Research on the Current Situation of Smart Services in Shanghai Internet Hospitals and User Behavior—An Integrated Analysis Based on Andersen Model and Technology Acceptance Model

Research on the Current Situation of Smart Services in Shanghai Internet Hospitals and User Behavior—An Integrated Analysis Based on Andersen Model and Technology Acceptance Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Research on the Current Situation of Smart Services in Shanghai Internet Hospitals and User Behavior—An Integrated Analysis Based on Andersen Model and Technology Acceptance Model Rong Huang, Tingting Li, Jingliu Huang, Tao Jiang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8787379/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Objectives: This study aims to assess the development of smart services in Shanghai's Internet hospitals and analyze the factors influencing user behavior by integrating the Andersen Health Service Model and the Technology Acceptance Model (TAM), with a focus on addressing service accessibility and health equity. Methods: A mixed-methods approach was employed, combining a cross-sectional survey of 1,028 valid questionnaires collected via multi-stage stratified sampling with service data analysis of 117 Internet hospitals in Shanghai. Statistical analyses, including chi-square tests, K-means cluster analysis, association rule mining (Apriori algorithm), and ordinal logistic regression, were conducted using SPSS 25.0 and Python. Results: Shanghai's Internet hospital system has achieved full coverage across 37 municipal-level hospitals, with 13.32 million registered users and 149 million cumulative service instances. However, significant usage disparities exist: the elderly population (over 61 years) exhibited significantly lower usage rates and satisfaction compared to younger groups (P<0.01). Cluster analysis identified three distinct user typologies: high-demand and high-adaptability (32.1%), passive usage (46.7%), and low-resource and high-barrier (21.2%). Association rule mining revealed that the service combination of "intelligent pre-consultation + electronic medical record card + online follow-up consultation" was the most preferred (confidence >85%). Key influencing factors included education level, income, chronic disease status, perceived usefulness (promoting factors), and advanced age, rural residence, and low perceived ease of use (hindering factors). Conclusion: While Shanghai has established a robust Internet hospital infrastructure, challenges related to the digital divide, particularly for elderly and low-resource groups, and a mismatch between service supply and demand persist. To potentially enhance health equity and service sustainability, targeted interventions such as aging-friendly technological transformations could be considered. This study provides evidence to inform policy innovation in online medical insurance payment, optimization of integrated service supply models, and strengthening of quality supervision frameworks. Internet hospital Andersen Model Technology Acceptance Model smart healthcare health equity Figures Figure 1 1 Introduction Driven by the global digital transformation wave and the normalization of COVID-19 prevention and control, Internet hospitals, as a new carrier of medical and health services, are profoundly changing the traditional medical service model. As one of the cities with the most abundant medical resources and the highest informatization level in China, Shanghai took the lead in promoting the construction of Internet hospitals. By 2024, the Shanghai Municipal Hospital Internet Platform has connected 37 municipal-level hospitals, with more than 13.32 million registered users and a cumulative service volume of 149 million person-times, providing more than 40,000 online services per day. [1,2]These figures reflect residents' urgent demand for convenient medical services and also demonstrate the important role of Internet hospitals in optimizing the allocation of medical resources. At the policy level, the national "14th Five-Year Plan for Digital Economy Development" clearly proposes to "accelerate the construction of Internet hospitals [3] and promote the digital transformation of medical services". [1,4] As a national pilot city for health and medical big data, Shanghai issued the "Measures for the Administration of Internet Hospitals in Shanghai" in 2021, establishing an institutional framework from qualification approval, service standards, safety supervision, etc., to provide policy guarantee for the rapid development of Internet hospitals. [5] The COVID-19 epidemic became a practical catalyst: during the lockdown in Shanghai in 2022, the number of online follow-up consultations in Internet hospitals surged by 320% compared with usual days, and drug delivery orders exceeded 800,000, becoming the "lifeline" to ensure residents' basic medical needs. [6] This "policy + emergency" dual drive has not only accelerated the popularization of Internet hospitals but also exposed problems such as unbalanced service capabilities and insufficient coverage of special groups, highlighting the responsiveness of this study to real needs.[7] However, Internet hospitals also face many challenges in the process of rapid development. A survey report by the Shanghai Quality Association User Evaluation Center in 2022 showed that the evaluation of Internet hospitals by the elderly over 61 years old was significantly lower than that of the young group, especially in terms of operational convenience and service efficiency. At the same time, a study in rural areas of Ningxia showed that the utilization of online health services is more determined by social and economic conditions rather than health needs, and there is an obvious "digital divide" phenomenon. [8] These phenomena have triggered in-depth thinking on the equity of Internet medical services. This study selects the Andersen Behavioral Model and the Technology Acceptance Model (TAM) as the theoretical basis. The Andersen Model analyzes the influencing mechanism of health service utilization from three dimensions: predisposing characteristics, enabling resources, and need factors, while the Technology Acceptance Model predicts technology usage behavior through perceived usefulness and perceived ease of use. The integration of the two can comprehensively analyze the personal, technical, and social environmental factors affecting the utilization of Internet hospital services.[9,10] The research focuses on three core issues: (1) What is the current situation of smart service supply in Shanghai Internet hospitals? (2) What factors affect users' service utilization and behavioral willingness? (3) How to optimize the service model to improve accessibility and equity? Through the analysis of 117 Internet hospitals and the mining of 1,028 user questionnaires, it aims to provide a scientific basis for the sustainable development of Internet hospitals. 2 Literature Review and Theoretical Basis 2.1 Development and Practice of Internet Hospitals The development of Internet hospitals in China has gone through three stages [11]: the exploration period of telemedicine (before 2010), the platform construction period (2010-2019), and the standardized service period (since 2020). As a pioneer, Shanghai took the lead in establishing the "Shanghai Internet General Hospital" in 2019, integrating resources from 38 municipal-level hospitals to provide services such as appointment registration, cloud image films, and precise appointment in the Yangtze River Delta [1,12,13]. After the outbreak of the epidemic in 2020, the platform added a "general entrance of Internet hospitals" to support online follow-up consultation, medical insurance payment, and drug delivery. [14]The cumulative appointments in 2020 reached 23.41 million, an increase of 13.2% compared with 2019.[15] In terms of service scenario innovation, Shanghai has launched seven convenient medical digital transformation scenarios: precise appointment (time accurate to 30 minutes), intelligent pre-consultation (waiting time reduced by 70%), electronic medical record card, interconnection and mutual recognition (recognition rate 91.7%), "one-stop" medical payment, "one-click access" for nucleic acid testing, and the "five major centers" for first aid. As a demonstration zone, Qingpu District has realized the closed loop of "remote consultation - online follow-up consultation - drug delivery" through the Yangtze River Delta Smart Internet Hospital, enabling residents to obtain municipal expert services in village clinics [16]. 2.2 Application of Andersen Model in Health Service Research Since its proposal in 1968, the Andersen Model has become a classic framework for analyzing health service utilization. Xu Bixia et al., in their study on children's willingness to seek medical treatment at the grassroots level, revised the model into four dimensions: situational characteristics (policy environment), personal characteristics (demographic factors), health behavior (service usage frequency), and health outcomes (satisfaction). The study found that children's medical behavior is mainly affected by geographical accessibility (OR=1.514), satisfaction with primary services (OR=0.348), and the number of medical visits (OR=0.248) [17]. Chen Lijiang et al.'s study on county medical communities confirmed that occupation type (employees of enterprises and institutions OR=2.439), medical insurance cognition (those who are very familiar OR=2.840), and medical treatment type (inpatients OR=1.994) significantly affect satisfaction. These studies provide methodological references for the construction of this model. 2.3 Technology Acceptance Model and Health Behavior Research The Technology Acceptance Model (TAM) emphasizes that perceived usefulness and perceived ease of use are the core antecedents of technology adoption. A study on online health services for rural residents in Ningxia showed that health knowledge scores (β=0.21, P<0.01) and smartphone penetration rate (β=0.18, P<0.05) significantly improve perceived ease of use; while income level (β=0.32, P<0.01) and previous use experience (β=0.41, P<0.01) directly affect payment willingness. A survey in Shanghai further found that due to cognitive decline and insufficient digital literacy, the elderly scored 34.7% lower than young people in terms of operational convenience (P<0.01). 2.4 Research Framework Construction Integrating the Andersen Model and the Technology Acceptance Model, a four-dimensional framework of "service supply - individual ability - technical cognition - behavioral outcome" [18]is formed as Figure 1 . This framework takes into account both structural factors and individual cognitive factors, providing a systematic perspective for Internet hospital service research. Detailed data information is shown in Table 1 . Table 1: Research Variables and Measurement Dimensions Model Dimension Core Variables Operational Definition Measurement Source Predisposing Characteristics Age, education level, place of residence Basic demographic characteristics Andersen Model Enabling Resources Income, medical insurance type, device access Supporting conditions for service utilization Andersen Model Need Factors Chronic diseases, self-rated health Degree of demand for health services Andersen Model Technical Cognition Perceived usefulness, perceived ease of use Attitude towards accepting technical tools TAM Model Service Evaluation Scores on quality, efficiency, safety Satisfaction with service experience Likert Scale Utilization Behavior Usage frequency, preference for service types Actual usage situation Multiple Options 3 Research Methods 3.1 Research Design A mixed research design combining cross-sectional survey [19] and service data analysis [20] was adopted. The research objects are 117 Internet hospitals in Shanghai, covering municipal hospitals (37), district hospitals (42), community health service centers (32), and third-party platforms (6). Service data are from the 2024 statistical report of Shanghai Municipal Health Commission, and user data are collected through questionnaires. 