ASTM E1578-18 Standard Guide for Laboratory Informatics 翻译交流

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Scope
1.1 This guide helps describe the laboratory informatics landscape and covers issues commonly encountered at all stages in the life cycle of laboratory informatics from inception to retirement. It explains the evolution of laboratory informatics tools used in today's laboratories such as laboratory information management systems (LIMS), laboratory execution systems (LES), laboratory information systems (LIS), electronic laboratory notebooks (ELN), scientific data management systems (SDMS), and chromatography data systems (CDS).


范围
1.1本指南帮助描述了实验室信息处理的情况，涵盖了实验室信息学从开始到退休生命周期中所有阶段经常遇到的问题。它解释了当今实验室使用的实验室信息学工具的演变，如实验室信息管理系统(LIMS)、实验室执行系统(LES)、实验室信息系统(LIS)、电子实验室记录本(ELN)、科学数据管理系统(SDMS)和色谱数据系统(CDS)。


It also covers the relationship (interactions) between these tools and the external systems in a given organization. The guide discusses supporting laboratory informatics tools and a wide variety of the issues commonly encountered at different stages in the life cycle. The subsections that follow describe the scope of this document in specific areas.


它还涵盖了这些工具互相之间，以及与特定组织中的外部系统之间的关系（交互）。该指南讨论了配套的实验室信息学工具和生命周期中不同阶段常见的各种各样的问题。以下小节描述了本文件在特定领域的范围。


1.2 High-Level Purpose--The purpose of this guide includes: (1) educating new users on laboratory informatics tools; (2) providing a standard terminology that can be used by different vendors and end users; (3) establishing minimum requirements for laboratory informatics; (4) providing guidance for the specification, evaluation, cost justification, implementation, project management, training, and documentation of the systems; and (5) providing a functional requirements checklist for laboratory informatics systems that can be adopted within the laboratory and integrated with existing systems.


1.2高层级用途--本指南的目的包括：(1)对新用户进行实验室信息学工具的教育；(2)提供一个标准术语，可供不同的供应商和最终用户使用；(3)建立实验室信息学的最低要求；(4)为质量标准、评估、成本论证提供指导，系统的实施、项目管理、培训和记录；以及(5)为实验室信息学系统提供功能要求检查表，可在实验室内采用并与现有系统集成。


1.3 Laboratory Informatics Definition_-Laboratory informatics is the specialized application of information technology aimed at optimizing laboratory operations. It is a collection of informatics tools utilized within laboratory environments to collect, store, process, analyze, report, and archive data and information from the laboratory and its supporting processes. Laboratory informatics includes the effective use of critical
data management systems, the electronic delivery of results to customers, and the use and integration of supporting systems (for example, training and policy management).


1.3实验室信息学定义_-实验室信息学是信息技术的专门应用，旨在优化实验室操作。它是实验室环境中使用的信息学工具的集合，用于收集、存储、处理、分析、报告和存档来自实验室及其支持过程的数据和信息。实验室信息学包括有效使用关键数据管理系统、向客户电子交付结果以及使用和集成支持系统（例如，培训和政策管理）。


Examples of primary laboratory informatics tools include laboratory information management systems (LIMS), laboratory executionsystems (LES), laboratory information systems (LIS), elec-tronic laboratory notebooks (ELN), scientific data managementsystems (SDMS), and chromatography data systems (CDS).


主要实验室信息学工具的例子包括实验室信息管理系统(LIMS)、实验室执行系统(LES)、实验室信息系统(LIS)、电子实验室记录本(ELN)、科学数据管理系统(SDMS)和色谱数据系统(CDS)。

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1.4 Scope Considerations when Selecting and Implementing Laboratory Informatics Solutions-_Many laboratories have determined that they need to deploy multiple laboratory informatics systems to automate their laboratory processes and manage their data. Selection of an informatics solution requires a detailed analysis of the laboratory's requirements and should not be a simple product category decision. Information technology (IT) representatives and subject matter experts (SMEs) who understand the needs of the laboratory need to be involved in the selection and implementation of a laboratory informatics system to ensure that the needs of the laboratory are met and IT can support it. Customers (internal and external) of laboratory information should also be included in the laboratory informatics solution design to ensure full electronic integration between systems.

1.4选择和实施实验室信息学解决方案时的范围考虑-_许多实验室已经确定他们需要部署多个实验室信息学系统来自动化他们的实验室过程和管理他们的数据。信息学解决方案的选择需要对实验室的要求进行详细分析，不应该是简单的产品类别决策。了解实验室需求的信息技术(IT)代表和主题专家(SME)需要参与实验室信息学系统的选择和实施，以确保实验室的需求得到满足，IT可以给予支持。实验室信息学解决方案设计中还应包括实验室信息的客户（内部和外部），以确保系统之间的完整电子集成。

1.5 The scope of this guide covers a wide range of laboratory types, industries, and sizes. Examples of laboratory types and industries include:


1.5本指南的范围涵盖了广泛的实验室类型、行业和规模。实验室类型和行业示例包括：

1.5.1 General Laboratories:
1.5.1.1 Standards (ASTM, IEEE. ISO) and
1.5.1.2 Government (EPA, FDA, JPL, NASA, NRC. USDA, USGS. FERC).


