Governance and Management of Medical Scientific Data Sharing and Application

Inhaltsverzeichnis

1. Frontmatter

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2. Chapter 1. Fundamental Overview of Medical Scientific Data Sharing

   • Open Access

   Jian Guan

   Abstract

   Medical scientific data are a crucial resource for innovation in the digital and big data era. These data are considered the key drivers of modern productivity. Defining medical scientific data, sharing, and applications are essential for establishing effective data governance and management systems. In the context of Internet and data science, data sharing has become an inevitable trend. This chapter defines key terms related to data and data sharing, standardizes basic concepts, classifies scientific medical data from various perspectives, and summarizes the significance and challenges of data sharing, thereby laying the groundwork for exploring governance theories and practices.

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3. Chapter 2. Data Sharing Governance and Management Framework

   • Open Access

   Jian Guan

   Abstract

   The sustainable development of medical data sharing necessitates data governance and management frameworks and systems that are grounded in the fundamental elements and tailored to specific objectives corresponding to the challenges in this field. We propose that data and stakeholders are the fundamental elements of medical data sharing and application. Multilevel frameworks and systems are designed based on the general principles of effective governance and considering the three relationships established between data and stakeholders, along with data governance objectives. We address key issues and propose governance and management mechanisms and rules for medical data sharing and application, and emphasize on clarifying the medical data processing rules as sensitive personal information, particularly on the systemic institutional data systems we constructed.

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4. Chapter 3. International Practice and Important Policies and Consensus

   • Open Access

   Jian Guan

   Abstract

   International organizations and regional alliances have developed international policies during data-sharing practices, including reaching principles and consensus that significantly influence governance. This chapter briefly introduces international data-sharing practices. The primary objective is to select and introduce representative international and regional policies and principles of international guiding value, data governance and ethics, personal data processing rules, and open access. The documents covered include the OECD's “Principles and Guidelines for Access to Research Data from Public Funding” and “Good Practice Principles for Data Ethics in the Public Sector,” the European Union's General Data Protection Regulation, and the FAIR principles.

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5. Chapter 4. China's Data Sharing Practice and Data Governance and Legal Exploration

   • Open Access

   Jian Guan

   Abstract

   The Chinese government actively participates in international data-sharing initiatives and practices, leveraging its strengths in medical data to explore data governance systems that promote data sharing and trading. This chapter provides a comprehensive overview of China's framework and significant progress in advancing data-sharing practices in data governance and management. It focuses on key policies and legal frameworks for data governance, outlines the overarching principles and crucial regulations for establishing China's basic data system, and interprets the exploration and practice of data property rights system. Regulations governing sensitive personal information processing specify management requirements for medical data handling, highlight distinctive features of privacy protection in China, and discuss dual governance strategies combining general and specialized approaches for health data legal and ethical considerations.

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6. Chapter 5. Data Ethics Governance

   • Open Access

   Jian Guan

   Abstract

   Data ethics has emerged as a branch of the evolving ethical domain, particularly in addressing the ethical challenges associated with data processing, algorithms, and their applications. Ethical governance is crucial for sharing and utilizing medical scientific data. Ethical reviews serve as a practical embodiment of ethical principles, ensuring their implementation and protection within data-centric strategies and protocols. We outline the scope of data ethics norms, establish a data ethics review framework based on international advancements, and analyze the applicability of basic ethical principles applied to data ethics. We also elaborate on China's data ethics review system and the strategies grounded in these regulations.

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7. Chapter 6. Institutional Data Governance and Management

   • Open Access

   Jian Guan

   Abstract

   Institutional governance is a fundamental component of the data-governance framework. The institutional governance framework is based on national policies and legislation on data management and research ethics as well as international regulations and industry standards. The framework should establish a governance management system designed to protect the rights and interests of the state, institutions, and stakeholders. Here, We outline an institutional system for health data that defines the responsible departments, committees, and personnel along with their specific duties; clarifies internal and external data management systems, documentation, and processes, particularly the internal strategies and measures for efficiency. Moreover, we highlight the key elements of the data ethics review guidelines we established, which are contingent upon the temporal context, have been developed through expert consensus, and established as a team standard.

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8. Chapter 7. Institutional Management for Privacy Protection and Informed Consent

   • Open Access

   Jian Guan

   Abstract

   Privacy protection and informed consent are two fundamental rights in the application of personal health information, including data sharing, which are subject to legal and ethical oversight. Privacy protection and informed consent face significant challenges, often clashing with data sharing practices and exhibiting contradictory relationships. This chapter presents our standards and methods for assessing the severity of private information and the potential risks of re-identification to provide a foundation for hierarchical management. Recommendations are provided for choosing informed consent models based on refined privacy conditions. Additionally, it investigates related factors and methods. Specifically, to determine the potential risks of re-identification, we innovatively introduced the concepts of ‘general technical personnel’ and ‘prior art’ as used in patent definitions. We suggest the ‘pre-engagement’ consent pathway as an active protective measure.

