Background: The integrated health management system (IHMS), which unites all health care–related institutions under a health-centered organizational framework, is of great significance to China in promoting the hierarchical treatment system and improving the new health care reform. China’s IHMS policy consists of multiple policies at different levels and at different times; however, there is a lack of comprehensive interpretation and analysis of these policies, which is not conducive to the further development of the IHMS in China. Objective: This study aims to comprehensively analyze and understand the characteristics, development, and evolution of China’s IHMS policy to inform the design and improvement of the system. Methods: We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to collect 152 policy documents. With the perspective of policy tools and policy orientation as the core, a comprehensive 6D framework including policy level, policy nature, release time, policy tools, stakeholders, and policy orientation was established by combining the content of policy texts. These dimensions were then analyzed using content analysis. Results: First, we found that, regarding the coordination of policy tools and stakeholders, China’s IHMS policy was more inclined to use environment-based policy tools (1089/1929, 56.45%), which suggests a need for further balance in the internal structure of policy tools. Attention to different actors varied, and the participation of physicians and residents needs further improvement (65/2019, 3.22% and 11/2019, 0.54%, respectively). Second, in terms of level differences, Shanghai’s IHMS policy used fewer demand-based policy tools (43/483, 8.9%), whereas the national IHMS policy and those of other provinces and cities used fewer supply-based tools (61/357, 17.1% and 248/357, 69.5%, respectively). The national IHMS strategy placed more emphasis on the construction of smart health care (including digital health; 10/275, 3.6%), whereas Shanghai was a leader in the development of healthy community and healthy China (9/158, 5.7% and 4/158, 2.5%, respectively). Third, in terms of time evolution, the various policy tools showed an increasing and then decreasing trend from 2014 to 2021, with relatively more use of environment-based policy tools and less use of demand-based policy tools in the last 3 years. The growth of China’s IHMS policy can be divided into 3 stages: the disease-centered period (2014-2017), the e-health technology development period (2017-2019), and the health-centered period (2018-2021). Conclusions: Policy makers should make several adjustments, such as coordinating policy tools and the uneven relationships among stakeholders; grasping key policy priorities in the context of local characteristics; and focusing on horizontal, multidimensional integration of health resources starting from the community. This study expands the objects of policy research and improves the framework for policy analysis. The findings provide some possible lessons for future policy formulation and optimization.

Background: The application of big data resources and the development of medical collaborative networks (MCNs) boost each other. However, MCNs are often assumed to be exogenous. How big data resources affect the emergence, development, and evolution of endogenous MCNs has not been well explained. Objective: This study aimed to explore and understand the influence of the mechanism of a wide range of shared and private big data resources on the transaction efficiency of medical services to reveal the impact of big data resources on the emergence and development of endogenous MCNs. Methods: This study was conducted by administering a survey questionnaire to information technology staff and medical staff from 132 medical institutions in China. Data from information technology staff and medical staff were integrated. Structural equation modeling was used to test the direct impact of big data resources on transaction efficiency of medical services. For those big data resources that had no direct impact, we analyzed their indirect impact. Results: Sharing of diagnosis and treatment data (β=.222; P=.03) and sharing of medical research data (β=.289; P=.04) at the network level (as big data itself) positively directly affected the transaction efficiency of medical services. Network protection of the external link systems (β=.271; P=.008) at the level of medical institutions (as big data technology) positively directly affected the transaction efficiency of medical services. Encryption security of web-based data (as big data technology) at the level of medical institutions, medical service capacity available for external use, real-time data of diagnosis and treatment services (as big data itself) at the level of medical institutions, and policies and regulations at the network level indirectly affected the transaction efficiency through network protection of the external link systems at the level of medical institutions. Conclusions: This study found that big data technology, big data itself, and policy at the network and organizational levels interact with, and influence, each other to form the transaction efficiency of medical services. On the basis of the theory of neoclassical economics, the study highlighted the implications of big data resources for the emergence and development of endogenous MCNs.

