The major obstacle to optimizing the design of rare disease coverage is the fragmented decision-making process among medical services, pharmaceuticals, and medical insurance departments. There is an urgent need to realize data sharing and digital empowerment, as well as to adopt top-level design and systematic decision-making. It is also crucial to establish mechanisms, facilitated by digital intelligence, for sharing power and responsibilities, and assessing rewards and punishments. Furthermore, there is an urgent need to incorporate the theories of collaborative governance, digital governance, and the full life cycle into the entire process, which includes patient classification, diagnosis and treatment, medical assistance, medication protection, and health insurance fund management for rare diseases. This integration aims to provide theoretical reference for the effective linkage of medical services, pharmaceuticals, and medical insurance, and to improve the efficiency and equity of resource allocation in the public sector.

Hairy cell leukemia(HCL) is a rare malignant hematological tumor. This article presents the diagnosis and treatment of a patient with recurrent HCL. This patient, a 51-year-old female, was diagnosed with HCL in June 2013 and subsequently received monotherapy with cladribine. Post-treatment evaluation indicated a partial remission. In November 2023, she experienced chest tightness and shortness of breath with ultrasound revealing a right-sided pleural effusion. A follow-up examination in February 2024 confirmed the relapse of HCL. She was then treated with a combination of vemurafenib and rituximab, which resulted in a rapid complete remission without minimal residual disease. This case provides valuable insights into the management of recurrent HCL.

To summarize the applications of data-intelligence technology in diagnosing lysosomal storage disease(LSD), analyze their opportunities and challenges in clinical practice as well as their development trends, and provide insights and recommendations for advancing digitally driven auxiliary diagnostic technologies.

To review the randomized controlled trials (RCTs) at home and abroad on digital intelligence (DI)-driven rehabilitation in patients of neuromuscular disease, compare the effects of DI-driven rehabilitation with traditional rehabilitation, summarize the special needs and challenges faced by patients in rehabilitation of rare neuromuscular diseases, and provide evidence for the development and quality improvement of rehabilitation for rare neuromuscular diseases.

There is a substantial unmet need for treatments in the field of pediatric rare diseases, and investigator initiated trial(IIT) provide a critical pathway for testing and developing new drugs or treatment strategies. However, healthcare institutions, when conducting such research, must address compliance risks related to project approval, contract management, data protection, and conflict of interest management. This study aims to analyze the particularities and challenges of IIT in pediatric rare diseases, review relevant regulations and regulatory requirements, and provide healthcare institutions with a reference framework for compliance risk management to maximize the benefits of IIT. Based on literature review, analysis of laws and regulations, practical work experience, and frameworks from other institutions, we summarize the unique aspects of pediatric rare disease IIT in terms of participant characteristics, innovative technologies, and organizational structures.On this basis, targeted compliance management recommendations are proposed, which include establishing a risk rating and full-cycle risk monitoring mechanism, a consent and ethical review mechanism tailored to pediatric participants, a robust contract management mechanism, a comprehensive data security management mechanism, and a multidisciplinary team and multi-channel compensation mechanism. The study concludes that healthcare institutions, funders, and other collaborating entities should implement compliance management in line with the characteristics of IIT to ensure the safety and effectiveness of research and facilitate innovation and development in the treatment of pediatric rare diseases.

This article explored the strategies of promoting access to medicines for rare diseases with digital and intelligent transformation technology in China. It summarized the mature, global-oriented, digital and intelligent transformation supported platforms for promoting rare disease research, technology and product development, as well as diagnosis and treatment information sharing in typical countries and region. This was followed with analyses of the challenges of access to medicines for rare diseases in China. It also systematically reviewed a range of policies implemented in China intended to promote access to medicines for rare diseases, and the digital and intelligent solutions to promote access to medicines for rare diseases at national and regional levels. The authors proposed that the central government agencies and their technical support institutions take a lead to construct a patient-centered dynamic and decentralized national big data sharing platform for rare diseases registry and research, medicines development, production and supply, access to care and medicines, payment and security, utilization surveillance in China. With the support of digital and intelligent technologies, this national platform would be able to link multiple stakeholders to achieve integrated analysis of big data, as well as international sharing and exchange, which will help to effectively and efficiently promote access to medicines for rare diseases in China.

