Introduction

The information technology revolution has profoundly reshaped the healthcare industry, revolutionizing how healthcare services are delivered. One of the most notable advancements is the seamless integration of healthcare services, achieved through informatization, which has streamlined every phase of patient care from pre-diagnosis to post-treatment. Automated appointment systems, for example, have significantly reduced waiting times, while electronic medical records (EMRs) have replaced paper-based systems, eliminating the inefficiencies and error risks associated with manual documentation1,2. During treatment, real-time monitoring and data analytics enable healthcare providers to closely track patient conditions, allowing for early identification of potential issues and the timely implementation of preventive measures to reduce medical errors and adverse events3,4. Additionally, the standardization of EMRs and the development of information-sharing mechanisms have enhanced team collaboration and ensured seamless continuity of care across institutions, fostering ongoing assessment and improvement of healthcare quality5,6,7.

Advancements in information technology have also revolutionized telemedicine, significantly enhancing the accessibility and reach of healthcare services. By harnessing the power of the internet and mobile communication, healthcare providers can overcome geographical limitations, offering real-time diagnoses and treatments to patients, regardless of their location. This enables patients to conveniently access professional medical advice through their mobile devices, effectively reducing the need for in-person visits. For example, during the COVID-19 pandemic, many healthcare systems experienced a surge in virtual consultations as patients sought safe ways to receive care8. In particular, the growth of telemedicine has benefited remote and under-resourced areas with limited medical infrastructure9,10. For residents in these regions, telemedicine provides unprecedented access to quality healthcare services, playing a pivotal role in closing the urban–rural healthcare gap and improving overall health outcomes.

Furthermore, the rapid advancement of big data and artificial intelligence (AI) technologies has led to an unprecedented accumulation of data resources in the healthcare industry, driving a significant transformation toward smart health services. In particular, the widespread adoption of wearable medical devices, such as smartwatches and heart rate monitors, along with mobile health applications, has revolutionized personal health management, making it increasingly digitalized and personalized11,12,13,14,15. By collecting and analyzing individual patients’ physiological data, healthcare institutions can tailor customized treatment plans that significantly enhance treatment outcomes16,17. This data-driven approach to precision medicine not only enables healthcare providers to formulate treatment plans more scientifically, but it also minimizes unnecessary tests and interventions, ultimately reducing medical costs and improving overall healthcare efficiency18,19. As a result, the healthcare system is evolving towards more effective and patient-centered care.

Despite the numerous benefits that information technology has brought to the healthcare sector, significant challenges remain, with the digital divide emerging as one of the most pressing issues. This divide refers to disparities in access to technology and digital literacy among various demographic groups. Research indicates that marginalized populations often face barriers to telehealth services due to insufficient Internet access and limited technological skills20. This problem is particularly pronounced in developing countries like China. While China’s healthcare system has made notable strides in informatization, the progress is uneven. Regional disparities in digital infrastructure—particularly between eastern and western regions, as well as between urban and rural areas—impede access to telemedicine services for low-income populations and residents in underdeveloped regions. These challenges are further compounded by China’s aging population, as many older adults struggle to navigate smart devices for health management, placing them at a distinct disadvantage in accessing essential digital medical resources.

The Chinese government has acknowledged the challenges posed by the digital divide in achieving equitable healthcare access and has implemented a range of policy measures to tackle this issue, with the "Healthy China 2030" plan playing a pivotal role. This plan explicitly highlights the significance of digital health in enhancing overall health levels, optimizing healthcare quality, and improving health outcomes21. To support these goals, the government has ramped up investments in healthcare information infrastructure and promoted the widespread integration of cutting-edge technologies like big data and artificial intelligence within the medical sector. Initiatives aimed at bridging the urban–rural digital divide have included fostering telemedicine development, thereby facilitating access to high-quality healthcare resources for residents in remote areas. Additionally, the government has rolled out digital literacy training programs to empower rural and marginalized populations to effectively utilize modern medical technologies. However, despite the good intentions behind these policies, the actual effectiveness of these measures is yet to be determined. Particularly, the sustainability and reach of infrastructure development and technology adoption will require time to assess their long-term impact.

