As a foundational strategic resource for the nation, healthcare data plays a vital role in safeguarding public health and advancing the development of the medical and health sector through its secure and efficient utilization. Data classification and grading constitute a critical component in healthcare data security governance and the capitalization of data assets. This paper elaborates on the conceptual framework, objectives, and significance of data classification and grading. It provides an in-depth analysis of the background, necessity, unique characteristics, challenges, and regulatory trends pertaining to data classification and Grading in the healthcare industry. Key technologies and core operational workflows are systematically introduced, followed by an examination of practical implementation strategies within medical and health institutions. Furthermore, recommendations for enhancing data classification and grading capabilities are proposed. Finally, the limitations of this study are identified.

The development of big data and artificial intelligence technology has driven an increase in the asset and application value of healthcare data, and the risks of data leakage, tampering and illegal utilization have also increased. China's data security governance has formed a top-level design at the policy level, but data classification and grading in the healthcare field based on security governance is still in the exploratory stage. In this paper, based on the data security governance work of a grade A class three hospital, we explore the theoretical basis, technical points and practical path of data classification and grading, construct a hospital data classification standard that contains 6 categories, 25 subcategories and 44 subcategories, and data grading rules based on level 1~5, complete the data asset sorting of about 120 sets of information systems in the hospital, and establish a variety of algorithmic combinations to realize the classification and grading automation, and the algorithmic model embedded in the data. The classification is automated, and the algorithm model is embedded in the data security monitoring service platform, which effectively improves the capacity and efficiency of hospital data security governance by real-time monitoring of the systematic distribution and flow of different types and levels of data.

Based on the trend of refined data security management, this study will start from the data governance requirements, integrate the security compliance requirements, and explore the application of data security classification in the level of refined data management. According to the relevant standards of the medical industry, the practice process of classification and classification was explored through business research, asset clarification, data dictionary improvement and standard formulation. After data classification management, hospitals can reasonably allocate medical resources through fine study of specific categories of data; after hierarchical management based on data importance and sensitivity, hospitals can better identify and evaluate the risks of various data, which helps to formulate targeted risk management strategies for different levels and ensure the improvement of compliance of hospitals in laws and industry standards. Classification and grading not only optimize data storage and access, improve management efficiency, but also significantly reduce the risk of data leakage. It not only provides support for the rational allocation of hospital medical resources and scientific research innovation, but also achieves the purpose of comprehensively improving data security management.

Healthcare data possesses immense value for scientific research and translational utilization. The integration, mining, sharing, and utilization of data will emerge as the future direction and trend of development. However, due to the sensitivity and uniqueness of healthcare data, the elementization of healthcare data still encounters significant difficulties and challenges. Notably, the industry's lack of a mature data management system and the absence of a scientific and reasonable classification and grading system for data have emerged as significant obstacles hindering the practical exploration of healthcare data elementization. Based on the investigation and analysis of 135 hospitals' healthcare data elementization, combined with the industry characteristics and application scenarios, this paper proposes the implementation path for constructing the data classification and grading system, and offers reference to overcome the challenges of healthcare data elementization.

This study aims to investigate the application of large language models (LLMs) and retrieval-augmented generation (RAG) technology in the hierarchical classification of medical data, addressing key challenges in medical data governance, including the processing difficulties of structured, semi-structured, and unstructured data, inefficiencies in sensitive data identification, and delays in dynamic knowledge updates. By harnessing the advanced capabilities of LLMs in processing large-scale datasets and the continuous algorithmic optimization afforded by deep learning, we enhance the intelligence of decision-making and predictive performance in data classification and grading engines. Furthermore, the integration of RAG as a localized knowledge base improves the accuracy and reliability of DeepSeek's applications in medical data scenarios through enriched retrieval of domain-specific knowledge. Through iterative advancements in model architecture, retrieval techniques, and self-learning mechanisms, the proposed framework achieves an 80% improvement in classification efficiency and elevates recognition accuracy to over 90%, significantly optimizing the precision and operational efficacy of medical data classification and grading. The proposed methodology offers a highly interpretable and dynamically adaptive technical solution for medical data governance, demonstrating substantial potential for practical implementation in critical areas such as data asset cataloging and secure data sharing.