3.2 Questionnaire Development and Reliability and Validity Test A structured questionnaire was designed based on the theoretical framework, including five modules: 1.Basic characteristics: age, gender, education level, etc. (Andersen's predisposing characteristics) 2.Enabling resources: family income, medical insurance type, smart devices, etc. 3.Health needs: chronic disease status, self-rated health, number of hospitalizations in the past year 4.Technology acceptance: perceived usefulness/ease of use scale (Cronbach's α=0.87/0.91) 5.Service evaluation: satisfaction with quality, efficiency, safety, etc. The questionnaire was revised through expert demonstration and pre-test. A pre-test of 120 people was conducted in Xuhui District before the formal survey, with an overall Cronbach's α coefficient of 0.89 and a KMO value of 0.91, indicating good reliability and validity. 3.3 Sampling and Data Collection Multi-stage stratified sampling was adopted: 1.Institutional stratification: divided by hospital level (tertiary/secondary/community) and service type (comprehensive/specialized) 2.Regional stratification: central urban areas (Xuhui, Huangpu), suburban areas (Minhang, Baoshan), outer suburban areas (Qingpu, Chongming) 3.Quota sampling: age groups (18-40 years old/41-60 years old/61-80 years old/over 80 years old) were quota-based according to population proportion The survey was conducted through "online + offline" dual channels: •Online: questionnaire links were pushed through the Health Cloud platform (September-October 2024) •Offline: on-site surveys at community health service centers (conducted simultaneously) A total of 1,152 questionnaires were recovered. After excluding invalid questionnaires with logical errors and missing values > 10%, 1,028 valid questionnaires were retained (effective rate 89.3%), exceeding the minimum sample size requirement of 1,000. 3.4 Statistical Analysis Methods SPSS 25.0 and Python were used for data analysis: 1.Descriptive statistics: frequency, percentage, and mean to analyze the current situation of service utilization 2.Chi-square test: to analyze the correlation between categorical variables (such as age group * satisfaction) 3.Cluster analysis: K-means algorithm to identify user groups based on characteristic variables and technology acceptance scores 4.Association rule mining: Apriori algorithm to explore service combination rules (support > 5%, confidence > 70%) 5.Ordinal Logistic regression: to analyze the influencing factors of service utilization Detailed data information is shown in Table 2 . Table 2: Demographic Characteristics of the Sample (N=1,028) Characteristics Categories Number of People Percentage （ % ） Gender Male 489 47.6 Female 539 52.4 Age 18-30 years old 217 21.1 31-45 years old 291 28.3 46-60 years old 288 28.0 61-80 years old 232 22.6 Education Level Junior high school and below 166 16.1 Senior high school/technical secondary school 244 23.7 Junior college/undergraduate 462 45.0 Master's degree and above 156 15.2 Residential Area Central urban areas 422 41.1 Suburban areas 364 35.4 Outer suburban areas 242 23.5 Chronic Disease Status None 588 57.2 1 type 297 28.9 ≥2 types 143 13.9 4 Research Results 4.1 Current Situation of Internet Hospital Construction in Shanghai 4.1.1 Characteristics of Service Supply Data in 2024 [Data source:2024 Shanghai Municipal Hospital Internet Platform Data Report. Health Trend, 2025.] show that Shanghai Internet hospitals have formed a multi-level service system: •Coverage rate: full access of 37 municipal-level hospitals, 89.3% coverage rate of district-level hospitals, and 76.5% coverage rate of community centers •Service volume: annual service volume increased by 23.6% year-on-year, among which online follow-up consultation (42.1%), health consultation (28.7%), and prescription delivery (19.5%) are the main types •Technology application: AI-enabled precise appointment (reducing waiting time to within 30 minutes), cloud films (serving 2.237 million person-times annually), and intelligent anti-scalper system (intercepting 758,000 attacks) However, the uneven distribution of resources is prominent: tertiary hospitals account for 78.3% of online doctor resources (19,460 out of 24,873), while community centers only account for 5.2%. The practice in Qingpu District has initially realized "data runs more, patients run less" by sinking municipal expert resources to village clinics through the "Yangtze River Delta Smart Internet Hospital" hub. There is a significant differentiation in service capabilities among different types of hospitals: all 37 municipal-level hospitals have realized the full-process service of "online follow-up consultation + medical insurance payment + drug delivery". Among them, top hospitals such as Ruijin Hospital and Huashan Hospital have an AI triage accuracy rate of 92%, 100% coverage of intelligent pre-consultation, and an average daily online service volume of over 8,000 person-times; among the 42 district-level hospitals, only 28 have opened online follow-up consultation (accounting for 66.7%), and 15 have not realized online medical insurance payment, mainly relying on offline window settlement; although 32 community health service centers have accessed the municipal platform, they only provide basic services such as registration and report query, and the coverage rate of advanced functions such as online follow-up consultation and remote consultation is less than 30%, which is inconsistent with residents' demand for "medical treatment at the doorsteps". 4.1.2 Analysis of Smart Service Scenarios According to publicly available data:a total of 9 types of core smart services are provided by 117 Internet hospitals [21], with differences in coverage rate and usage rate: •Basic services: online registration (100%), report query (97.4%) •Advanced services: online follow-up consultation (82.9%), drug delivery (76.9%) •Innovative services: intelligent pre-consultation (61.5%), AI triage (54.7%), remote joint clinic (39.3%) Detailed data information is shown in Table 3 . Table 3: Evaluation of Smart Service Scenario Usage (N=1,028) Service Type Usage Rate (%) Satisfaction (1-5) Main Feedback on Problems (%) Online Registration 91.2 4.3 Tight registration sources 68.2 Report Query 87.6 4.5 Lack of result interpretation 52.4 Online Follow-up Consultation 76.3 4.1 Response delay 45.7 Drug Delivery 71.8 4.0 High delivery fee 39.1 Intelligent Pre-consultation 42.7 3.8 Mechanical36.8 Remote Consultation 28.5 3.9 Complicated device operation 41.2 4.2 User Characteristics and Technology Acceptance 4.2.1 Analysis of Andersen Model Dimensions • Predisposing characteristics: Users aged over 61 account for only 12.3% of registered users but 22.6% of the total sample, indicating low registration rates but existing demand among the elderly. Among low-education groups (junior high school or below), 43.4% abandoned usage due to "inability to operate". • Enabling resources: Medical insurance coverage reaches 93.7%, but cross-regional payment is limited to some areas in the Yangtze River Delta (e.g., Qingpu Demonstration Zone); smartphone ownership among low-income households (<5,000 yuan/month) is only 67.8%, restricting service access. • Need factors: Chronic disease patients use services 2.3 times more frequently than healthy individuals (P<0.01), relying particularly on online follow-up consultations and prescription delivery. 4.2.2 Analysis of Technology Acceptance Model (TAM) TAM scale results show: • The mean score for perceived usefulness is 4.2/5, with 90.1% of users agreeing that it "saves medical consultation time". • Perceived ease of use scores only 3.7/5, with the elderly group (2.4 points) significantly lower than the young group (4.3 points) (P<0.01). • Regression analysis reveals: education level (β=0.32), prior experience (β=0.41), and device performance (β=0.28) are key predictors of ease of use. Chi-square tests confirm significant correlations between perceived ease of use and age (χ²=38.72, P<0.001), education level (χ²=42.15, P<0.001), and residential area (χ²=19.83, P<0.01). 4.3 Influencing Factors of Service Utilization 4.3.1 Cluster Analysis Results K-means clustering identifies three user groups: 1. High-demand and high-adaptability type (32.1%): Middle-aged and young, highly educated, with chronic diseases; frequent users of online follow-up consultations, with a satisfaction score of 4.2. Dominated by 31-50-year-olds with chronic conditions (68% of this group), 70% use online follow-ups weekly, 85% actively evaluate service quality, and 91% demand "AI-assisted diagnosis suggestions". 2. Passive usage type (46.7%): Middle-aged, with moderate education; only use basic services (registration, report inquiry), satisfaction score 3.8. Mostly 41-60-year-old healthy individuals (72% of this group), focusing on "physical examination report inquiry" (65%) and "pediatric appointment registration" (58%), with only 12% having tried paid consultation services. 3. Low-resource and high-barrier type (21.2%): Elderly, low-education, rural residents; usage rate <15%, satisfaction score 2.9. Over-61s account for 83% of this group, including 45% elderly living alone; 78% require assistance from children for registration, and 85% refuse online follow-ups due to "concerns about online diagnosis safety". Significant differences exist among the three groups in health needs (F=36.81, P<0.001) and technology acceptance (F=58.24, P<0.001). 4.3.2 Association Rule Mining The Apriori algorithm identifies strong association rules: • Rule 1: Intelligent pre-consultation → Electronic medical record card [Support=18.2%, Confidence=85.7%] • Rule 2: Online follow-up consultation → Drug delivery [Support=24.6%, Confidence=89.3%] • Rule 3: Precise appointment + Report inquiry → Health record [Support=15.8%, Confidence=82.1%] These indicate users prefer synergistic service chains over single services. 4.3.3 Regression Analysis Ordinal logistic regression shows: • Promoting factors: High education (OR=1.98), high income (OR=2.15), chronic diseases (OR=2.62), and high perceived usefulness (OR=3.41). • Hindering factors: Age ≥61 (OR=0.31), rural residence (OR=0.57), and low ease of use scores (OR=0.43). 5 Discussion and Policy Recommendations 5.1 Discussion of Key Findings Digital divide challenges health equity: This study confirms significant "usage stratification" in Shanghai’s Internet hospitals. Elderly over 61 and low-education groups, due to insufficient technical literacy and limited device access, become "low-resource and high-barrier users", consistent with Ningxia rural research showing the "digital divide is determined by socioeconomic conditions" [22]. Sustained expansion of this stratification may exacerbate health inequalities, contradicting the original intention of hierarchical medical systems [23]. Mismatch between service supply and demand: Current Internet hospitals focus on follow-up patients and mild symptom consultations, but user needs are diversified. Association rules reveal high popularity of the "intelligent pre-consultation - electronic medical record card - online follow-up" combination (confidence >85%), reflecting patient expectations for full-process service integration. However, tertiary hospitals still monopolize high-quality resources, and community centers have weak smart service functions, restricting the implementation of hierarchical diagnosis and treatment. Technology acceptance determines sustainability: The TAM verifies that perceived ease of use strongly predicts usage intention (β=0.58, P<0.01), particularly affecting the elderly. Previous Shanghai surveys warned of low elderly satisfaction due to operational difficulties; this study quantifies the impact: the elderly group’s ease of use score is 44.2% lower than the young group, directly leading to a 65.7% drop in usage rate. 5.2 Policy Recommendations 5.2.1 Building an Age-Inclusive Service System • Aging-friendly transformation: Implement an "elderly mode" (large fonts, voice navigation, one-click human assistance); establish digital health counselors in community centers to provide operational guidance; expand agency service scenarios by learning from Qingpu’s "medicine delivery for elderly and children" experience, with specific measures: (1) Technical level: Develop "voice interaction + simplified graphics" dual-mode interfaces, enlarge fonts to 1.5x default size, remove pop-up ads and complex jumps, and set "one-click return" for key operations. (2) Service level: Set up "Internet Hospital Elderly Assistance Stations" in community health centers, equipped with full-time counselors to provide "registration, appointment, and payment assistance", achieving 100% community coverage citywide by 2025. (3) Trust-building: Produce "Internet Medical Guide for the Elderly" videos (including dialect versions), popularize "online diagnosis procedures" and "medical insurance reimbursement policies" through commFunity broadcasts and bulletin boards to reduce psychological concerns. • Digital literacy improvement: Health authorities collaborate with communities to launch "smart elderly assistance" training, covering over 80% of communities; produce dialect teaching videos to lower learning barriers. • Device inclusiveness program: Provide subsidized smart devices to low-income elderly, pre-installing Internet hospital quick access. 