This study aims to examine the impact of informatization development on China’s healthcare services through an empirical analysis utilizing provincial panel data and dynamic spatial panel models. This approach can effectively captures both the temporal and spatial effects of informatization on China’s healthcare landscape. We expect to find specific digital factors that can be harnessed to enhance healthcare efficiency and quality, providing policymakers with actionable insights to develop more effective strategies for reducing the digital divide in healthcare. Additionally, as countries worldwide increasingly adopt digital health solutions, the lessons drawn from China’s experiences can contribute to the global conversation on healthcare informatization, promoting the development of more equitable and effective healthcare systems around the world.

Methodology

Variables and measurements

Dependent variable: healthcare service level (medi)

Measuring the level of healthcare services is a complex process that not only reflects the quality and efficiency of services provided by the healthcare system at a specific time, place, and under certain conditions but also serves as an indicator of the rationality of healthcare resource allocation and the effectiveness of healthcare work. Focusing solely on a single dimension may fail to comprehensively reflect the true level of healthcare services.

To measure healthcare service levels, Kruk et al. introduced a framework with 18 core indicators covering aspects such as safety, effectiveness, and a people-centered approach22. Similarly, Erdebilli et al. proposed frameworks that include factors like patient satisfaction, medical staff expertise, equipment quality, and financial performance23. Additionally, scholars like Pan et al. and Tan et al. have assessed healthcare service levels in terms of human, material, and financial resources, respectively24,25. Drawing on the relevant literature and considering the unique context of China’s healthcare system, this study integrates these indicators into a comprehensive framework and evaluates healthcare services across three key dimensions: human resources, material resources, and financial resources.

In the realm of human resources, this study focuses on several key indicators, including the number of health technicians per 10,000 individuals, the frequency of visits to healthcare institutions, and the volume of inpatient surgeries conducted. These indicators are instrumental in evaluating not only the quantity but also the professional competencies and service capabilities of healthcare personnel. By analyzing these factors, we can gain insights into the rationality and adequacy of human resource allocation within the healthcare system.

In terms of material resources, this study examines several key indicators, including the number of healthcare institutions, the number of hospital beds per 10,000 individuals, and the hospital bed utilization rate. These metrics provide valuable insights into the distribution of medical facilities, the capacity of services offered, and the efficiency with which resources are utilized. By evaluating these indicators, we can better understand the overall infrastructure of healthcare services and identify areas for improvement.

Regarding financial resources, local fiscal healthcare expenditure is chosen as its key indicator. By focusing on this indicator, we can evaluate how funding levels impact healthcare outcomes, efficiency, and the overall effectiveness of community services. This approach can provide a clearer understanding of the relationship between financial investment and health system performance, enabling the identification of best practices and highlighting areas for improvement.

Independent variable: informatization development level (info)

Although previous studies on the evaluation system for China’s informatization development level reveal some variations, most reference the framework established in the national “12th Five-Year Plan” for informationization26. This system, which originated from the “11th Five-Year Plan,” was designed to comprehensively assess and reflect the overall level of informatization development in a country or region27. It takes into account various factors, including the construction of information infrastructure, the degree of information application, environmental constraints, and residents’ information consumption patterns. During the preparation of the “12th Five-Year Plan,” these indicators were further refined, resulting in the selection of metrics across several dimensions: infrastructure, industrial technology, application consumption, knowledge support, and development outcomes.

In this study, an evaluation system was developed based on the framework established in China’s “12th Five-Year Plan” for informationization. This system encompasses three dimensions: information infrastructure, information service consumption, and the information industry. Indicators for information infrastructure include the mobile phone penetration rate and the number of broadband Internet access users per 10,000 people. Information service consumption is assessed through per capita telecommunications service volume. The level of the information industry is evaluated using two indicators: the number of urban employed personnel in software, information transmission, and information technology services per 10,000 people, as well as the number of domestic patent applications accepted per 10,000 people, which reflect human capital and information technology involvement, respectively.

Control variables

To ensure a comprehensive analysis of healthcare services, several control variables were included in this study to account for the influence of other factors. These variables encompass government expenditure, the level of urbanization, population density, and the degree of extroversion to the outside world. By incorporating these indicators, we aim to provide a more nuanced understanding of the various elements that affect healthcare service delivery.