The exploration and pilot studies of applying blockchain to drug supply chain show great potential in promoting information sharing, collaboration competence among the actors, regulatory efficiency, and etc. In the future, with the help of blockchain, the optimization of the entire supply chain for orphan drugs is expected to be realized. However, there is no such exploration in China at present. This paper systematically sorts out the whole process of supply chain for orphan drugs and the existing problems of the chain. The article concludes that at present, blockchain is mainly used in the " circulation" and " use" of the drug supply chain. It helps to improve the traceability of drugs, to cope with the problem of counterfeit drugs, to enable actors of the drug supply chain to form a collaborative network in optimizing resource allocation, and to improve the operation and supervision efficiency of the supply chain. In the future, the application faces challenges such as high costs in system conversion, lack of personnel awareness, and incomplete supporting systems. Based on the three dimensions of technology, practice, and research, this paper also looks into the future and suggests for the future use of blockchain in the supply chain of orphan drugs by constructing a practice model, the so called DI-GIVE (Digital, Intelligence, Government′s supervision, Innovation, Views of variety, Evaluation-based) hoping to innovate the supply chain of orphan drugs and to ensure the drug use for the patients with rare diseases in China.

To summarize the current status in the application of digital and intelligent technologies in the field of pharmacovigilance and to provide reference to the selection and development of methods for pharmacovigilance of rare diseases.

Our study aims at systematically summarizing and evaluating the applications of digital intelligence technologies in the field of rare disease medical care insurance now and in the future and at constructing a conceptual framework for the digital powered mechanism for the medical care insurance for rare diseases. By using Chinese keywords of " rare disease" " medical insurance"" artificial intelligence"" prediction model"" machine learning"" big data"" algorithm" and their English equivalents, we searched the databases of PubMed, Embase, Web of Science, CNKI, Wanfang, and VIP, collected relevant literature, and decided the criteria of inclusion and exclusion. The finding of our study shows that medical care insurance mechanism of rare disease in China faces significant challenges in drug accessbility and the funding sustainability. Meanwhile, our study shows that the digital intelligence technologies have broad potential in applications-in financing, accessbility, payment, and supervision. Specifically, dynamic simulation models and big data analysis can make precise prediction of the demand for funding of medical care insurance. The machine learning algorithms improve the dynamic evaluation of drug safety and cost-effectiveness. The personalized payment models enhance the efficiency in identifying the cohort with high expenditure so as to alleviate fund expenditure pressures. The intelligent monitoring technologies can accurately detect the abnormal behaviors in funds of medical care insurance. These technologies provide systematic and scientific solutions for improving the medical care mechanism for rare diseases. Even though further investigation is needed, the digital intelligence technologies have shown remarkable potential in enhancing the flexibility, efficiency, and sustainability of the medical care insurance system and a promising future in meeting the needs of patients with rare diseases.

The inherent clinical uncertainties, substantial costs, and small patient cohorts of orphan drugs limit the applicability of randomized controlled trial (RCT)-based health technology assessments (HTAs) in guiding coverage criteria, sustainable financing models, and equitable reimbursement frameworks for medical financial assistance policies for rare diseases.The digital transformation in healthcare system leads to solutions to the challenges in designing the policy by using data-driven decision-making. This article summarizes the decision-making issues in policy design, discusses the current status and trends of digital transformation, and analyzes the important new opportunities for AI-driven policy design for medical financial assistance policies for rare diseases. Decision-making that is digital intelligence driven and using techniques such as big data analytics and real-world research methods will enhance targeting efficiency, improve the quality of financing, and realize the performance-based reimbursement in the medical financial assistance, providing significant value in facilitating the policy reform and development for rare diseases healthcare.