The DRG/DIP payment reform requires public hospitals to achieve refined and real-time cost control. However, traditional models face challenges such as crude accounting, lagging control, data fragmentation, and insufficient clinical collaboration. From the theoretical perspectives of cognitive empowerment and decision optimization, this study explores innovative pathways for generative artificial intelligence (AI) to empower hospital cost control. This technology can enable dynamic accounting of disease-specific costs through multimodal data processing, establish a closed-loop, dynamic control system for the entire process based on knowledge reasoning, and promote collaboration between clinical and cost management through natural language interaction. Our analysis indicates that generative AI has the potential to significantly enhance the precision and efficiency of hospital cost control, providing technical support for operational optimization under the payment reform. Simultaneously, the study identifies core challenges including data security, "hallucination" risks, organizational adaptation, and cost-effectiveness, and proposes corresponding strategies. The "cognition-decision" integrated framework constructed in this study aims to guide cost control to extend from end-point control to the optimization of front-end clinical behaviors, offering theoretical reference and practical guidance for generative AI to empower value-based healthcare and facilitate the lean management of clinical pathways.

The rapid advancement of artificial intelligence (AI), particularly generative AI (GenAI), is profoundly reshaping the research paradigms and practical approaches of health technology assessment (HTA). AI has already demonstrated significant potential across multiple core components of HTA, including the systematic literature reviews and evidence synthesis, the automatic extraction and analysis of clinical evidence (encompassing both randomized controlled trials and real-world data), and the intelligent development of health economic models. This review systematically examines recent global and domestic advances in the application of AI in HTA, critically analyzes key challenges related to scientific rigor, data privacy, algorithmic bias, regulatory frameworks, and technical workforce capacity, and proposes a localized implementation pathway for AI-enabled HTA in China, leveraging China's unique strengths in its national healthcare security system, data ecosystem, and policy environment. The aim of this article is to provide a reference for the standardized application, guideline development, and policy integration of AI in HTA within the Chinese context.

In the context of continuous optimization and improvement of healthcare system, medical institutions play an increasingly prominent role in medical insurance fund supervision. To effectively curb inappropriate fund expenditures, medical institutions actively utilize management advantages and advanced technologies to conduct pre-auditing of medical insurance expenses. However, traditional manual auditing methods is difficult to meet the regulatory requirements of medical insurance management departments, while emerging embedded intelligent auditing systems face limitations such as system delays and impact on core business operations in high-concurrency scenarios at large tertiary hospitals. Addressing these problems, this study explores new models for intelligent medical insurance auditing, independently developing a distributed auxiliary tool for intelligent medical insurance auditing. This tool employs a non-embedded design that fully considers the actual capacity of large tertiary medical institutions, significantly enhancing the automation and intelligence of the auditing process while avoiding impacts on hospital information systems' availability and sustainability, thereby achieving optimization of auditing efficiency and accuracy. This research elaborates on the independent distributed medical insurance intelligent audit auxiliary model, systematically analyzes the technical solutions and implementation paths of the auxiliary tool, and comprehensively evaluates its actual operation combined with key data. It summarizes a set of replicable practical experience to better serve medical insurance fund supervision work.

In the new stage of achieving universal healthcare coverage and the full completion of the "National Unified Healthcare Information Platform" in China, data quality and cross-domain collaboration have become crucial for improving management efficiency and effectiveness. Blockchain technology, with its characteristics of "immutability, traceability, and distributed consensus", can serve as a trusted data infrastructure, supporting process optimization and system upgrades in healthcare security management. This study posits that the value evolution of blockchain in healthcare security management follows the pathway of "data credibility - process optimization - organizational collaboration - ecosystem co-governance". At the current stage, adopting a consortium blockchain architecture is the most feasible approach, though it requires overcoming core bottlenecks such as cross-chain interoperability and privacy computing. Its foundational application scenario involves constructing a nationally unified "Healthcare Security Master Ledger" (one record per individual, one record per enterprise), which in turn supports advanced applications such as cross-provincial business collaboration, provincial-level coordination, and intelligent settlement and reconciliation. In the future, through integration with big data and AI, a healthcare security intelligent system characterized by a "trusted data lake plus smart contracts" can be formed, enabling closed-loop business management. This paper ultimately proposes an implementation pathway of "system guidance - standards first - scenario-driven - ecosystem co-construction", and offers recommendations on data ownership, the validity of smart contracts, cross-chain supervision, and talent cultivation, aiming to provide theoretical and technical references for the digital transformation and high-quality development of healthcare security management.

With the rapid development of medical insurance informatization, efficient processing of massive medical insurance data has become one focus of informatization construction. This article explores the application of KMP algorithm in the field of medical insurance, elaborating on the principles, workflow, and advantages of KMP algorithm in string matching, and analyzes its mathematical principles. Through case experiment data display and comparison with traditional algorithms, the implementation principles and advantages of KMP algorithm in core business processes such as medical insurance data matching, cost accounting, and fraud detection have been demonstrated, which provides strong technical support for the efficient operation of medical insurance information systems and precision medical services, and helps to carry out medical insurance informatization more accurately and efficiently.