Government Expenditure (Gove) was measured by the proportion of local fiscal healthcare expenditure compared to the total government spending. This ratio highlights the commitment of local governments to healthcare services relative. A higher proportion may indicate a stronger focus on improving healthcare access and quality, while a lower proportion could suggest competing fiscal demands or underinvestment in healthcare. Analyzing this measure can help identify disparities in healthcare funding and inform strategies for optimizing resource allocation to enhance service delivery.

Urbanization Level (Urban) was evaluated by the percentage of a population living in urban areas compared to the total population. Regions with higher urbanization rates typically boast more developed healthcare infrastructure, a greater number of healthcare providers, and broader coverage of healthcare services. As a result, urban residents generally enjoy easier access to high-quality healthcare compared to their rural counterparts. However, rapid urbanization can place significant strain on urban healthcare systems, particularly in the realms of public health and emergency services. This dynamic underscores the need for effective planning and resource allocation to ensure that healthcare systems can adequately meet the demands of growing urban populations.

Population Density (Popu) was measured by dividing the total population of an area by the land area it occupies. Areas with higher population density often experience greater demands for healthcare services, which can pose significant challenges to healthcare systems. This increased density may lead to resource constraints in healthcare, heighten the risk of infectious disease transmission, and contribute to environmental pollution, all of which can indirectly impact residents’ health. Consequently, effective management of healthcare services in high-density areas is essential for enhancing service quality and ensuring the health and well-being of the population.

The Extroversion Degree (Exter) was measured by the ratio of foreign investment converted into RMB to GDP. Extroversion indicates the degree of engagement with the outside world, including international trade, foreign investment, and technology exchange. A higher degree of extroversion can facilitate the introduction and updating of medical technology and knowledge, thereby enhancing local healthcare service levels. For example, international cooperation projects can help introduce advanced medical equipment and management expertise, train healthcare professionals, and consequently improve the quality of healthcare services.

Model construction

Before constructing the model, we tested for endogeneity among the variables to ensure appropriate model selection and specification. Specifically, we conducted the Durbin-Wu-Hausman (DWH) test, which yielded a P-value of 0.273, indicating no significant endogeneity issues. We also assessed spatial effects across provinces using Moran’s I and Geary’s C indices, revealing significant spatial correlations among the variables across regions (Moran’s I = 0.562, p < 0.01; Geary’s C = 0.438, p < 0.01).

It is proposed that healthcare services are evolving dynamically, with the impact of informatization on these services continuously changing, and that both informatization development and healthcare services exhibit strong spatial characteristics. In this context, relying solely on a static panel model may distort the effects, making a dynamic panel model more suitable. Additionally, to better understand the influence of informatization development in other regions on local healthcare services, spatial lag terms for the informatization development indicators are incorporated to assess these spatial interactions. Consequently, the model specification for this study is set as follows:

$$\begin{gathered} Medi_{it} = \beta Medi_{it - 1} + \rho \sum\nolimits_{j = 1}^{n} {W_{ij} Medi_{it} } + \gamma Info_{it} { + }\eta W_{ij} Info_{it} + \delta X_{ijt} + \alpha_{i} + \nu_{it} + \varepsilon_{it} \hfill \\ \varepsilon_{it} = \lambda \sum\nolimits_{j = 1}^{n} {W_{ij} \varepsilon_{it} + \mu_{it} } \hfill \\ \end{gathered}$$

In the equations provided, \(Info_{it}\) represents the explanatory variable of informatization development level, \(Medi_{it}\) represents the explained variable of healthcare services, \(X_{ijt}\) denotes the selected control variables, \(W_{ij}\) denotes the spatial weight matrix, where weights are represented by the reciprocal of geographical distances, and \(\varepsilon_{it}\) represents the random disturbance term.

The evaluation of both informatization development level and healthcare service level indicators employs the entropy weight-TOPSIS method. This method utilizes information entropy to calculate the weights of each indicator, thereby mitigating the subjective weighting flaws and enhancing objectivity and fairness. The entropy weight-TOPSIS method provides a comprehensive reflection of performance across various system layers. Furthermore, this method is computationally straightforward, without necessitating complex mathematical derivations, and has been extensively applied across multiple research domains.