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9. Chapter 8. Construction of the Ontology of Bioethics and Ethics of Science & Technology

   • Open Access

   Jian Guan

   Abstract

   Data standards constitute the foundation for effective data sharing and reuse. They are essential for achieving the goals of FAIR principles in data sharing practices. Scientific and technological activities that involve processing and applying human data are crucial for ethical governance. In this chapter, we briefly introduce some important data standards for sharing, focusing on ontologies, particularly some representative ontologies in the medical field. We then concentrate on the necessity and feasibility of ethics ontologies based on bioethics ontologies and their limited development in recent years. Finally, we introduce our exploration of the framework for the Ontology of Bioethics and Ethics of Science & Technology (OBEST). OBEST is established based on basic ethical principles, bioethics, and data ethics framework, along with several examples of important logical relationships.

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10. Chapter 9. Data Interoperability and Data Structure Standard

    • Open Access

    Jian Guan

    Abstract

    The mechanism for addressing the relationships among data is interoperability, which is of great importance for data integrity and integration through data structure standards. Enhancing data quality and standardizing data structures facilitate improved data interoperability. The standardization of data structures simultaneously promotes data quality and interoperability. We focus on introducing the framework, general requirements, and key points of the data structure standard system. Specifically, we established a standard data structure system and recommended modules and information tables for individual-level data as the structural unit for research and real-world data involving humans within the framework. We propose best practice recommendations to enhance data interoperability and introduce a data quality assessment solution that targets datasets and databases to help determine data value. This assists in guiding the collection and processing of valuable data in the medical field.

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11. Chapter 10. A General Theory of Medical Big Data and Crucial Issues

    • Open Access

    Jian Guan

    Abstract

    There is no universally accepted definition of big data, yet it is characterized by widely recognized attributes. Medical big data embodies all these characteristics. These attributes present certain challenges for big data and its applications. We elaborates on medical big data and its interpretation, encompassing three representative of medical big data that exhibit distinct compositional features. We discuss misconceptions in medical big data applications emphasizing that the implementation of big data requires technical analysis, and address unique technological, legal, and ethical challenges. Here we focus on core issues and controversies in medical big data, particularly the challenges of confirming property rights and potnetial solutions, with special attention paid to Aggregated Data rights. We also analyze the applicability of traditional intellectual property frameworks and point out the impact of technological advancements on right confirmation.

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12. Chapter 11. Big Data in Healthcare

    • Open Access

    Jian Guan

    Abstract

    Healthcare big data represents a significant category of medical big data, and its applications facilitate precision services, clinical decision-making, and data-driven research. It also supports policy and public health initiatives to advance the big data heathcare industry. This chapter focuses on real-world data studies, highlighting their advantages as real-world evidence and addressing the associated ethical issues. We introduce the critical importance of real-world data for the development of hospital preparations and Traditional Chinese Medicine (TCM). Another emphasis is placed on the current state and emerging trends in TCM big data, key TCM big databases, and their applications in data-driven clinical decision-making and research.

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13. Chapter 12. ‘Omics’ Big Data and Precision Medicine

    • Open Access

    Jian Guan

    Abstract

    Omics Big Data underpins genetic information and its expression for precision medicine, thereby accelerating the discovery of disease mechanisms, drug targets, and drug development. It has a wide range of applications, including clinical genomics and pharmacogenomics. This chapter offers a concise overview of various types of omics, current trends, and characteristics of the resulting omics big data. It also explores the applications of these data in the context of rare diseases, tumors, chronic diseases, and infectious diseases, along with the challenges of data sharing. The discussion primarily focuses on analyzing the ethical issues and challenges in clinical genomics and proposing ethical guidelines for clinical genomic testing and interpretation. These guidelines were published as an expert consensus.

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14. Chapter 13. Public Health Big Data Sharing

    • Open Access

    Jian Guan

    Abstract

    Public health faces unique ethical challenges. The concept of precision in public health has arisen in the age of big data. The sharing and utilization of public big data can yield substantial scientific and societal benefits. However, deficiencies still exist in ethical norms, management guidelines, and practical experiences. This chapter presents trends and advancements in public health practices and ethical norms, grounded in the mission of public health. It explores the significant challenges and conflicts of interest inherent in the sharing of public health data and suggests targeted strategies and potential practical solutions. The emphasis is on establishing traceable Pathogen Relational Databases, informed consent strategies, and ontology framework on informed consent for major infectious diseases throughout the entire lifecycle of pathogen data. Furthermore, it outlines key points and feasible procedures, and data ethics reviews during major infectious disease outbreaks.

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15. Chapter 14. Governance and Management of Medical Artificial Intelligence

    • Open Access

    Jian Guan

    Abstract

    Medical artificial intelligence represents the integration and interdisciplinary progress of medical big data and machine learning, including generative artificial intelligence. This convergence presents new opportunities and potentials for medical services and research. However, the challenges that arise from their application necessitate new demands for governance and management. This chapter explores the impact of artificial intelligence and generative artificial intelligence within the medical domain, surveys internationally recognized principles and regulations for AI governance, and delves into China's governance and management norms for artificial intelligence. It focuses on the applications of AI in medicine and nursing, highlights ethical issues, examines AI principles with specific considerations for the medical field, and scrutinizes the governance and management framework for medical AI, as well as the applicable basis and key points for AI research review.

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