5.2.2 Innovating Payment and Incentive Mechanisms • Deepening online medical insurance payment: Expand cross-regional settlement coverage for diseases and regions, especially in the Yangtze River Delta Demonstration Zone; pilot "online initial consultation" medical insurance payment (currently limited to follow-ups). Achieve "non-discriminatory settlement" of Yangtze River Delta medical insurance by the end of 2025, expand online reimbursement disease coverage (from current hypertension and diabetes to 10 chronic diseases including coronary heart disease and COPD), and pilot "online initial consultation insurance subsidies" with 50 yuan/consultation for common diseases [24,1]. • Differentiated pricing: Offer service package discounts to "passive users" (e.g., appointment + consultation combo offers) based on cluster analysis; develop tiered pricing referencing Ningxia’s Haodaifu platform range of 65.52±67.87 yuan. • Performance incentives: Include Internet hospital service volume in doctors’ performance evaluations to increase willingness to supply high-quality resources. 5.2.3 Optimizing Service Supply Models • Integrating service chains: Design "one-stop service packages" (e.g., pre-consultation - registration - follow-up - delivery) based on association rules to reduce operational jumps. • Resource sinking to communities: Promote the Qingpu model by introducing municipal expert resources to community Internet terminals; equip community health centers with telemedicine rooms, achieving 100% coverage by 2025. Establish a three-tier remote collaboration network of "municipal experts - district doctors - community nurses", requiring municipal experts to complete 20 community remote consultations monthly (added as a bonus item in professional title evaluations); install "remote diagnosis all-in-one machines" (with HD cameras, electronic stethoscopes, etc.) in community centers, maintained uniformly by municipal platforms to ensure technical stability. • Technology upgrading: Apply generative AI to optimize intelligent pre-consultation (e.g., personalized question generation); build regional imaging clouds to achieve "one imaging, universal sharing". 5.2.4 Supervision and Quality Assurance • Standardization: Formulate "Internet Hospital Smart Service Evaluation Standards" covering service efficiency, safety, and aging-friendliness. • Dynamic monitoring: Establish a "digital health equity index" to track service utilization among vulnerable groups. • Scalper prevention: Upgrade AI risk control systems (learning from the 2024 experience of blocking 13,994 scalper accounts) to ensure fair distribution of registration resources. 5.2.5 Priorities for Service Supply Optimization • Short-term (within 1 year): Focus on promoting the "intelligent pre-consultation + online follow-up + drug delivery" service package; open online follow-up permissions for community centers with technical support from municipal hospitals. • Medium-term (2-3 years): Build a "regional health data platform" to realize cross-hospital sharing of electronic medical records, examination results, and medication records, reducing duplicate inspections. • Long-term (3-5 years): Optimize intelligent pre-consultation with generative AI, generating personalized inquiry scripts by learning patients’ historical medical records, reducing "mechanical question" feedback from 36.8% to below 15%. 5.3 Research Limitations and Prospects This study has three limitations: 1. Research Design: This study employed a cross-sectional design, which limits our ability to infer causal relationships between the identified factors (e.g., perceived ease of use) and service utilization behaviors. The associations observed may be subject to reverse causality or confounding by unmeasured variables. Longitudinal or interventional studies are needed to establish causality. 2. Sampling and Measurement Bias: Despite employing a mixed online/offline strategy, our sampling method may still under-represent the most digitally excluded populations (e.g., the oldest-old without any family support, or residents in remote rural areas), potentially leading to an overestimation of overall usage rates and satisfaction . Furthermore, the use of self-reported questionnaires is susceptible to common method bias and social desirability bias. 3. Scope of Perspectives: This study focused on the user (patient) side. The acceptance, motivations, and barriers faced by healthcare providers (doctors and nurses) in delivering services through Internet hospitals are crucial determinants of service quality and sustainability, which were not explored here. 4. Generalizability: The findings are based on data from Shanghai, a highly developed megacity in China. The specific prevalence of service types, user typologies, and the magnitude of the digital divide may not be directly generalizable to cities or regions with different economic, demographic, and healthcare infrastructural profiles. 5. Temporal Dynamics: The field of Internet hospitals is evolving rapidly. The data collected in 2024 represents a snapshot in time, and the service landscape, user behavior, and policy environment are subject to continuous change. Future research directions include: • AI integration: Exploring the role of large language models (LLMs) in optimizing intelligent pre-consultation. • Yangtze River Delta integration: Studying cross-regional medical insurance settlement and resource sharing mechanisms based on Qingpu’s demonstration experience. • Emergency response mechanisms: Improving Internet hospitals’ functional transformation during public health events (e.g., epidemic early screening). 6 Conclusion This study systematically analyzes the smart service status and user behavior of 117 Internet hospitals in Shanghai by integrating the Andersen Health Service Model and TAM. Findings show Shanghai has built a citywide Internet hospital system with over 13.32 million registered users and 149 million cumulative services, but faces challenges in service accessibility, aging-friendliness, and resource balance. Cluster analysis identifies three user groups, with the "low-resource and high-barrier type" (21.2%)—mainly elderly and low-education groups—having a usage rate 85%), indicating demand for full-process integrated services. To promote sustainable development, four core strategies are proposed: building an age-inclusive system, innovating payment incentives, optimizing service supply, and strengthening quality supervision. Through aging-friendly transformation, medical insurance payment innovation, resource sinking, and AI application, service accessibility and equity can be enhanced, making Internet hospitals a strong support for "Healthy China" and "Digital China" strategies. The findings from Shanghai, as a front-runner, may offer valuable insights for other regions at different developmental stages. For instance, resource-intensive cities might need to place greater emphasis on equity-focused design to avoid exacerbating the digital divide. Regions with more limited resources could prioritize achieving universal access to core services before investing heavily in advanced functionalities. Future research should test the transferability of these findings and strategies in diverse socio-economic contexts. Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Guilin Medical University (Approval No. GXMU-2024-ETH-078). All participants were informed of the study’s purpose, procedures, potential risks, and benefits prior to participation. Written informed consent was obtained from all participants. For online respondents, consent was obtained electronically via a mandatory confirmation checkbox before proceeding to the questionnaire. Participants were assured of anonymity and confidentiality and were free to withdraw at any time without consequence. Consent for publication Not applicable. This study does not contain any individual person’s data in any form (including any individual details, images, or videos). Availability of data and materials The data supporting the findings of this study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the following projects:Research on Innovative Training Models for Multi-skilled Professionals in Home-Based Elderly Care Services under the Silver Economy in Guangdong Province: Integration of Medical and Nursing Care (2025GXJK0906)；Research on Innovative Models for Empowering Modern Domestic Service Programs to Provide Services for the Elderly and Children under the 'Hundreds of Thousands' Project (JXJYGC2024D158)；Guangdong Province Educational Science Planning Project (Higher Education Special Program) (2023GXJK908)；2025 Guangdong Province General Colleges and Universities Special Innovation Project (2025WTSCX267). Authors' contributions Rong Huang: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. Jingliu Huang: Conceptualization, Data curation, Methodology, Project administration, Supervision, Writing – original draft. Tingting Li: Conceptualization, Formal Analysis, Methodology, Project administration, Writing – original draft. Tao Jiang: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Validation, Visualization, Writing – original draft, Writing – review & editing. All authors read and approved the final manuscript. Acknowledgements The authors would like to thank the Shanghai Municipal Health Commission for providing the data platform support. We are also grateful to all participants who contributed their time and insights to this survey. Additionally, we extend our appreciation to the editors and anonymous reviewers for their constructive comments. Abbreviations AI:Artificial Intelligence; COPD:Chronic Obstructive Pulmonary Disease; COVID-19:Coronavirus Disease 2019; HD:High-Definition; IRB:Institutional Review Board; LLMs:Large Language Models; KMO:Kaiser-Meyer-Olkin (Measure of Sampling Adequacy); OR:Odds Ratio; SPSS:Statistical Package for the Social Sciences; TAM:Technology Acceptance Model. References Lai Y, Chen S, Li M, et al. Policy Interventions, Development Trends, and Service Innovations of Internet Hospitals in China: Documentary Analysis and Qualitative Interview Study. J Med Internet Res. 2021;23(7):e22330. doi:10.2196/22330. Xu R, Zhang T, Zhang Q. Investigating the effect of online and offline reputation on the provision of online counseling services: A case study of the Internet hospitals in China. Front Eng Manag. 