 | 0.944 | ||||
| Monthly income | -0.090 | -1.967 | ||||
| Digitalization of medical services | 0.458 | 10.257*** | ||||
| Technology readiness | 0.300 | 7.300*** | ||||
| Digitalization of medical services*Technology readiness | 0.192 | 4.477*** | ||||
Further simple slope analysis was conducted to plot the relationship between digitalization of medical services and patient psychological empowerment at different levels of technology readiness, with M ± 1SD as the grouping variable [49] (Fig. 3). The results showed that at low levels of technological readiness (M-1SD), the positive impact of digitalization of medical services on patient psychological empowerment was relatively small (t = 4.321, P < 0.001), while at high levels of technological readiness (M + 1SD), the impact was greater (t = 10.864, P < 0.001) (Table 13), indicating that technological readiness plays a positive moderating role in the impact of digitalization of medical services on patient psychological empowerment. As individual technological readiness increased, the predictive effect of digitalization of medical services on patient psychological empowerment gradually increased. In addition, at the three levels of technological readiness, the mediating effect of patient psychological empowerment on the relationship between digitalization of medical services and patient satisfaction was gradually increasing. That is, as the level of technological readiness improved, digitalization of medical services was more likely to enhance patient psychological perception and improve medical satisfaction.
Fig. 3.
The moderating effect of technological readiness on the relationship between digitalization of medical services and patient psychological empowerment
Table 13.
Mediating effects on different levels of technological readiness
| Intermediary Path | Technology readiness | Effect value | Boot standard error | Boot CI lower limit | Boot CI upper limit |
|---|---|---|---|---|---|
| Patient psychological empowerment | low | 0.099 | 0.032 | 0.043 | 0.165 |
| high | 0.236 | 0.040 | 0.161 | 0.315 |
Discussion
The results of this study are consistent with those of Wang [50], Li [51], and others, verifying that digitalization of medical services not only improved service efficiency and quality but also significantly enhanced patient satisfaction. With the advancement of digital transformation, medical services had gradually become intelligent and convenient, greatly simplified medical procedures, improved service efficiency and quality, and brought new medical experiences to patients, from online appointment registration and consultation, to online payment, drug purchase, report query, and then to remote follow-up and Internet medicine. In addition, the results of this study are consistent with Len et al.’s research, which found that psychological empowerment had a significant impact on customer satisfaction. However, unlike previous studies that mainly focused on improving service quality, this study further reveals the psychological mechanism between digital transformation and patient satisfaction.
The study designed and measured the degree of digital transformation of medical services based on the Guttman scale, and empirically tested the relationship between digital transformation of medical services and patient satisfaction. It was found that digital transformation of medical services had a positive effect on patient satisfaction, that is, the digitalization of medical services improved patient satisfaction and service quality. Meanwhile, the research found that the digital transformation of medical services not only directly affected patient satisfaction but also had an impact on patient satisfaction through psychological empowerment, thus improving the mechanism of the impact of digital transformation of medical services on patient satisfaction. On the one hand, the research showed that the higher the degree of digital transformation of medical services, the higher the psychological empowerment perception of patients. Digital medical services promoted patients to feel a sense of control when receiving medical services, gained more autonomy in decision-making, and thus chose the desired medical services based on actual needs, enhanced patients’ perception of choice and improved their psychological empowerment. On the other hand, the research showed that the higher the level of psychological empowerment of patients was, the higher their satisfaction was. In this study, patient psychological empowerment could be seen as the psychological perception of digital medical services during the medical process. Based on customer empowerment theory, when patients had a sense of control over the medical process, digital medical services gave them greater autonomy and enhanced their perception of decision-making power towards medical services or products, thereby improving medical satisfaction.
In addition, different patients might have different perceptions and evaluations of the same medical service [52]. The difference in patients’ own technical readiness led to different perceptions of digital healthcare services. For patients with high technological readiness, they had a high level of innovation and an optimistic attitude towards new technologies. They had a more positive service experience and evaluation towards the transformation of service models caused by the digital transformation of medical services, and understood the convenience that digital services bring to the medical process. Therefore, when they accepted the empowerment measures granted by digital medical services, it was more likely to trigger a sense of psychological empowerment, increased the degree of psychological empowerment, and gained a sense of control over the service process. On the contrary, patients with low technological readiness tended to have a more conservative attitude towards new technologies and had more concerns during their use. The authorization measures brought about by the digital transformation of medical services could easily increase the burden on patients, making them unable to choose from multiple service modes and worried about information leakage, resulting in a lower sense of psychological empowerment and weaker sense of control over services.