To investigate methods and technologies based on datasets and scenarazation for achieving effective practice and efficient application of health data classification and grading. We analyzed the challenges and requirements in health data management and explored methods, technologies and applications for health data classification and grading based on datasets and scenarazation. Practical implementation in the classification and grading of electronic medical records and electronic health records in public health specialty hospital demonstrated that the proposed methods and technologies based on datasets and scenarization are scientific and effective. The health data classification and grading methods and technologies based on datasets and scenarazation provide a compliant and efficient foundational framework for data security, data circulation, and the capitalization of data elements.

This article introduces 5 key technologies (representation, fusion, transformation, alignment, and collaborative learning), 6 application steps (determining application scenarios, selecting modal data, optimizing algorithm models, preparing data, training and validating models, deploying and implementing), and 2 basic environments (computing power infrastructure, algorithm model tools) for medical multimodal data machine learning, providing readers with reference for research and application of medical multimodal data machine learning.

A study was conducted on the application status of clinical pathways in a grade A class three hospital from 2014 to 2023. Through multi-level and multi-dimensional data analysis and utilization, targeted improvement strategies for clinical pathway implementation were proposed. A comprehensive comparative analysis of clinical pathway application data from 2014 to 2023 was performed. Patient data meeting the criteria for the "age-related cataract" clinical pathway were extracted. A t-test was used to compare hospitalization days and costs between patients who completed the pathway and those who did not. Patients who completed the clinical pathway exhibited significantly shorter hospitalization days and lower hospitalization costs compared to non-completers, with statistically significant differences (P＜0.05). However, existing issues include insufficient awareness of clinical pathways among medical staff, poor integration of information systems, underutilization of information platforms, and a lack of effective supervision mechanisms. Clinical pathways play a significant role in improving medical quality and reducing healthcare costs. To address current challenges, it is essential to strengthen policy advocacy, optimize information systems, enhance platform construction, and refine incentive mechanisms. These measures will promote the advancement of medical service quality in hospitals under the digital medicine framework. 

To comprehensively analyze the internal and external environmental factors on the construction of the paperless office system for medical processes, and to propose improvement measures for advancing the construction of the system, as well as to evaluate its implementation effects. Using the SWOT analysis, the advantages, disadvantages, opportunities and threats in the construction of the system are analyzed in depth, formulate construction strategies and implement them. Compared to the traditional office work of 2018—2019, paperless office operations in 2022--2023 saved 5.28 million yuan in printing supplies and foundational services for medical record digitization. The number of Grade A cases is significantly improved, and the differences are all statistically significant (P < 0.05). The quality control rate of discharged medical records is significantly improved, from 10.57% to 100%, and the differences are all statistically significant (P＜0.05). The construction of paperless office system has an important role in saving hospital operation and management costs, improving medical service effectiveness, enhancing patient medical checkups, and promoting regional medical information sharing, etc. SWOT analysis provides comprehensive and in-depth theoretical support for the construction of the system, which becomes a key link in optimizing the management process of the hospital and empowers the promotion of the high-quality development of the hospital. 

To develop a medical insurance mobile payment system based on electronic medical insurance certificate, enabling real-time online medical insurance settlements for patients. The mobile payment end and payment service platform are connected through Web Service interfaces, the medical insurance settlement service end and payment service platform interact through HTTP, and the payment service platform is connected to the HIS database using stored procedures. The system enables real-time online medical insurance settlements for urban employees and patients with chronic/special diseases, incorporating full-process functions including online identity authentication via electronic medical insurace certificate, medical insurance payment ordering, settlement processing, and refund management. Post-implementation, the system allows patients to complete medical insurance settlements via mobile devices without queuing, effectively reducing in-hospital waiting time, alleviating pressure on billing counters, and significantly improving the overall hospital experience.