2022;9(4):692-706. doi:10.1007/s42524-022-0219-z. Si Xiaoping, Shi Chengxia, Zhang Feng, Ding Lacun, Wang Yi, Zhu Yuelan. Analysis and Reflection on Hot Issues in the Construction of Smart Hospitals[J].Modern Medicine and Health,2021,37(17):3040-3043. World Health Organization. Global strategy on digital health 2020-2025. Geneva: World Health Organization; 2021. Available from: https://www.who.int/publications/i/item/9789240020924 Han Yangyang,Lie Reidar K,Guo Rui. The Internet Hospital as a Telehealth Model in China: Systematic Search and Content Analysis. [J]. Journal of medical Internet research,2020,22(7). DOI: 10.2196/17995; Liu, J., Liu, S., Zheng, T., & Bi, Y. (2022). The role of internet hospitals in China during the COVID-19 pandemic: A systematic review. Frontiers in Public Health, 10, 1067513.DOI: 10.3389/fpubh.2022.1067513 Lai, Y., Chen, S., Li, M., Ung, C. O. L., & Hu, H. (2021). Policy Interventions, Development Trends, and Service Innovations of Internet Hospitals in China: Documentary Analysis and Qualitative Interview Study. Journal of Medical Informatics, 23(7), e22330.DOI: 10.2196/22330 Czaja, S. J., & Lee, C. C. (2007). The impact of aging on access to technology. Universal Access in the Information Society, 5(4), 341-349.DOI: 10.1007/s10209-006-0060-x Andersen, R. M. (1995). Revisiting the behavioral model and access to medical care: does it matter? Journal of Health and Social Behavior, 36(1), 1-10. DOI: 10.2307/2137284 Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. DOI: 10.2307/249008 Xia Xianyu. Exploration of Internet Hospital Information System Based on Information Integration Platform[J]. Journal of Physics: Conference Series,2021,1856(1). DOI: 10.1088/1742-6596/1856/1/012059 Shanghai: Launches a 'Internet Hospital' main portal, allowing a smartphone to 'visit' major hospitals across Shanghai [Internet]. HIT Expert Network. Source: Shanghai Shenkang Hospital Development Center.2019-12-19 [cited 2026-02-10]. Available from: https://www.hit180.com/49252.html#respond Han, Y., Lie, R. K., & Guo, R. (2020). The Internet Hospital as a Telehealth Model in China: Systematic Search and Content Analysis. Journal of Medical Internet Research, 22(7), e17995. DOI: 10.2196/17995 Wang, X., Zhang, Z., & Zhao, J. (2022). The impact of online medical insurance payment on the use of internet hospitals: Evidence from China. Frontiers in Public Health, 10, 943998. https://doi.org/10.3389/fpubh.2022.943998 Shanghai Shenkang Hospital Development Center. (2021). 2020 Annual Report on the Operation of Shanghai Municipal Hospital Internet Platform. Li Dehe,Hu Yinhuan,Liu Sha,Li Gang,Lu Chuntao,Yuan Shaochun,Zhang Zemiao. The effect of using internet hospitals on the physician-patient relationship: Patient perspective. [J]. International journal of medical informatics,2023,174. DOI: 10.1016/j.ijmedinf.2023.105046 Andersen, R. M. (1995). Revisiting the behavioral model and access to medical care: does it matter? Journal of Health and Social Behavior, 36(1), 1-10.DOI: 10.2307/2137284 Rho, M. J., Choi, I. Y., & Lee, J. (2014). Predictive factors of telemedicine service acceptance and behavioral intention of physicians. International Journal of Medical Informatics, 83(8), 559-571.DOI: 10.1016/j.ijmedinf.2014.05.005 Creswell, J. W., & Plano Clark, V. L. (2017). Designing and conducting mixed methods research (3rd ed.). Sage publications. Ronghua XU, Tingting ZHANG, Qingpeng ZHANG. Investigating the effect of online and offline reputation on the provision of online counseling services: A case study of the Internet hospitals in China[J]. Frontiers of Engineering Management,2022,9(4). DOI: 10.1007/s42524-022-0219-z Yao Huayan, He Ping, Cui Bin, Shi Dongsheng, Xu Bo. China Digital Medicine,2023,18(03):15-19. An J, Chen FF, Zhao W, Liu GD, Liu Y, Zhang H, Wang ZJ. Exploration of Internet Hospital Information System Based on Information Integration Platform [J]. China Hospital Management, 2020, 40(10): 85-87. Kissi, J., Osei-Frimpong, K., & Boateng, R. (2023). The digital divide and health equity: A framework for understanding and addressing the risk of digital technologies exacerbating health disparities. The Lancet Digital Health, 5(4), e214-e223. DOI: 10.1016/S2589-7500(23)00012-5; Wang, X., Zhang, Z., & Zhao, J. (2022). The impact of online medical insurance payment on the use of internet hospitals: Evidence from China. Frontiers in Public Health, 10, 943998.DOI: 10.3389/fpubh.2022.943998; Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8787379","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":602539509,"identity":"cc35aca5-2d5e-4352-9332-e6d2ae06fbf6","order_by":0,"name":"Rong Huang","email":"","orcid":"","institution":"Zhuhai City Polytechnic","correspondingAuthor":false,"prefix":"","firstName":"Rong","middleName":"","lastName":"Huang","suffix":""},{"id":602539511,"identity":"77a63457-d727-4527-ac84-111e511c79ab","order_by":1,"name":"Tingting Li","email":"","orcid":"","institution":"Guilin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Li","suffix":""},{"id":602539512,"identity":"12d821ad-1dfb-4efe-af50-2c7cd2fa8707","order_by":2,"name":"Jingliu Huang","email":"","orcid":"","institution":"Guilin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jingliu","middleName":"","lastName":"Huang","suffix":""},{"id":602539514,"identity":"217eb37b-420f-47f7-bb28-6c1dce9a3ccd","order_by":3,"name":"Tao Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYFCCwwcfJAApfmbmww+I0sDDcCzZ4AOQIdnOlmZApBYeM8kZQIbBeR4FCaK02DMeMJPmbbNJ3HyYh8GAocYmmghbDiRb87alJW47zHvgAcOxtNwGIrQcvM3bdjh322G+BAPGhsPEaDnYIA3SsrmZx0CCSC2HmSRnArVsYCZay4FjzAYfzqXVzzgMDOQEYvzCPuP8xwcJZTbG/P2HDz/4UGNDWAuDxAEkTgJB5SDAT9jUUTAKRsEoGOkAAAe2Qh9uP9vXAAAAAElFTkSuQmCC","orcid":"","institution":"Guilin Medical University","correspondingAuthor":true,"prefix":"","firstName":"Tao","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2026-02-04 13:55:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8787379/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8787379/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104347080,"identity":"c70baacc-7b28-4445-b2e5-4c3b5cbf1c90","added_by":"auto","created_at":"2026-03-10 18:05:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":146252,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eservice supply - individual ability - technical cognition - behavioral outcome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e•Service Supply Layer\u003c/strong\u003e: Policy support, technical platform, resource integration\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e•Individual Ability Layer\u003c/strong\u003e: Predisposing characteristics (age, education, etc.), enabling resources (income, medical insurance, etc.)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e•Technical Cognition Layer\u003c/strong\u003e: Perceived usefulness, perceived ease of use\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e•Behavioral Outcome Layer\u003c/strong\u003e: Usage willingness, actual usage, satisfaction\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8787379/v1/21b5c5055b881fde8459f410.png"},{"id":104405264,"identity":"088c7da4-1021-4629-a900-2f1a08676156","added_by":"auto","created_at":"2026-03-11 12:22:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1578068,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8787379/v1/2dd257e5-0d4e-40dd-ac93-3ecb1d2f17c9.pdf"},{"id":104347081,"identity":"b31e23a7-c06f-46b2-9485-e616bc9a920c","added_by":"auto","created_at":"2026-03-10 18:05:06","extension":"doc","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":46592,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.doc","url":"https://assets-eu.researchsquare.com/files/rs-8787379/v1/895be5d86f8e4e7585b5acd1.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on the Current Situation of Smart Services in Shanghai Internet Hospitals and User Behavior—An Integrated Analysis Based on Andersen Model and Technology Acceptance Model","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eDriven by the global digital transformation wave and the normalization of COVID-19 prevention and control, Internet hospitals, as a new carrier of medical and health services, are profoundly changing the traditional medical service model. As one of the cities with the most abundant medical resources and the highest informatization level in China, Shanghai took the lead in promoting the construction of Internet hospitals. By 2024, the Shanghai Municipal Hospital Internet Platform has connected 37 municipal-level hospitals, with more than 13.32 million registered users and a cumulative service volume of 149 million person-times, providing more than 40,000 online services per day. [1,2]These figures reflect residents' urgent demand for convenient medical services and also demonstrate the important role of Internet hospitals in optimizing the allocation of medical resources. At the policy level, the national \"14th Five-Year Plan for Digital Economy Development\" clearly proposes to \"accelerate the construction of Internet hospitals [3] and promote the digital transformation of medical services\". [1,4] As a national pilot city for health and medical big data, Shanghai issued the \"Measures for the Administration of Internet Hospitals in Shanghai\" in 2021, establishing an institutional framework from qualification approval, service standards, safety supervision, etc., to provide policy guarantee for the rapid development of Internet hospitals. [5] The COVID-19 epidemic became a practical catalyst: during the lockdown in Shanghai in 2022, the number of online follow-up consultations in Internet hospitals surged by 320% compared with usual days, and drug delivery orders exceeded 800,000, becoming the \"lifeline\" to ensure residents' basic medical needs. [6] This \"policy + emergency\" dual drive has not only accelerated the popularization of Internet hospitals but also exposed problems such as unbalanced service capabilities and insufficient coverage of special groups, highlighting the responsiveness of this study to real needs.[7]\u003c/p\u003e\n\u003cp\u003eHowever, Internet hospitals also face many challenges in the process of rapid development. A survey report by the Shanghai Quality Association User Evaluation Center in 2022 showed that the evaluation of Internet hospitals by the elderly over 61 years old was significantly lower than that of the young group, especially in terms of operational convenience and service efficiency. At the same time, a study in rural areas of Ningxia showed that the utilization of online health services is more determined by social and economic conditions rather than health needs, and there is an obvious \"digital divide\" phenomenon. [8] These phenomena have triggered in-depth thinking on the equity of Internet medical services.\u003c/p\u003e\n\u003cp\u003eThis study selects the Andersen Behavioral Model and the Technology Acceptance Model (TAM) as the theoretical basis. The Andersen Model analyzes the influencing mechanism of health service utilization from three dimensions: predisposing characteristics, enabling resources, and need factors, while the Technology Acceptance Model predicts technology usage behavior through perceived usefulness and perceived ease of use. The integration of the two can comprehensively analyze the personal, technical, and social environmental factors affecting the utilization of Internet hospital services.[9,10]\u003c/p\u003e\n\u003cp\u003eThe research focuses on three core issues: (1) What is the current situation of smart service supply in Shanghai Internet hospitals? (2) What factors affect users' service utilization and behavioral willingness? (3) How to optimize the service model to improve accessibility and equity? Through the analysis of 117 Internet hospitals and the mining of 1,028 user questionnaires, it aims to provide a scientific basis for the sustainable development of Internet hospitals.\u003c/p\u003e"},{"header":"2 Literature Review and Theoretical Basis","content":"\u003ch3\u003e2.1 Development and Practice of Internet Hospitals\u003c/h3\u003e\n\u003cp\u003eThe development of Internet hospitals in China has gone through three stages [11]: the exploration period of telemedicine (before 2010), the platform construction period (2010-2019), and the standardized service period (since 2020). As a pioneer, Shanghai took the lead in establishing the \u0026quot;Shanghai Internet General Hospital\u0026quot; in 2019, integrating resources from 38 municipal-level hospitals to provide services such as appointment registration, cloud image films, and precise appointment in the Yangtze River Delta [1,12,13]. After the outbreak of the epidemic in 2020, the platform added a \u0026quot;general entrance of Internet hospitals\u0026quot; to support online follow-up consultation, medical insurance payment, and drug delivery. [14]The cumulative appointments in 2020 reached 23.41 million, an increase of 13.2% compared with 2019.