Limitations
This research has some limitations. Firstly, due to the limitations of time, funding, and data availability, the quota sampling method used in the current study is deficient in science, which is mainly manifested in the low percentage of elderly samples. However, the reality is that the elderly are the core group of people who seek medical care, and with the development of Internet healthcare, future research in this area is expected to improve in terms of funding and data availability, thus promoting a more scientific and rational sampling scheme design. Secondly, although this study utilized the Guttman scale for the design of the degree of digital transformation of healthcare services and verified its good unidimensionality, there may still be certain measurement validity issues. Therefore, future research could develop a dedicated scale for digital transformation in healthcare to improve the reliability of the study and the applicability of the scale. Third, this study did not investigate the dimensionality of patients’ psychological empowerment and technology readiness, and future research could further analyze the interaction between the variables in this study under different dimensions, so as to enrich the connotation of the relevant research. Fourth, medical service is an important area of national economy and people’s livelihood, and also a key link in the construction of digital China. In addition to focusing on the enhancement of patient experience through digital healthcare services from the perspective of patients, the fairness and accessibility of digital healthcare services can be further explored from the patient level; the operational efficiency of medical institutions can be paid attention to from their perspective; and the role of stakeholders in the process (such as healthcare insurance off-card payment, public value creation, and so on) can be focused on from the perspective of public management. Overall, although this study has certain limitations, it has laid a solid foundation for exploring the digital transformation of medical services in the future.
Conclusions
In conclusion, the findings of our study substantiate the impact mechanism of the digital transformation of medical services on patient satisfaction, providing practical guidance and management insights for medical institutions to carry out digital transformation and upgrading in the context of digital transformation. Firstly, the research results indicate that the digital transformation of medical services can significantly improve patient satisfaction. Therefore, medical institutions should accurately explore patient needs, promote digital construction, establish evaluation systems and feedback mechanisms, dynamically adjust service content, develop convenient and efficient digital services, and enhance patient experience. Secondly, the study revealed the mediating role of patient psychological empowerment between digital transformation and satisfaction, and suggested that medical institutions enhance patients’ perception of psychological empowerment, autonomy, and satisfaction through diversified services and personalized feedback. In addition, the research suggests that it is necessary to accurately identify the target patient group and adopt differentiated service measures for patients with different levels of technological readiness, in order to reduce the resistance of patients with low technological readiness to digital services. These findings provide a theoretical basis and practical path for medical institutions to optimize service models, improve management efficiency and patient satisfaction, and also provide a reference for health policy makers, helping to promote the digital transformation and high-quality development of medical services.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
We would like to thank all subjects who participated in the study.
Abbreviations
- CPES
Customer psychological empowerment scale
- PSS
Patient satisfaction scale
- TRS
Technology readiness scale
- CMIN
Chi-square minimum
- DF
Degrees of freedom
- RMSEA
Root Mean square error of approximation
- GFI
Goodness of fit index
- AGFI
Adjusted goodness of fit index
- CFI
Comparative fit index
- NFI
Normed fit index
- IFI
Incremental fit index
- TLI
Tucker-lewis index
- M
Mean
- SD
Standard deviation
- CR
Composite reliability
- AVE
Average variance extracted
Author contributions
Guan-shuang Zhou, Sheng-hui Shi, and Qiu-ying Zheng contributed to the study design. Guan-shuang Zhou and Sheng-hui Shi contributed equally to this manuscript, including data analysis and manuscript drafting. Guan-shuang Zhou, Sheng-hui Shi, Qian-rui Qian, and Qiu-ying Zheng contributed to data collection, organization, and statistical processing. All authors had read and approved the final manuscript.
Funding
The paper was supported by a grant from the Beijing Municipal Industrial Development Research Institute (CYYJY-2024-032).
Data availability
The are available from corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
All methods in this study were conducted in accordance with the principles stated in the Declaration of Helsinki. This study received ethical clearance from Ethics Committee of Beijing university of Chinese medicine. Consent was directly obtained from the participants, and all participants signed an informed consent form.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Sheng-hui Shi, Email: bucmssh1024@163.com.
Qiu-ying Zheng, Email: zhengqy@bucm.edu.cn.
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Associated Data
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Supplementary Materials
Data Availability Statement
The are available from corresponding author upon reasonable request.