To explore the application effect of critical care information management system in the nursing work of intensive care unit. The overall architecture of the system was constructed with multi-component, loosely coupled three-layer architecture, layered modular architecture design, and distributed network deployment scheme to ensure system operation. A total of 300 critically ill patients in the department from January 2023 to January 2024 were selected and divided into observation group and intervention group before and after the application of the critical care system. The observation group adopted HIS system model, and the intervention group adopted the critical care information management system model on the basis of the observation group. The implementation of various nursing indicator between the two groups was compared, including nursing form writing, drug registration time and catheter expiration maintenance accuracy. The correct rate of nursing event writing in the intervention group was significantly higher than that in the observation group, χ2=19.107 (P＜0.01), and the errors in the statistics of input and output, the execution of nursing plan and the execution of medical orders were significantly reduced, χ2=28.073, χ2=28.350, χ2=9.729 (P＜0.01). The writing time of nursing form and drug registration time were reduced. t=19.768, t=21.736 (P＜0.05), the accuracy of catheter expiration maintenance is improved, χ2=10.983 (P＜0.01). The application of critical care information management system in ICU can improve the accuracy of event writing, reduce the number of nursing plan execution and doctor order execution errors, improve the accuracy of catheter expitation maintenance, and ensure the quality of clinical nursing. 

To enhance the quality of medical record front-page in a multi-institutional ophthalmic hospital group, this study investigated the development and practical value of an efficient, group-oriented quality control system tailored to ophthalmic specialties. Relying on the self-developed medical cloud platform of an ophthalmic hospital group, we established a medical record front-page quality control system that seamlessly integrated with the hospital's various information systems, aligning with DRG/DIP policy requirements and clinical workflows. The efficacy of this quality control system was evaluated across 50 institutions within the group. The system was successfully applied in multiple hospitals of the group. The completeness of medical record front-page information markedly increased (DRG cohort: χ²=17909.612, P＜0.05; DIP cohort: χ²=9284.538, P＜0.05), the accuracy rates of primary diagnosis selection, code assignment, as well as major procedure selection and coding were significantly improved (P＜0.05). The constrution and application of this quality control system effectively enhanced data quality and provided strong support for the smooth implementation of DRG/DIP reforms in the ophthalmic 
hospital group.

In the context of payment by Diagnosis-Intervention Packet (DIP) medical insurance payment, the construction path and method of ICD coding quality control index system were explored in depth, aiming to improve the management efficiency and data quality of the hospital under the DIP mode. Based on the requirements of hospital administrators, department heads, and medical record administrators, we conducted scientific scoring and adjusted the evaluation weights of various quality control indicators to establish an evaluation scheme for the ICD coding quality control index system. We construct a set of ICD coding quality control index system, and objectively present the current situation of the hospital in terms of ICD coding quality and medical record management level through in-depth analysis of the relevant data of the three wards and the whole hospital. The application of the ICD coding quality control index system provides a scientific basis for the refined management and sustainable development of the hospital, and promotes the high quality sustainable development of the hospital.

To develop an intelligent Traditional Chinese Medicine (TCM) constitution recommendation system based on multimodality, and explore the effective way to combine multimodal information with constitution and health preservation recommendation, in order to improve the accuracy of Traditional Chinese Medicine (TCM) constitution identification and personalized health management. Improve constitution questionnaire and collect basic information such as user's age, height, weight, etc, record their symptomatic manifestations and past (disease) history in detail, and analyze and process them together with tongue and face images to optimize the existing constitution prediction rules. User's constitution, BMI, tooth marks on the tongue, cracks on the tongue, tongue color, thickness of tongue coating, tongue coating color, and face color can be accurately displayed. Based on the above analyses,  further personalized health preservation program covering some dimensions such as massage health preservation, exercise health preservation, food health preservation, medicinal diet health preservation, health preservation gongfu, and music health preservation (with MP3 playback function) are recommended. The construction of intelligent TCM constitution recommendation system based on multimodality realizes the qualitative analysis of constitution cold and heat and completes the accurate quantification of constitution characteristics through the comprehensive analysis of user's multidimensional information, and in the recommendation decision-making link, the system combine user's real-time health status and historical data characteristics through the dynamic reasoning mechanism to realize the adaptive generation and dynamic optimization of personalized health preservation recommendation.The system can provide effective technical support and solutions for the construction of Traditional Chinese Medicine (TCM) intelligent health management system.