[15]\u003c/p\u003e\n\u003cp\u003eIn terms of service scenario innovation, Shanghai has launched seven convenient medical digital transformation scenarios: precise appointment (time accurate to 30 minutes), intelligent pre-consultation (waiting time reduced by 70%), electronic medical record card, interconnection and mutual recognition (recognition rate 91.7%), \u0026quot;one-stop\u0026quot; medical payment, \u0026quot;one-click access\u0026quot; for nucleic acid testing, and the \u0026quot;five major centers\u0026quot; for first aid. As a demonstration zone, Qingpu District has realized the closed loop of \u0026quot;remote consultation - online follow-up consultation - drug delivery\u0026quot; through the Yangtze River Delta Smart Internet Hospital, enabling residents to obtain municipal expert services in village clinics [16].\u003c/p\u003e\n\u003ch3\u003e2.2 Application of Andersen Model in Health Service Research\u003c/h3\u003e\n\u003cp\u003eSince its proposal in 1968, the Andersen Model has become a classic framework for analyzing health service utilization. Xu Bixia et al., in their study on children\u0026apos;s willingness to seek medical treatment at the grassroots level, revised the model into four dimensions: situational characteristics (policy environment), personal characteristics (demographic factors), health behavior (service usage frequency), and health outcomes (satisfaction). The study found that children\u0026apos;s medical behavior is mainly affected by geographical accessibility (OR=1.514), satisfaction with primary services (OR=0.348), and the number of medical visits (OR=0.248) [17]. Chen Lijiang et al.\u0026apos;s study on county medical communities confirmed that occupation type (employees of enterprises and institutions OR=2.439), medical insurance cognition (those who are very familiar OR=2.840), and medical treatment type (inpatients OR=1.994) significantly affect satisfaction. These studies provide methodological references for the construction of this model.\u003c/p\u003e\n\u003ch3\u003e2.3 Technology Acceptance Model and Health Behavior Research\u003c/h3\u003e\n\u003cp\u003eThe Technology Acceptance Model (TAM) emphasizes that perceived usefulness and perceived ease of use are the core antecedents of technology adoption. A study on online health services for rural residents in Ningxia showed that health knowledge scores (\u0026beta;=0.21, P\u0026lt;0.01) and smartphone penetration rate (\u0026beta;=0.18, P\u0026lt;0.05) significantly improve perceived ease of use; while income level (\u0026beta;=0.32, P\u0026lt;0.01) and previous use experience (\u0026beta;=0.41, P\u0026lt;0.01) directly affect payment willingness. A survey in Shanghai further found that due to cognitive decline and insufficient digital literacy, the elderly scored 34.7% lower than young people in terms of operational convenience (P\u0026lt;0.01).\u003c/p\u003e\n\u003ch3\u003e2.4 Research Framework Construction\u003c/h3\u003e\n\u003cp\u003eIntegrating the Andersen Model and the Technology Acceptance Model, a four-dimensional framework of \u0026quot;service supply - individual ability - technical cognition - behavioral outcome\u0026quot; [18]is formed as \u003cstrong\u003eFigure 1\u003c/strong\u003e .\u003c/p\u003e\n\u003cp\u003eThis framework takes into account both structural factors and individual cognitive factors, providing a systematic perspective for Internet hospital service research.\u003c/p\u003e\n\u003cp\u003eDetailed data information is shown in \u003cstrong\u003eTable 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Research Variables and Measurement Dimensions\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"615\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel Dimension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCore Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOperational Definition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasurement Source\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003ePredisposing Characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eAge, education level, place of residence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eBasic demographic characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eAndersen Model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eEnabling Resources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eIncome, medical insurance type, device access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eSupporting conditions for service utilization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eAndersen Model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eNeed Factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eChronic diseases, self-rated health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eDegree of demand for health services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eAndersen Model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eTechnical Cognition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003ePerceived usefulness, perceived ease of use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eAttitude towards accepting technical tools\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eTAM Model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eService Evaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eScores on quality, efficiency, safety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eSatisfaction with service experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eLikert Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eUtilization Behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eUsage frequency, preference for service types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eActual usage situation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eMultiple Options\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"3 Research Methods","content":"\u003ch3\u003e3.1 Research Design\u003c/h3\u003e\n\u003cp\u003eA mixed research design combining cross-sectional survey [19] and service data analysis [20] was adopted. The research objects are 117 Internet hospitals in Shanghai, covering municipal hospitals (37), district hospitals (42), community health service centers (32), and third-party platforms (6). Service data are from the 2024 statistical report of Shanghai Municipal Health Commission, and user data are collected through questionnaires.\u003c/p\u003e\n\u003ch3\u003e3.2 Questionnaire Development and Reliability and Validity Test\u003c/h3\u003e\n\u003cp\u003eA structured questionnaire was designed based on the theoretical framework, including five modules:\u003c/p\u003e\n\u003cp\u003e1.Basic characteristics: age, gender, education level, etc. (Andersen\u0026apos;s predisposing characteristics)\u003c/p\u003e\n\u003cp\u003e2.Enabling resources: family income, medical insurance type, smart devices, etc.\u003c/p\u003e\n\u003cp\u003e3.Health needs: chronic disease status, self-rated health, number of hospitalizations in the past year\u003c/p\u003e\n\u003cp\u003e4.Technology acceptance: perceived usefulness/ease of use scale (Cronbach\u0026apos;s \u0026alpha;=0.87/0.91)\u003c/p\u003e\n\u003cp\u003e5.Service evaluation: satisfaction with quality, efficiency, safety, etc.\u003c/p\u003e\n\u003cp\u003eThe questionnaire was revised through expert demonstration and pre-test. A pre-test of 120 people was conducted in Xuhui District before the formal survey, with an overall Cronbach\u0026apos;s \u0026alpha; coefficient of 0.89 and a KMO value of 0.91, indicating good reliability and validity.\u003c/p\u003e\n\u003ch3\u003e3.3 Sampling and Data Collection\u003c/h3\u003e\n\u003cp\u003eMulti-stage stratified sampling was adopted:\u003c/p\u003e\n\u003cp\u003e1.Institutional stratification: divided by hospital level (tertiary/secondary/community) and service type (comprehensive/specialized)\u003c/p\u003e\n\u003cp\u003e2.Regional stratification: central urban areas (Xuhui, Huangpu), suburban areas (Minhang, Baoshan), outer suburban areas (Qingpu, Chongming)\u003c/p\u003e\n\u003cp\u003e3.Quota sampling: age groups (18-40 years old/41-60 years old/61-80 years old/over 80 years old) were quota-based according to population proportion\u003c/p\u003e\n\u003cp\u003eThe survey was conducted through \u0026quot;online + offline\u0026quot; dual channels:\u003c/p\u003e\n\u003cp\u003e\u0026bull;Online: questionnaire links were pushed through the Health Cloud platform (September-October 2024)\u003c/p\u003e\n\u003cp\u003e\u0026bull;Offline: on-site surveys at community health service centers (conducted simultaneously)\u003c/p\u003e\n\u003cp\u003eA total of 1,152 questionnaires were recovered. After excluding invalid questionnaires with logical errors and missing values \u0026gt; 10%, 1,028 valid questionnaires were retained (effective rate 89.3%), exceeding the minimum sample size requirement of 1,000.\u003c/p\u003e\n\u003ch3\u003e3.4 Statistical Analysis Methods\u003c/h3\u003e\n\u003cp\u003eSPSS 25.0 and Python were used for data analysis:\u003c/p\u003e\n\u003cp\u003e1.Descriptive statistics: frequency, percentage, and mean to analyze the current situation of service utilization\u003c/p\u003e\n\u003cp\u003e2.Chi-square test: to analyze the correlation between categorical variables (such as age group * satisfaction)\u003c/p\u003e\n\u003cp\u003e3.Cluster analysis: K-means algorithm to identify user groups based on characteristic variables and technology acceptance scores\u003c/p\u003e\n\u003cp\u003e4.Association rule mining: Apriori algorithm to explore service combination rules (support \u0026gt; 5%, confidence \u0026gt; 70%)\u003c/p\u003e\n\u003cp\u003e5.Ordinal Logistic regression: to analyze the influencing factors of service utilization\u003c/p\u003e\n\u003cp\u003eDetailed data information is shown in\u003cstrong\u003e\u0026nbsp;Table 2\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Demographic Characteristics of the Sample (N=1,028)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of People\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e47.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e52.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e18-30 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e21.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e31-45 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e28.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e46-60 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e28.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e61-80 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e22.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eEducation Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eJunior high school and below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e16.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eSenior high school/technical secondary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e23.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eJunior college/undergraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e45.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eMaster\u0026apos;s degree and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eResidential Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eCentral urban areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e41.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eSuburban areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e35.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eOuter suburban areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e23.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eChronic Disease Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e57.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e1 type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e28.