Before conducting the analysis with the dynamic spatial panel model, we evaluated the model’s suitability through such standard procedures as LM, LR, and Wald tests. The LM test statistics were significant at the 5% level, confirming the appropriateness of the spatial panel model for this study. The LR test statistics were significant at the 1% level, indicating that the dynamic spatial panel model could not be simplified to a SAR or SEM model. The Wald test also demonstrated significance at the 1% level, underscoring the superiority of the dynamic spatial panel model over both SAR and SEM models.

Data collection

To ensure the accessibility and reliability of the research data, this study primarily utilized publicly available data from the "China Statistical Yearbook" published by the National Bureau of Statistics, as well as data provided by provincial statistical bureaus. The research sample includes 31 provinces in mainland China, with the study period covering the years 2010 to 2022, accounting for potential lags in data publication. During data processing, some gaps were identified and addressed using interpolation techniques to maintain data integrity.

Regional divisions in the study followed the classification outlined in the “China Statistical Yearbook 2023,” categorizing the country into Eastern, Central, Western, and Northeastern regions. The Eastern region comprises 10 provinces, including municipalities directly under the central government; the Central region consists of 6 provinces; the Western region includes 12 provinces, encompassing autonomous regions and municipalities directly under the central government; and the Northeastern region consists of 3 provinces.

Findings

Descriptive statistics

Table 1 presents the descriptive statistics for each variable. The mean healthcare service level is 0.257, with a standard deviation of 0.145, indicating that healthcare service levels in most Chinese provinces are relatively low, and there are significant disparities and imbalances among provinces. The informatization index shows even more pronounced variation, with a minimum value of 0.013 and a maximum value of 0.877, highlighting substantial differences in the level of informatization development across regions.

Table 1 Descriptive statistical analysis of key variables.
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Figure 1 presents the healthcare service levels across 31 provinces in China from 2010 to 2022, revealing notable regional disparities. While the Eastern and Central regions largely rank in the higher tier, the Western and Northeastern regions mostly fall into the lower tier. These disparities are closely linked to differences in regional infrastructure, technological development, the scale of the healthcare workforce, and population density.

Fig. 1
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Average healthcare service levels across 31 provinces inserted here.

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It is notable that despite Beijing’s advanced economic and political status, its high population density prevents it from ranking at the forefront in healthcare service levels. In contrast, its neighboring province, Hebei, benefits from a more balanced population density and access to high-quality medical resources from Beijing, which contributes to Hebei’s position as one of the top provinces in terms of healthcare service levels. Despite not being among China’s wealthiest provinces, Henan has experienced significant improvements in healthcare service levels. This progress is largely attributed to sustained government efforts aimed at enhancing grassroots healthcare capabilities and strengthening rural medical teams, which have helped raise the overall quality and accessibility of healthcare services in the region.

Provinces such as Tianjin are generally expected to have higher overall economic and public service levels. However, there are apparent disparities in specific healthcare areas compared to other provinces. This may contribute to its relatively lower overall healthcare service level. In addition, provinces like Tibet, Qinghai, and Ningxia, located in the western regions with challenging geographical conditions and limited transportation access, struggle with the development of healthcare services and the equitable allocation of resources. Similarly, Hainan, a southern island province experiencing rapid growth in tourism, has faced increasing pressure on its public healthcare system due to the significantly higher influx of visitors in recent years, which has contributed to lower healthcare service levels.

Moreover, a scatter plot was created to provide a preliminary illustration of the relationship between informatization development and healthcare services. As depicted in Fig. 2, a clear positive correlation between the two variables is evident. This indicates that advancements in informatization development may play a role in enhancing healthcare service levels. However, this initial finding requires further rigorous analysis to confirm.

Fig. 2
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The correlation between informatization development and healthcare services inserted here.

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Panel model estimation

To further investigate the relationship between informatization development and healthcare services, a dynamic spatial panel model is utilized. It aims to clarify the specific impact of informatization development on the improvement of healthcare services, offering a more comprehensive and robust exploration of their relationship.