Establish acceptance standards for hospital informatization construction projects, standardize the acceptance work of informatization construction projects in secondary and above hospitals, and improve the quality of informatization project construction and application effects. Through research methods such as literature research, standard and policy research, model construction, field investigation, and expert consultation, a set of acceptance standards for hospital informatization construction projects has been formulated. This study breaks down hospital informatization projects into three major dimensions: infrastructure, software construction, and services. Different acceptance quality assessment models are constructed for their distinctive features, and dynamic acceptance process norms are also provided. This research outcome not only provides a reusable acceptance methodology for hospital informatization construction, but also promotes the standardized development of the medical industry from the top-level design perspective. By strengthening standard constraints and process supervision, it can effectively avoid construction risks, improve resource utilization efficiency, and provide key support for the construction of smart hospitals, demonstrating significant industry demonstration value.

With the rapid development of emerging technologies such as the Internet of Things, big data, cloud computing, blockchain, and artificial intelligence, the healthcare sector is undergoing a profound digital transformation. Digital therapeutics have demonstrated significant advantages in improving patients' quality of life, enhancing treatment adherence, and optimizing clinical decision-making. Through a systematic literature review, this study comprehensively examines the research progress and practical applications of digital therapeutics in oncology health management, aiming to provide a scientific foundation for theoretical research and clinical implementation of digital therapeutics for oncology in China. As an emerging field in oncology intervention, digital therapeutics present both opportunities and challenges. Their clinical efficacy and pathways for widespread adoption still require further validation through empirical studies.

To explore the potential application of traditional Chinese medicine in the treatment of Parkinson's disease. Data collection was conducted through medical records and the SymMap database, utilizing named entity recognition technology and constructing a knowledge graph using the Neo4j graph database. Four knowledge graph embedding models including TransE L1, TransE L2, DistMult, and ComplEx were employed to evaluate their performance and the quality of learned embedding vectors. The TransE L2 model was selected for link prediction. The constructed knowledge graph contains 8 types of entity labels and 7 types of relationships, totaling 13,870 nodes and 274,516 relationships. The selected TransE L2 model identified 30 Chinese herbal medicines that could potentially be used in the treatment of Parkinson's disease. Among them, seven drugs such as Thunder God Vine have been previously studied and proven to have potential therapeutic effects. This study indicates that drug repurposing based on medical case knowledge graph embedding is feasible in the field of Parkinson's disease treatment. The TransE L2 model effectively integrates knowledge graph information, providing important references for the repositioning of traditional Chinese medicine in the treatment of Parkinson's disease.

To explore the potential application of traditional Chinese medicine in the treatment of Parkinson's disease. Data collection was conducted through medical records and the SymMap database, utilizing named entity recognition technology and constructing a knowledge graph using the Neo4j graph database. Four knowledge graph embedding models including TransE L1, TransE L2, DistMult, and ComplEx were employed to evaluate their performance and the quality of learned embedding vectors. The TransE L2 model was selected for link prediction. The constructed knowledge graph contains 8 types of entity labels and 7 types of relationships, totaling 13,870 nodes and 274,516 relationships. The selected TransE L2 model identified 30 Chinese herbal medicines that could potentially be used in the treatment of Parkinson's disease. Among them, seven drugs such as Thunder God Vine have been previously studied and proven to have potential therapeutic effects. This study indicates that drug repurposing based on medical case knowledge graph embedding is feasible in the field of Parkinson's disease treatment. The TransE L2 model effectively integrates knowledge graph information, providing important references for the repositioning of traditional Chinese medicine in the treatment of Parkinson's disease.