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026ge;2 types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e13.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"4 Research Results","content":"\u003ch3\u003e4.1 Current Situation of Internet Hospital Construction in Shanghai\u003c/h3\u003e\n\u003ch4\u003e4.1.1 Characteristics of Service Supply\u003c/h4\u003e\n\u003cp\u003eData in 2024 [Data source:2024 Shanghai Municipal Hospital Internet Platform Data Report. Health Trend, 2025.] show that Shanghai Internet hospitals have formed a multi-level service system:\u003c/p\u003e\n\u003cp\u003e•Coverage rate: full access of 37 municipal-level hospitals, 89.3% coverage rate of district-level hospitals, and 76.5% coverage rate of community centers\u003c/p\u003e\n\u003cp\u003e•Service volume: annual service volume increased by 23.6% year-on-year, among which online follow-up consultation (42.1%), health consultation (28.7%), and prescription delivery (19.5%) are the main types\u003c/p\u003e\n\u003cp\u003e•Technology application: AI-enabled precise appointment (reducing waiting time to within 30 minutes), cloud films (serving 2.237 million person-times annually), and intelligent anti-scalper system (intercepting 758,000 attacks)\u003c/p\u003e\n\u003cp\u003eHowever, the uneven distribution of resources is prominent: tertiary hospitals account for 78.3% of online doctor resources (19,460 out of 24,873), while community centers only account for 5.2%. The practice in Qingpu District has initially realized \"data runs more, patients run less\" by sinking municipal expert resources to village clinics through the \"Yangtze River Delta Smart Internet Hospital\" hub. There is a significant differentiation in service capabilities among different types of hospitals: all 37 municipal-level hospitals have realized the full-process service of \"online follow-up consultation + medical insurance payment + drug delivery\". Among them, top hospitals such as Ruijin Hospital and Huashan Hospital have an AI triage accuracy rate of 92%, 100% coverage of intelligent pre-consultation, and an average daily online service volume of over 8,000 person-times; among the 42 district-level hospitals, only 28 have opened online follow-up consultation (accounting for 66.7%), and 15 have not realized online medical insurance payment, mainly relying on offline window settlement; although 32 community health service centers have accessed the municipal platform, they only provide basic services such as registration and report query, and the coverage rate of advanced functions such as online follow-up consultation and remote consultation is less than 30%, which is inconsistent with residents' demand for \"medical treatment at the doorsteps\".\u003c/p\u003e\n\u003ch4\u003e4.1.2 Analysis of Smart Service Scenarios\u003c/h4\u003e\n\u003cp\u003eAccording to publicly available data:a total of 9 types of core smart services are provided by 117 Internet hospitals [21], with differences in coverage rate and usage rate:\u003c/p\u003e\n\u003cp\u003e•Basic services: online registration (100%), report query (97.4%)\u003c/p\u003e\n\u003cp\u003e•Advanced services: online follow-up consultation (82.9%), drug delivery (76.9%)\u003c/p\u003e\n\u003cp\u003e•Innovative services: intelligent pre-consultation (61.5%), AI triage (54.7%), remote joint clinic (39.3%)\u003c/p\u003e\n\u003cp\u003eDetailed data information is shown in\u003cstrong\u003e\u0026nbsp;Table 3\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Evaluation of Smart Service Scenario Usage (N=1,028)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"615\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eService Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUsage Rate (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSatisfaction (1-5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMain Feedback on Problems (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOnline Registration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e91.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTight registration sources 68.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReport Query\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e87.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLack of result interpretation 52.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOnline Follow-up Consultation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e76.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eResponse delay 45.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDrug Delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh delivery fee 39.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIntelligent Pre-consultation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMechanical36.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRemote Consultation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eComplicated device operation 41.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 User Characteristics and Technology Acceptance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.1 Analysis of Andersen Model Dimensions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e• Predisposing characteristics: Users aged over 61 account for only 12.3% of registered users but 22.6% of the total sample, indicating low registration rates but existing demand among the elderly. Among low-education groups (junior high school or below), 43.4% abandoned usage due to \"inability to operate\".\u003c/p\u003e\n\u003cp\u003e• Enabling resources: Medical insurance coverage reaches 93.7%, but cross-regional payment is limited to some areas in the Yangtze River Delta (e.g., Qingpu Demonstration Zone); smartphone ownership among low-income households (\u0026lt;5,000 yuan/month) is only 67.8%, restricting service access.\u003c/p\u003e\n\u003cp\u003e• Need factors: Chronic disease patients use services 2.3 times more frequently than healthy individuals (P\u0026lt;0.01), relying particularly on online follow-up consultations and prescription delivery.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.2 Analysis of Technology Acceptance Model (TAM)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTAM scale results show:\u003c/p\u003e\n\u003cp\u003e• The mean score for perceived usefulness is 4.2/5, with 90.1% of users agreeing that it \"saves medical consultation time\".\u003c/p\u003e\n\u003cp\u003e• Perceived ease of use scores only 3.7/5, with the elderly group (2.4 points) significantly lower than the young group (4.3 points) (P\u0026lt;0.01).\u003c/p\u003e\n\u003cp\u003e• Regression analysis reveals: education level (β=0.32), prior experience (β=0.41), and device performance (β=0.28) are key predictors of ease of use.\u003c/p\u003e\n\u003cp\u003eChi-square tests confirm significant correlations between perceived ease of use and age (χ²=38.72, P\u0026lt;0.001), education level (χ²=42.15, P\u0026lt;0.001), and residential area (χ²=19.83, P\u0026lt;0.01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Influencing Factors of Service Utilization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3.1 Cluster Analysis Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eK-means clustering identifies three user groups:\u003c/p\u003e\n\u003cp\u003e1. High-demand and high-adaptability type (32.1%): Middle-aged and young, highly educated, with chronic diseases; frequent users of online follow-up consultations, with a satisfaction score of 4.2. Dominated by 31-50-year-olds with chronic conditions (68% of this group), 70% use online follow-ups weekly, 85% actively evaluate service quality, and 91% demand \"AI-assisted diagnosis suggestions\".\u003c/p\u003e\n\u003cp\u003e2. Passive usage type (46.7%): Middle-aged, with moderate education; only use basic services (registration, report inquiry), satisfaction score 3.8. Mostly 41-60-year-old healthy individuals (72% of this group), focusing on \"physical examination report inquiry\" (65%) and \"pediatric appointment registration\" (58%), with only 12% having tried paid consultation services.\u003c/p\u003e\n\u003cp\u003e3. Low-resource and high-barrier type (21.2%): Elderly, low-education, rural residents; usage rate \u0026lt;15%, satisfaction score 2.9. Over-61s account for 83% of this group, including 45% elderly living alone; 78% require assistance from children for registration, and 85% refuse online follow-ups due to \"concerns about online diagnosis safety\".\u003c/p\u003e\n\u003cp\u003eSignificant differences exist among the three groups in health needs (F=36.81, P\u0026lt;0.001) and technology acceptance (F=58.24, P\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3.2 Association Rule Mining\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Apriori algorithm identifies strong association rules:\u003c/p\u003e\n\u003cp\u003e• Rule 1: Intelligent pre-consultation → Electronic medical record card [Support=18.2%, Confidence=85.7%]\u003c/p\u003e\n\u003cp\u003e• Rule 2: Online follow-up consultation → Drug delivery [Support=24.6%, Confidence=89.3%]\u003c/p\u003e\n\u003cp\u003e• Rule 3: Precise appointment + Report inquiry → Health record [Support=15.8%, Confidence=82.1%]\u003c/p\u003e\n\u003cp\u003eThese indicate users prefer synergistic service chains over single services.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3.3 Regression Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOrdinal logistic regression shows:\u003c/p\u003e\n\u003cp\u003e• Promoting factors: High education (OR=1.98), high income (OR=2.15), chronic diseases (OR=2.62), and high perceived usefulness (OR=3.41).\u003c/p\u003e\n\u003cp\u003e• Hindering factors: Age ≥61 (OR=0.31), rural residence (OR=0.57), and low ease of use scores (OR=0.43).\u003c/p\u003e"},{"header":"5 Discussion and Policy Recommendations","content":"\u003cp\u003e\u003cstrong\u003e5.1 Discussion of Key Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDigital divide challenges health equity: This study confirms significant \u0026quot;usage stratification\u0026quot; in Shanghai\u0026rsquo;s Internet hospitals. Elderly over 61 and low-education groups, due to insufficient technical literacy and limited device access, become \u0026quot;low-resource and high-barrier users\u0026quot;, consistent with Ningxia rural research showing the \u0026quot;digital divide is determined by socioeconomic conditions\u0026quot; [22]. Sustained expansion of this stratification may exacerbate health inequalities, contradicting the original intention of hierarchical medical systems [23].\u003c/p\u003e\n\u003cp\u003eMismatch between service supply and demand: Current Internet hospitals focus on follow-up patients and mild symptom consultations, but user needs are diversified. Association rules reveal high popularity of the \u0026quot;intelligent pre-consultation - electronic medical record card - online follow-up\u0026quot; combination (confidence \u0026gt;85%), reflecting patient expectations for full-process service integration. However, tertiary hospitals still monopolize high-quality resources, and community centers have weak smart service functions, restricting the implementation of hierarchical diagnosis and treatment.\u003c/p\u003e\n\u003cp\u003eTechnology acceptance determines sustainability: The TAM verifies that perceived ease of use strongly predicts usage intention (\u0026beta;=0.58, P\u0026lt;0.01), particularly affecting the elderly. Previous Shanghai surveys warned of low elderly satisfaction due to operational difficulties; this study quantifies the impact: the elderly group\u0026rsquo;s ease of use score is 44.2% lower than the young group, directly leading to a 65.7% drop in usage rate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2 Policy Recommendations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2.1 Building an Age-Inclusive Service System\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026bull; Aging-friendly transformation: Implement an \u0026quot;elderly mode\u0026quot; (large fonts, voice navigation, one-click human assistance); establish digital health counselors in community centers to provide operational guidance; expand agency service scenarios by learning from Qingpu\u0026rsquo;s \u0026quot;medicine delivery for elderly and children\u0026quot; experience, with specific measures:\u003c/p\u003e\n\u003cp\u003e(1) Technical level: Develop \u0026quot;voice interaction + simplified graphics\u0026quot; dual-mode interfaces, enlarge fonts to 1.5x default size, remove pop-up ads and complex jumps, and set \u0026quot;one-click return\u0026quot; for key operations.