As shown in Table 2, the coefficients of the spatial lag term (ρ) for healthcare services are positive and statistically significant, indicating clear spatial convergence in healthcare service levels. Also, the significantly positive coefficients of the time lag term for healthcare services imply dynamic continuity in their development over time. Within the dynamic spatial panel model, the spatial lag term of informatization development is included to examine its impact and spatial interactions. The results show that the coefficient for this spatial lag term (W × Info) is significantly negative, indicating that informatization development in neighboring areas hinders the improvement of local healthcare services. This effect may be due to significant disparities in informatization development across regions, where areas with advanced informatization exert a "siphon effect," drawing resources away from less developed areas.

Table 2 Dynamic spatial panel estimation results.
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The impact of government expenditure on healthcare service levels is positive but not statistically significant, which may seem counterintuitive. This can be attributed to two main factors. First, with China’s aging population and rising public awareness of health, the demand for healthcare services has surged sharply. This rapid growth in demand may have outpaced fiscal investments, making them insufficient to meet the immediate needs for healthcare services. Second, the allocation of specialized funds still requires improvement. Government investments often focus too heavily on infrastructure, while equally important areas such as talent development and service process optimization are overlooked. A balanced approach that enhances both facilities and service quality is essential for achieving sustainable improvements in healthcare service levels.

Urbanization levels have a significantly positive effect on healthcare service levels. In the early stages of urbanization, the concentration of resources tends to improve the efficiency and quality of healthcare services while also promoting medical research and technological innovation. Although the rapid growth of urban populations inevitably puts pressure on healthcare systems, China’s urbanization process has prioritized human-centered development. This approach ensures that as urbanization expands, social public services, including healthcare, also improve, preventing resource imbalances caused by excessive population concentration. However, the concentration of healthcare resources in urban areas can lead to unequal distribution between urban and rural regions, resulting in challenges such as inadequate healthcare services in rural areas.

Population density has a significant negative impact on healthcare service levels. In densely populated areas, local healthcare resources and public services, such as doctor-patient ratios and hospital bed availability, come under immense pressure—challenges that are particularly evident in many large cities in China. High population density also increases the risk of environmental health issues, such as inadequate waste disposal and sewage treatment, which can facilitate disease transmission and further strain healthcare systems. While densely populated regions in China are often more economically developed, they also face persistently high living costs. This can make healthcare expenses unaffordable for vulnerable populations, such as migrant workers. Furthermore, densely populated areas encounter greater challenges in disease prevention and health education, underscoring the urgent need for more effective public health awareness campaigns and education initiatives.

The level of extroversion has not significantly contributed to improvements in healthcare services. This can be largely attributed to the continuous advancement of domestic healthcare in China, reducing the country’s reliance on imported medical equipment and technologies. Meanwhile, healthcare reforms in China face both internal and external obstacles, including resistance from vested interests and concerns about the costs and risks associated with reform and opening up, which may hinder significant improvements in healthcare services. In summary, relying solely on foreign technologies may not significantly enhance healthcare service levels. A more pragmatic approach would be to strengthen local healthcare infrastructure and explore development paths tailored to China’s specific needs.

Regional analysis and moderation

Significant differences in economic development, population distribution, public services, and ecological environments among Chinese regions result in varying effects of regional informatization development on healthcare services. Therefore, this study employs spatial heterogeneity testing to analyze these differential effects. Table 3 presents the results of the regional panel model estimation. In the Eastern and Western regions, both the time lag and spatial lag terms for healthcare service levels are significantly negative, while in the Central and Northeastern regions, these terms are significantly positive. This indicates the presence of dynamic and spatial effects on healthcare service improvement within regional contexts.

Table 3 Regional dynamic spatial panel estimation results.
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Moreover, the effects of informatization development vary significantly among regions. In the Eastern, Northeastern, and Western regions, informatization development positively influences healthcare services, although this effect is not statistically significant in the Eastern region. In contrast, informatization development has a significantly negative impact on healthcare services in the Central region. The less significant impact in the Eastern region may be attributed to its early developmental advantage, where well-established healthcare services have already extensively utilized informatization, leading to limited additional benefits from further development. Compared to other regions, the Central region may be trapped in an industrial development phase driven by production factors, resulting in environmental degradation and uneven social public services. Additionally, factors such as residents’ education levels and economic conditions may hinder certain groups from benefiting from informatization development.