As one of the crucial drivers for the development of new quality productive forces, healthcare security data holds significant potential to stimulate economic growth through comprehensive value extraction. At present, the healthcare security sector focuses on strengthening data governance to promote data elementization, establishing secure and trustworthy data circulation mechanisms, and fostering deep integration of healthcare security data with emerging technologies such as artificial intelligence, in order to promote the deep transformation and upgrading of industries. This paper takes the construction of a trusted data space in the healthcare security industry as an example, introduces how to establish an open and supervisable data circulation system oriented towards practical application scenarios based on comprehensive data supervision, and promotes the value mining of healthcare data within a compliant framework. The data space construction can effectively improve the level of healthcare governance, and create fresh opportunities for the medical industry's advancement, injecting new momentum into China's new development paradigm.

Implementing payment based on diseases types for traditional Chinese medicine (TCM) advantageous diseases is an effective way to explore the payment mode of medical insurance in line with the characteristics of TCM, which is of great significance to promote the inheritance and innovation of TCM. The rise of big data and artificial intelligence technology has brought new opportunities for the development of innovative TCM. In view of the limitations existing in the current implementation of payment by disease types of TCM advantageous diseases, which leads to the problem that the advantages and value of TCM treatment are not fully reflected, this paper proposes to integrate digital intelligence technology to build a basic data management system, therefore achieving the expression of data as structured information, improving the quality of TCM diagnosis and treatment data through data homogenization research, and using modern digital intelligence technology to mine, analyze and transform TCM human experience, so as to explore a new strategy to support payment by disease types of TCM advantageous diseases. Through a series of measures, we hope to provide reference for the development of medical insurance payment for TCM advantageousdiseases, and promote the sustainable and high-quality development of TCM.

With the rapid growth of multi-indication drugs on the market, designing access assessment methods for multi-indication drugs is a new challenge for the adjustment of the National Catalog of Drugs Covered by Basic Medical Insurance. This paper believes that the management of multi-indication drug by generic name is still the mainstream mode of medical insurance management in China at present. The assessment method system of “simple adjustment calculation method” as the main and “complete actuarial method” as the supplement should be built. When the indications of drug meet the standard of "one indication with large population and multiple indications with small population", simple adjustment calculation method is applied. The simple adjustment calculation method means "one reference drug, one measurement, and adjustment by population", which can control the fund expenditure effectively and fit into the current medical insurance management structure. When the indications of drugs are roughly equal in population, the complete actuarial method is applied. The complete actuarial method means "multiple reference drugs, multiple measurements, weighted average", fully reflecting the value orientation.

In order to solve the problems of provincial drugs and medical consumables centralized procurement platform, such as false orders and easy data tampering, a design scheme of the provincial drugs and medical consumables centralized procurement platform based on blockchain technology is proposed in this paper. By analyzing the characteristics of blockchain technology and combining with the needs of the medical industry, the article describes the architecture, key technologies and implementation path of the provincial centralized procurement platform, to provide technical support for the reform of centralized medical procurement, improve the efficiency of procurement and settlement, and ensure the security of medical procurement and settlement data.

One of the core responsibilities of health insurance agency is to supervise doctors who accept insurant of basic health insurance. The paper takes certified doctors who work in outpatient department of designated medical institutions in Shanghai as the research object, conducts data mining of their behaviors, and builds doctor profile based on four types of data, including personal information, doctor certificates, physician round and patient information to present a unified view of patterns. Meanwhile, the paper explores the laws of medical health handling through three steps of model building, model validation and empirical analysis, in order to standardize doctors’ behavior and protect the healthcare security fund.

Shanghai Healthcare Security Bureau has formulated relatively complex payment rules for medical consumables, most medical institutions currently determine, set medical insurance payment methods for medical consumables using manual approaches. With the increasingly sophisticated requirements of medical insurance supervision, manual approaches can no longer meet the management needs of medical institutions. By studying the links between the catalogue of medical consumables, medical insurance payment rules, establishing databases, the paper designs logical judgment rules to explore core algorithms suitable for informatization development, providing a design basis for the establishment of an intelligent audit system for medical consumables medical insurance payment settings

At present, the platform-led digital transformation of the medical insurance claim management and fund administration system has achieved significant milestones. However, the challenge of digital formalism has emerged as a notable issue in the process of digital governance within the medical insurance system. This paper examines specific practice during the construction and development of the national medical insurance information platform, recognizing the achievements while addressing the problems. It categorizes the phenomenon of digital formalism into three types: upward accountability due to the ecological constraints of top-level design, algorithmic accountability stemming from the malfunctioning use of digital technology, and organizational accountability resulting from the paradigm shift driven by the platform. By critically examining the evolution of digital formalism in healthcare security governance from an academic perspective, we can deconstruct the generative logic underlying contemporary platform-driven digital formalism. Through the lenses of algorithmic imagination, digital labor, and game of data factor, it becomes evident that the "platform-driven government" tends to extensively connect multiple stakeholders, yet faces numerous challenges in achieving collaborative governance. Transitioning from being a media tool for integrated planning, to a technical means for enhancing overall administrative efficiency, to a governance mechanism for public organizations, the platform serves not only as a digital infrastructure for building the medical insurance governance system but also as a reconfigurator of the associated social and production relations. Exploring the optimization of the platform in medical insurance management and administration services will aid in advancing the modernization of the governance capabilities of the medical insurance information platform.