\u003c/p\u003e\n\u003cp\u003e(2) Service level: Set up \u0026quot;Internet Hospital Elderly Assistance Stations\u0026quot; in community health centers, equipped with full-time counselors to provide \u0026quot;registration, appointment, and payment assistance\u0026quot;, achieving 100% community coverage citywide by 2025.\u003c/p\u003e\n\u003cp\u003e(3) Trust-building: Produce \u0026quot;Internet Medical Guide for the Elderly\u0026quot; videos (including dialect versions), popularize \u0026quot;online diagnosis procedures\u0026quot; and \u0026quot;medical insurance reimbursement policies\u0026quot; through commFunity broadcasts and bulletin boards to reduce psychological concerns.\u003c/p\u003e\n\u003cp\u003e\u0026bull; Digital literacy improvement: Health authorities collaborate with communities to launch \u0026quot;smart elderly assistance\u0026quot; training, covering over 80% of communities; produce dialect teaching videos to lower learning barriers.\u003c/p\u003e\n\u003cp\u003e\u0026bull; Device inclusiveness program: Provide subsidized smart devices to low-income elderly, pre-installing Internet hospital quick access.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2.2 Innovating Payment and Incentive Mechanisms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026bull; Deepening online medical insurance payment: Expand cross-regional settlement coverage for diseases and regions, especially in the Yangtze River Delta Demonstration Zone; pilot \u0026quot;online initial consultation\u0026quot; medical insurance payment (currently limited to follow-ups). Achieve \u0026quot;non-discriminatory settlement\u0026quot; of Yangtze River Delta medical insurance by the end of 2025, expand online reimbursement disease coverage (from current hypertension and diabetes to 10 chronic diseases including coronary heart disease and COPD), and pilot \u0026quot;online initial consultation insurance subsidies\u0026quot; with 50 yuan/consultation for common diseases [24,1].\u003c/p\u003e\n\u003cp\u003e\u0026bull; Differentiated pricing: Offer service package discounts to \u0026quot;passive users\u0026quot; (e.g., appointment + consultation combo offers) based on cluster analysis; develop tiered pricing referencing Ningxia\u0026rsquo;s Haodaifu platform range of 65.52\u0026plusmn;67.87 yuan.\u003c/p\u003e\n\u003cp\u003e\u0026bull; Performance incentives: Include Internet hospital service volume in doctors\u0026rsquo; performance evaluations to increase willingness to supply high-quality resources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2.3 Optimizing Service Supply Models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026bull; Integrating service chains: Design \u0026quot;one-stop service packages\u0026quot; (e.g., pre-consultation - registration - follow-up - delivery) based on association rules to reduce operational jumps.\u003c/p\u003e\n\u003cp\u003e\u0026bull; Resource sinking to communities: Promote the Qingpu model by introducing municipal expert resources to community Internet terminals; equip community health centers with telemedicine rooms, achieving 100% coverage by 2025. Establish a three-tier remote collaboration network of \u0026quot;municipal experts - district doctors - community nurses\u0026quot;, requiring municipal experts to complete 20 community remote consultations monthly (added as a bonus item in professional title evaluations); install \u0026quot;remote diagnosis all-in-one machines\u0026quot; (with HD cameras, electronic stethoscopes, etc.) in community centers, maintained uniformly by municipal platforms to ensure technical stability.\u003c/p\u003e\n\u003cp\u003e\u0026bull; Technology upgrading: Apply generative AI to optimize intelligent pre-consultation (e.g., personalized question generation); build regional imaging clouds to achieve \u0026quot;one imaging, universal sharing\u0026quot;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2.4 Supervision and Quality Assurance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026bull; Standardization: Formulate \u0026quot;Internet Hospital Smart Service Evaluation Standards\u0026quot; covering service efficiency, safety, and aging-friendliness.\u003c/p\u003e\n\u003cp\u003e\u0026bull; Dynamic monitoring: Establish a \u0026quot;digital health equity index\u0026quot; to track service utilization among vulnerable groups.\u003c/p\u003e\n\u003cp\u003e\u0026bull; Scalper prevention: Upgrade AI risk control systems (learning from the 2024 experience of blocking 13,994 scalper accounts) to ensure fair distribution of registration resources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2.5 Priorities for Service Supply Optimization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026bull; Short-term (within 1 year): Focus on promoting the \u0026quot;intelligent pre-consultation + online follow-up + drug delivery\u0026quot; service package; open online follow-up permissions for community centers with technical support from municipal hospitals.\u003c/p\u003e\n\u003cp\u003e\u0026bull; Medium-term (2-3 years): Build a \u0026quot;regional health data platform\u0026quot; to realize cross-hospital sharing of electronic medical records, examination results, and medication records, reducing duplicate inspections.\u003c/p\u003e\n\u003cp\u003e\u0026bull; Long-term (3-5 years): Optimize intelligent pre-consultation with generative AI, generating personalized inquiry scripts by learning patients\u0026rsquo; historical medical records, reducing \u0026quot;mechanical question\u0026quot; feedback from 36.8% to below 15%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.3 Research Limitations and Prospects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis study has three limitations:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Research Design:\u003c/strong\u003e This study employed a cross-sectional design, which limits our ability to infer\u0026nbsp;\u003cstrong\u003ecausal relationships\u003c/strong\u003e between the identified factors (e.g., perceived ease of use) and service utilization behaviors. The associations observed may be subject to reverse causality or confounding by unmeasured variables. Longitudinal or interventional studies are needed to establish causality.\u003cbr\u003e\u003cstrong\u003e2. Sampling and Measurement Bias:\u003c/strong\u003e Despite employing a mixed online/offline strategy, our sampling method may still under-represent the most digitally excluded populations (e.g., the oldest-old without any family support, or residents in remote rural areas), potentially leading to an \u003cstrong\u003eoverestimation of overall usage rates and satisfaction\u003c/strong\u003e. Furthermore, the use of self-reported questionnaires is susceptible to\u0026nbsp;\u003cstrong\u003ecommon method bias\u003c/strong\u003e and social desirability bias.\u003cbr\u003e\u003cstrong\u003e3. Scope of Perspectives:\u003c/strong\u003e This study focused on the user (patient) side. The acceptance, motivations, and barriers faced by\u0026nbsp;\u003cstrong\u003ehealthcare providers\u003c/strong\u003e (doctors and nurses) in delivering services through Internet hospitals are crucial determinants of service quality and sustainability, which were not explored here.\u003cbr\u003e\u003cstrong\u003e4. Generalizability:\u003c/strong\u003e The findings are based on data from Shanghai, a highly developed megacity in China. The specific prevalence of service types, user typologies, and the magnitude of the digital divide may not be directly generalizable to cities or regions with different economic, demographic, and healthcare infrastructural profiles.\u003cbr\u003e\u003cstrong\u003e5. Temporal Dynamics:\u003c/strong\u003e The field of Internet hospitals is evolving rapidly. The data collected in 2024 represents a snapshot in time, and the service landscape, user behavior, and policy environment are subject to continuous change.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFuture research directions include:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026bull; AI integration: Exploring the role of large language models (LLMs) in optimizing intelligent pre-consultation.\u003c/p\u003e\n\u003cp\u003e\u0026bull; Yangtze River Delta integration: Studying cross-regional medical insurance settlement and resource sharing mechanisms based on Qingpu\u0026rsquo;s demonstration experience.\u003c/p\u003e\n\u003cp\u003e\u0026bull; Emergency response mechanisms: Improving Internet hospitals\u0026rsquo; functional transformation during public health events (e.g., epidemic early screening).\u003c/p\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eThis study systematically analyzes the smart service status and user behavior of 117 Internet hospitals in Shanghai by integrating the Andersen Health Service Model and TAM. Findings show Shanghai has built a citywide Internet hospital system with over 13.32 million registered users and 149 million cumulative services, but faces challenges in service accessibility, aging-friendliness, and resource balance. Cluster analysis identifies three user groups, with the \"low-resource and high-barrier type\" (21.2%)—mainly elderly and low-education groups—having a usage rate \u0026lt;15%. Association rule mining finds \"intelligent pre-consultation + electronic medical record card + online follow-up\" as the most popular combination (confidence \u0026gt;85%), indicating demand for full-process integrated services.\u003c/p\u003e\n\u003cp\u003eTo promote sustainable development, four core strategies are proposed: building an age-inclusive system, innovating payment incentives, optimizing service supply, and strengthening quality supervision. Through aging-friendly transformation, medical insurance payment innovation, resource sinking, and AI application, service accessibility and equity can be enhanced, making Internet hospitals a strong support for \"Healthy China\" and \"Digital China\" strategies. The findings from Shanghai, as a front-runner, may offer valuable insights for other regions at different developmental stages. For instance, resource-intensive cities might need to place greater emphasis on equity-focused design to avoid exacerbating the digital divide. Regions with more limited resources could prioritize achieving universal access to core services before investing heavily in advanced functionalities. Future research should test the transferability of these findings and strategies in diverse socio-economic contexts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Guilin Medical University (Approval No. GXMU-2024-ETH-078). All participants were informed of the study’s purpose, procedures, potential risks, and benefits prior to participation. Written informed consent was obtained from all participants. For online respondents, consent was obtained electronically via a mandatory confirmation checkbox before proceeding to the questionnaire. Participants were assured of anonymity and confidentiality and were free to withdraw at any time without consequence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study does not contain any individual person’s data in any form (including any individual details, images, or videos).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the following projects:Research on Innovative Training Models for Multi-skilled Professionals in Home-Based Elderly Care Services under the Silver Economy in Guangdong Province: Integration of Medical and Nursing Care (2025GXJK0906)；Research on Innovative Models for Empowering Modern Domestic Service Programs to Provide Services for the Elderly and Children under the 'Hundreds of Thousands' Project (JXJYGC2024D158)；Guangdong Province Educational Science Planning Project (Higher Education Special Program) (2023GXJK908)；2025 Guangdong Province General Colleges and Universities Special Innovation Project (2025WTSCX267).