The effects of the spatial lag terms for regional informatization development on healthcare service improvement vary significantly. In the Western and Northeastern regions, the spatial lag term of informatization development is significantly positive, indicating that informatization in surrounding regions can enhance local healthcare service levels. This finding suggests that informatization development in the Eastern and Central regions can positively influence healthcare service levels in the Western and Northeastern regions. In the Eastern and Central regions, the spatial lag term for informatization development is not statistically significant, indicating that informatization in these areas may not significantly enhance local healthcare services. This could be attributed to the differing stages of regional development. It is argued that the Eastern region is facing typical developmental bottlenecks and requires revolutionary technological breakthroughs to address its challenges. Meanwhile, the Central region needs innovative allocation of existing resources and ongoing efforts to promote deep industrial transformation and upgrading.

In addition to subgroup analyses on different regional samples, conducting a moderation analysis on the full sample is necessary to gain a more comprehensive understanding of how regional differences impact the relationship between information technology development and the level of healthcare services. To achieve this, the study includes regional dummy variables and their interaction terms with the level of information technology development in the dynamic spatial panel model. In the model, the Eastern region is set as the reference group and three dummy variables introduced for the Central, Western, and Northeastern regions. “Regioni × Info” is the interaction term between the level of informatization development and the regional dummy variable, capturing the moderating effect of regional differences on the relationship between informatization development and the level of healthcare services. As shown in Table 4, the coefficients of the dummy variables for the Central region are significantly negative, suggesting that the level of healthcare services in the central region is generally lower compared to the Eastern region. The coefficients of the dummy variables for the Western and Northeastern regions are not significant. The interaction term between the Central region and the level of information technology development has a significantly negative coefficient, indicating that the positive impact of information technology development on healthcare services is significantly smaller in the Central region compared to the Eastern region. The interaction term between the Western region and the level of informatization development has a significantly positive coefficient, suggesting that the Western region experiences a greater positive impact on healthcare services from informatization development. The coefficient of the interaction term for the Northeastern region is not significant. These findings are generally consistent with the results of the subgroup analyses.

Table 4 Full sample moderation effect estimation results.
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To illustrate the moderating effect of regional differences on the relationship between informatization development and healthcare services, we present the marginal effect diagram (Fig. 3) and the interaction effect diagram (Fig. 4). Figure 3 shows that as informatization development increases, healthcare services improve in the Eastern, Western, and Northeastern regions, while the Central region shows a downward trend. This suggests that the Central region may have faced unique challenges during the informatization process, resulting in less effective improvements in healthcare services compared to other regions. Figure 4 highlights the varying impact of informatization on healthcare services across regions. The marginal effect is strongest in the Eastern region, followed by the Western and Northeastern regions, with the Central region showing the weakest effect. Notably, in the Western region, the improvement effect is insignificant at low levels of informatization, but once a certain threshold is reached, the effect becomes substantial, surpassing that of the northeastern region. This may reflect how informatization development helps overcome the western region’s medical resource challenges. In summary, the moderating effect analysis supports the subgroup regression findings, confirming significant regional differences in the impact of informatization development on healthcare services in China.

Fig. 3
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The marginal effect plot inserted here.

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Fig. 4
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The interaction effect plot inserted here.

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Robustness testing

To assess the robustness of the findings, we tested the models using random effects and fixed effects specifications. As shown in Table 5, the random effects model shows a significantly positive impact of information technology development on healthcare services at the 1% level, which aligns with the findings from the dynamic spatial panel model. The fixed effects model also shows a significantly positive impact at the 1% level, though with a slightly smaller coefficient. This suggests that, after controlling for regional heterogeneity, the positive effect of information technology development on healthcare services is somewhat reduced but still remains significant.

Table 5 The estimation results of random and fixed effects models.
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The Hausman test yields a p-value of 0.0388, rejecting the null hypothesis of the random effects model at the 5% level, suggesting that the fixed effects model is more suitable for this study. This likely reflects the correlation between region-specific heterogeneity and informatization development, which could bias the random effects estimates. Therefore, the fixed effects model better controls for regional heterogeneity and provides consistent estimates. Despite minor differences between the two models, their overall direction and significance remain consistent, strengthening the validity of the findings and offering a better understanding of the relationship between informatization and healthcare services.