Activating prior medical knowledge in diagnosis and treatment is an important basis for clinicians to improve their care ability. However, it has not been systematically explained whether and how various big data resources affect the activation of prior knowledge in the big data environment faced by clinicians. The aim of this study is to contribute to a better understanding on how the activation of prior knowledge of clinicians is affected by a wide range of shared and private big data resources, to reveal the impact of big data resources on clinical competence and professional development of clinicians. Through the comprehensive analysis of extant research results, big data resources are classified as big data itself, big data technology and big data services at the public and institutional levels. A survey was conducted on clinicians and IT personnel in Chinese hospitals. A total of 616 surveys are completed, involving 308 medical institutions. Each medical institution includes a clinician and an IT personnel. SmartPLS version 2.0 software package was used to test the direct impact of big data resources on the activation of prior knowledge. We further analyze their indirect impact of those big data resources without direct impact. (1) Big data quality environment at the institutional level and the big data sharing environment at the public level directly affect activation of prior medical knowledge; (2) Big data service environment at the institutional level directly affects activation of prior medical knowledge; (3) Big data deployment environment at the institutional level and big data service environment at the public level have no direct impact on activation of prior knowledge of clinicians, but they have an indirect impact through big data quality environment and service environment at the institutional level and the big data sharing environment at the public level. Big data technology, big data itself and big data service at the public level and institutional level interact and influence each other to activate prior medical knowledge. This study highlights the implications of big data resources on improvement of clinicians’ diagnosis and treatment ability.

It has been controversial whether the matching of learning styles with teaching environment has improved the teaching effects. This paper constructs matching modes by choosing Sternberg's three learning styles (liberal leaning, internal scope and global level) and adopts curriculum comprehensiveness and instructing modes. The research, based on data of off-line teaching collected from 456 college students in China, finds that (a) direct effect: three learning styles have significant effects on eight dimensions of learners’ cognitive information processing (CIP); (b) matching effect: the alignment of learners’ liberal leaning and instructing modes has a direct impact on the eight dimensions of learners’ CIP；the alignment of learners’ global level and curriculum characteristics has a direct impact on the 7 dimensions；the alignment of learners’ internal scope and instructing modes have no significant direct impact on the 7 dimensions; (c) moderating effect of the mandatory use of IT (MUIT): MUIT may restrain or strengthen effects of some matching modes on some dimensions of CIP. The key conclusion is that whether personalized instructing based on learners’ characteristics can improve teaching results requires the selection of appropriate matching modes. Not all matching modes have matching effects, and matching effect is influenced by MUIT.

This study aims to explore the difference between on-line and off-line teaching and comprehensively evaluate the of elearning system operation effect as both technology-mediated learning and an open information system. This study develops “process-situation-result” (PSR) network, a comprehensive learning system evaluation model. It empirically analyzes the PSR models under off-line and on-line teaching, as well as the intermediary role of e-learning situation in the impact of e-learning process on elearning results. The results show: (1) Learners' perception of e-learning situation is obviously different between off-line and on-line teaching. (2) The eight dimensions of e-learning process have no significant direct impact on e-learning outcome Except in on-line teaching groups, SF (getting self-feedback) has a significantly positive influence. In off-line teaching, GA (gaining attention), IO (informing learner of the objectives), and PG (providing learning guidance) have indirect effects on e-learning. (3) GA, IO and PG have a direct impact on e-learning situation in both on-line and off-line teaching, and only RK (Stimulating recall of prior knowledge) has an impact only in on-line teaching. The other four dimensions have no direct impact on e-learning We provide a theoretical framework for the integration of technology and teaching, on-line teaching and off-line teaching.

Purpose: The purpose of this paper to explore a set of methods to formally display and analyze the nature and laws of the emergence and evolution of all kinds of medical collaborative practices (MCPs) within multi-level market.Methodology: Based on Yang’s neoclassical economics, the paper argues that the reason for the origin of MCPs with multi-level market is the division of labor for different medical services and medical transaction service. From an infra-marginal perspective, the study processes are (1) to build a role generation model for collaborators. (2) to determine the role boundaries of collaborators by calculating the corner solution, and formalize the entanglement between role identities and specific MCPs through the corner equilibrium analysis. (3) to evaluate the stability of different forms of MCPs by general equilibrium analysis, and analyze their possible evolutionary paths of various MCPs by sensitivity analysis of the stability conditions of different structures. Results: (1) A role generation model of "producer-consumer", which is composed of three functions and involves three economic parameters, reflects the coexistence of division and cooperation, and integrate the influencing factors with process views. (2) The paper extracts eight role models and six structures of MCPs with corner equilibrium, and further formalizes the different ways of legalization of specific role identities in different MCPs in no market, or single-level or multi-level market. (3) Three structures (structure A with no market, structure CN with single-level market and structure CC with two-level market) have global equilibrium in different parameter spaces. With the increase of learning cost of medical services and medical transaction services, there are two structural evolution paths more likely to occur: " A - >CN -> CC " and "CN -> CC ->A".Conclusions: The research will provide theoretical basis for the government to select, formulate and implement the relevant policies of specific MCP, and help IT companies better develop internet medical service transaction platforms.