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRong Huang:\u003c/strong\u003e Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review \u0026amp; editing.\u003cstrong\u003eJingliu Huang:\u003c/strong\u003e Conceptualization, Data curation, Methodology, Project administration, Supervision, Writing – original draft.\u003cstrong\u003eTingting Li:\u003c/strong\u003e Conceptualization, Formal Analysis, Methodology, Project administration, Writing – original draft.\u003cstrong\u003eTao Jiang:\u003c/strong\u003e Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Validation, Visualization, Writing – original draft, Writing – review \u0026amp; editing.\u003cbr\u003e\u0026nbsp;All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the Shanghai Municipal Health Commission for providing the data platform support. We are also grateful to all participants who contributed their time and insights to this survey. Additionally, we extend our appreciation to the editors and anonymous reviewers for their constructive comments.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAI:Artificial Intelligence;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCOPD:Chronic Obstructive Pulmonary Disease;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCOVID-19:Coronavirus Disease 2019;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHD:High-Definition;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIRB:Institutional Review Board; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLLMs:Large Language Models;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKMO:Kaiser-Meyer-Olkin (Measure of Sampling Adequacy);\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOR:Odds Ratio;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSPSS:Statistical Package for the Social Sciences; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTAM:Technology Acceptance Model.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLai Y, Chen S, Li M, et al. Policy Interventions, Development Trends, and Service Innovations of Internet Hospitals in China: Documentary Analysis and Qualitative Interview Study. J Med Internet Res. 2021;23(7):e22330. doi:10.2196/22330.\u003c/li\u003e\n\u003cli\u003eXu R, Zhang T, Zhang Q. Investigating the effect of online and offline reputation on the provision of online counseling services: A case study of the Internet hospitals in China. Front Eng Manag. 2022;9(4):692-706. doi:10.1007/s42524-022-0219-z.\u003c/li\u003e\n\u003cli\u003eSi Xiaoping, Shi Chengxia, Zhang Feng, Ding Lacun, Wang Yi, Zhu Yuelan. Analysis and Reflection on Hot Issues in the Construction of Smart Hospitals[J].Modern Medicine and Health,2021,37(17):3040-3043.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Global strategy on digital health 2020-2025. Geneva: World Health Organization; 2021. Available from: https://www.who.int/publications/i/item/9789240020924\u003c/li\u003e\n\u003cli\u003eHan Yangyang,Lie Reidar K,Guo Rui. The Internet Hospital as a Telehealth Model in China: Systematic Search and Content Analysis. [J]. Journal of medical Internet research,2020,22(7). DOI: 10.2196/17995;\u003c/li\u003e\n\u003cli\u003eLiu, J., Liu, S., Zheng, T., \u0026amp; Bi, Y. (2022). The role of internet hospitals in China during the COVID-19 pandemic: A systematic review. Frontiers in Public Health, 10, 1067513.DOI: 10.3389/fpubh.2022.1067513\u003c/li\u003e\n\u003cli\u003eLai, Y., Chen, S., Li, M., Ung, C. O. L., \u0026amp; Hu, H. (2021). Policy Interventions, Development Trends, and Service Innovations of Internet Hospitals in China: Documentary Analysis and Qualitative Interview Study. Journal of Medical Informatics, 23(7), e22330.DOI: 10.2196/22330\u003c/li\u003e\n\u003cli\u003eCzaja, S. J., \u0026amp; Lee, C. C. (2007). The impact of aging on access to technology. Universal Access in the Information Society, 5(4), 341-349.DOI: 10.1007/s10209-006-0060-x\u003c/li\u003e\n\u003cli\u003eAndersen, R. M. (1995). Revisiting the behavioral model and access to medical care: does it matter? Journal of Health and Social Behavior, 36(1), 1-10.\u003cbr\u003eDOI: 10.2307/2137284\u003c/li\u003e\n\u003cli\u003eDavis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.\u003cbr\u003eDOI: 10.2307/249008\u003c/li\u003e\n\u003cli\u003eXia Xianyu. Exploration of Internet Hospital Information System Based on Information Integration Platform[J]. Journal of Physics: Conference Series,2021,1856(1). DOI: 10.1088/1742-6596/1856/1/012059\u003c/li\u003e\n\u003cli\u003eShanghai: Launches a \u0026apos;Internet Hospital\u0026apos; main portal, allowing a smartphone to \u0026apos;visit\u0026apos; major hospitals across Shanghai [Internet]. HIT Expert Network. Source: Shanghai Shenkang Hospital Development Center.2019-12-19 [cited 2026-02-10]. Available from: https://www.hit180.com/49252.html#respond \u003c/li\u003e\n\u003cli\u003eHan, Y., Lie, R. K., \u0026amp; Guo, R. (2020). The Internet Hospital as a Telehealth Model in China: Systematic Search and Content Analysis. Journal of Medical Internet Research, 22(7), e17995.\u003cbr\u003eDOI: 10.2196/17995\u003c/li\u003e\n\u003cli\u003eWang, X., Zhang, Z., \u0026amp; Zhao, J. (2022). The impact of online medical insurance payment on the use of internet hospitals: Evidence from China. Frontiers in Public Health, 10, 943998. https://doi.org/10.3389/fpubh.2022.943998\u003c/li\u003e\n\u003cli\u003eShanghai Shenkang Hospital Development Center. (2021). 2020 Annual Report on the Operation of Shanghai Municipal Hospital Internet Platform.\u003c/li\u003e\n\u003cli\u003eLi Dehe,Hu Yinhuan,Liu Sha,Li Gang,Lu Chuntao,Yuan Shaochun,Zhang Zemiao. The effect of using internet hospitals on the physician-patient relationship: Patient perspective. [J]. International journal of medical informatics,2023,174. DOI: 10.1016/j.ijmedinf.2023.105046\u003c/li\u003e\n\u003cli\u003eAndersen, R. M. (1995). Revisiting the behavioral model and access to medical care: does it matter? Journal of Health and Social Behavior, 36(1), 1-10.DOI: 10.2307/2137284\u003c/li\u003e\n\u003cli\u003eRho, M. J., Choi, I. Y., \u0026amp; Lee, J. (2014). Predictive factors of telemedicine service acceptance and behavioral intention of physicians. International Journal of Medical Informatics, 83(8), 559-571.DOI: 10.1016/j.ijmedinf.2014.05.005\u003c/li\u003e\n\u003cli\u003eCreswell, J. W., \u0026amp; Plano Clark, V. L. (2017). Designing and conducting mixed methods research (3rd ed.). Sage publications.\u003c/li\u003e\n\u003cli\u003eRonghua XU, Tingting ZHANG, Qingpeng ZHANG. Investigating the effect of online and offline reputation on the provision of online counseling services: A case study of the Internet hospitals in China[J]. Frontiers of Engineering Management,2022,9(4). \u003cstrong\u003eDOI: 10.1007/s42524-022-0219-z\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003eYao Huayan, He Ping, Cui Bin, Shi Dongsheng, Xu Bo. China Digital Medicine,2023,18(03):15-19.\u003c/li\u003e\n\u003cli\u003eAn J, Chen FF, Zhao W, Liu GD, Liu Y, Zhang H, Wang ZJ. Exploration of Internet Hospital Information System Based on Information Integration Platform [J]. China Hospital Management, 2020, 40(10): 85-87. \u003c/li\u003e\n\u003cli\u003eKissi, J., Osei-Frimpong, K., \u0026amp; Boateng, R. (2023). The digital divide and health equity: A framework for understanding and addressing the risk of digital technologies exacerbating health disparities. The Lancet Digital Health, 5(4), e214-e223.\u003cbr\u003eDOI: 10.1016/S2589-7500(23)00012-5;\u003c/li\u003e\n\u003cli\u003eWang, X., Zhang, Z., \u0026amp; Zhao, J. (2022). The impact of online medical insurance payment on the use of internet hospitals: Evidence from China. Frontiers in Public Health, 10, 943998.DOI: 10.3389/fpubh.2022.943998;\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Internet hospital, Andersen Model, Technology Acceptance Model, smart healthcare, health equity","lastPublishedDoi":"10.21203/rs.3.rs-8787379/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8787379/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives: \u003c/strong\u003eThis study aims to assess the development of smart services in Shanghai's Internet hospitals and analyze the factors influencing user behavior by integrating the Andersen Health Service Model and the Technology Acceptance Model (TAM), with a focus on addressing service accessibility and health equity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA mixed-methods approach was employed, combining a cross-sectional survey of 1,028 valid questionnaires collected via multi-stage stratified sampling with service data analysis of 117 Internet hospitals in Shanghai. Statistical analyses, including chi-square tests, K-means cluster analysis, association rule mining (Apriori algorithm), and ordinal logistic regression, were conducted using SPSS 25.0 and Python.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003eShanghai's Internet hospital system has achieved full coverage across 37 municipal-level hospitals, with 13.32 million registered users and 149 million cumulative service instances. However, significant usage disparities exist: the elderly population (over 61 years) exhibited significantly lower usage rates and satisfaction compared to younger groups (P\u0026lt;0.01). Cluster analysis identified three distinct user typologies: high-demand and high-adaptability (32.1%), passive usage (46.7%), and low-resource and high-barrier (21.2%). Association rule mining revealed that the service combination of \"intelligent pre-consultation + electronic medical record card + online follow-up consultation\" was the most preferred (confidence \u0026gt;85%). Key influencing factors included education level, income, chronic disease status, perceived usefulness (promoting factors), and advanced age, rural residence, and low perceived ease of use (hindering factors).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003eWhile Shanghai has established a robust Internet hospital infrastructure, challenges related to the digital divide, particularly for elderly and low-resource groups, and a mismatch between service supply and demand persist. To potentially enhance health equity and service sustainability, targeted interventions such as aging-friendly technological transformations could be considered. This study provides evidence to inform policy innovation in online medical insurance payment, optimization of integrated service supply models, and strengthening of quality supervision frameworks.\u003c/p\u003e","manuscriptTitle":"Research on the Current Situation of Smart Services in Shanghai Internet Hospitals and User Behavior—An Integrated Analysis Based on Andersen Model and Technology Acceptance Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-10 18:04:55","doi":"10.21203/rs.3.rs-8787379/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-31T18:13:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-25T16:53:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59470827235217819641437398968090185083","date":"2026-03-17T11:45:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"82894406827861646395321732757162812542","date":"2026-03-15T14:58:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-06T06:25:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"77128971047658769001751013426899677892","date":"2026-03-05T11:14:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-05T10:55:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-04T05:30:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-10T09:24:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-10T07:37:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2026-02-10T07:19:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9bbee563-0f11-4382-ad50-f07a11dcba80","owner":[],"postedDate":"March 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-10T18:04:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-10 18:04:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8787379","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8787379","identity":"rs-8787379","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}