Conclusion and discussion

This study systematically examines the mechanisms by which informatization development influences healthcare services in China, using provincial panel data and dynamic spatial panel models to empirically test the impact. The research also explores regional disparities in the effects of informatization development. The findings reveal significant regional differences in healthcare service levels across China, with the Eastern and Central regions forming the top tier, and the Western and Northeastern regions constituting the second tier. Both informatization development and healthcare services exhibit substantial spatial interaction effects, with informatization development having a significantly positive impact on healthcare services overall. However, the effects vary regionally. In the Eastern, Northeastern, and Western regions, informatization development positively affects healthcare services, though the effect in the Eastern region is not statistically significant. In contrast, the Central region experiences a significant negative impact from informatization development on healthcare services. The spatial lag terms of informatization development in the Western and Northeastern regions are significantly positive, indicating that informatization development in the Eastern and Central regions can drive improvements in healthcare services in the Western and Northeastern regions.

To effectively leverage informatization for the improvement of healthcare services in China, this study recommends the following policy actions. First of all, it is essential to increase investment in healthcare information infrastructure and establish a comprehensive medical information standards system. China’s development of healthcare informatization started relatively late and still lags behind developed countries. To close this gap, the government should boost investment to achieve full deployment of hospital information systems, disease prevention and control systems, and public health service systems. Meanwhile, unified standards for data exchange, medical record-keeping, and treatment processes should be established to enhance interoperability among different healthcare systems and improve the efficiency of medical data integration and utilization.

Also, information technology should be actively utilized to optimize the allocation of healthcare resources. Beyond building basic infrastructure, a more advanced application of informatization can enhance resource efficiency through innovative service models. Specifically, the government should prioritize the development of such Internet-based services as telemedicine and online consultation, particularly in under-resourced areas, where technology can transcend geographical barriers to deliver high-quality healthcare. Moreover, utilizing big data and artificial intelligence can unlock valuable insights from medical data, allowing for more accurate predictions of disease trends. This, in turn, provides a scientific foundation for the better distribution of healthcare resources. Such efforts will help to further balance medical resources between urban and rural areas, as well as across different regions.

In addition, healthcare data security must be reinforced during the deployment of healthcare information systems. As healthcare informatization advances rapidly, the risks associated with data breaches and privacy violations have become increasingly urgent. To tackle these challenges, the government should establish a comprehensive legal framework that clearly defines security standards and responsibilities for every stage of data handling—collection, storage, use, and sharing. Additionally, healthcare institutions need to enhance their information security management by hiring skilled professionals and implementing robust security measures. It is expected that a well-structured information security system can help restore public trust in healthcare digitalization, thereby contributing to the ongoing improvement of healthcare services.

As many nations seek to enhance their healthcare systems through digital transformation, this study’s identification of a positive correlation between informatization development and healthcare service levels underscores the potential of investing in digital infrastructure to improve health outcomes. However, policymakers should adopt differentiated strategies that are tailored to local conditions. In developed countries with relatively abundant healthcare resources, China’s experience offers valuable insights. These regions should emphasize the development of advanced healthcare informatization systems and the introduction of innovative service models, such as telemedicine and smart hospitals. These initiatives not only enhance the quality and convenience of healthcare services but also facilitate resource sharing with surrounding less-developed areas. In contrast, less-developed regions with limited healthcare resources should prioritize infrastructure development and the strengthening of informatization capabilities within primary healthcare institutions. This approach mirrors China’s successes in advancing informatization in rural and remote areas.

It is important to recognize that certain limitations may affect the findings of this study and their generalizability. For instance, our reliance on provincial panel data might overlook nuanced local factors influencing healthcare informatization, as city-level variations in economic development, healthcare service, and policy implementation exist within each province in China. Moreover, while the dynamic spatial panel models used in this study are robust, they may not fully capture the complexities of interactions and integration between information technologies and human beings and their cumulative effects on healthcare outcomes. Future research could adopt a more granular approach by incorporating qualitative methods such as interviews or case studies, which would provide deeper insights into the experiences of healthcare providers and patients. Additionally, future research could broaden its scope to include comparisons with other developing nations. This approach would enrich our understanding of best practices and shared challenges in healthcare informatization around the world.