1.5.1通用实验室：1.5.1.1标准(ASTM、IEEE)。ISO）和1.5.1.2政府（EPA、FDA、JPL、NASA、NRC。USDA，USGS。FERC）。

1.5.2 Environmental:
1.5.2.1 Environmental monitoring.


1.5.2环境：1.5.2.1环境监测。

1.5.3 Life Science Laboratories;
1.5.3.1 Biotechnology and
1.5.3.2 Diagnostic.


1.5.3生命科学实验室；1.5.3.1生物技术和1.5.3.2诊断。

1.5.4 Healthcare and Medical:
1.5.4.1 Bionomics/genomics.
1.5.4.2 Medical devices,
1.5.4.3 Pharmaceutical,
1.5.4.4 Veterinary,
1.5.4.5 Public health, and
1.5.4.6 Hospital.


1.5.4医疗保健和医学：1.5.4.1生物学/基因组学、1.5.4.2医疗器械、1.5.4.3药品、1.5.4.4兽医、1.5.4.5公共卫生和1.5.4.6医院。

1.5.5 Heavy Industry Laboratories:
1.5.5.1 Energy and resources,
1.5.5.2 Manufacturing and construction,
1.5.5.3 Materials and Chemicals, and
1.5.5.4 Transportation and shipping.
1.5.5重工业实验室：1.5.5.1能源和资源，1.5.5.2生产和建筑，1.5.5.3材料和化学品，以及1.5.5.4运输和装运。


1.5.6 Food and Beverage Laboratories:
1.5.6.1 Agriculture,
1.5.6.2 Beverages,
1.5.6.3 Food, and
1.5.6.4 Food service and hospitality.


1.5.6食品和饮料实验室：1.5.6.1农业、1.5.6.2饮料、1.5.6.3食品和1.5.6.4餐饮服务和招待。


1.5.7 Public Sector Laboratories:
1.5.7.1 Law enforcement/forensic,
1,5.7.2 State and local government,
1,5.7.3 Education and nonprofits, and
1.5.7.4 Public utilities (water, electric, waste treatment).


1.5.7公共部门实验室：1.5.7.1执法/法医、1.5.7.2州和地方政府、1.5.7.3教育和非营利组织以及1.5.7.4公用事业（水、电、废物处理）。

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1.6 Integration- The scope of integration covered in this guide includes communication and meaningful data exchange between different laboratory informatics tools and other external systems (document management, chromatography data systems, laboratory instruments, spectroscopy data systems, enterprise resource planning (ERP), manufacturing execution systems (MES), investigations/deviations and CAPA management systems), and other integrated business systems (for example, clinical or hospital environments) provide significant business benefits to any laboratory and is discussed at a high level in this guide.


1.6集成——本指南所涵盖的集成范围包括不同实验室信息学工具和其他外部系统（文件管理、色谱数据系统、实验室仪器、光谱数据系统、企业资源规划(ERP)、生产执行系统(MES)、调查/偏差和CAPA管理系统）和其他综合业务系统（例如，临床或医院环境）之间的交流和有意义的数据交换，为任何实验室提供了显著的业务效益，本指南将在高水平上进行讨论。


1.7 Life-Cycle Phases--The scope of this guide is intended to provide an understanding of laboratory informatics tools* life cycle from project initiation point to retirement and decommissioning. This guide was designed to help newer audiences in understanding the complexity in the relationships between different laboratory informatics tools and how to plan and manage the implementation project, while seasoned users may use the different life cycles to maintain existing laboratory informatics tools. Integrating additional informatics tools to existing ones in today's evolving laboratory environment adds constraints that need to be considered. The life-cycle discussion includes both the laboratory informatics solution life cycle as well as the project life cycle.


1.7生命周期阶段-本指南的范围旨在提供从项目启动点到退役和停用的实验室信息学工具*生命周期的理解。本指南旨在帮助较新的受众了解不同实验室信息学工具之间关系的复杂性以及如何计划和管理实施项目，而经验丰富的用户可以使用不同的生命周期来维护现有的实验室信息学工具。在当今不断发展的实验室环境中，将额外的信息学工具整合到现有工具中，增加了需要考虑的制约因素。生存周期讨论包括实验室信息学解决方案生存周期以及项目生存周期。


1.7.1 The product life cycle encompasses a specific laboratory informatics system and the expected useful life of that system before it needs to be replaced or upgraded.
1.7.2 The project life cycle encompasses the activities to acquire, implement operate, and eventually retire a specific laboratory informatics system.


1.7.1产品生命周期包括特定的实验室信息系统和系统在需要更换或升级之前的预期使用寿命。1.7.2项目生命周期包括获取、实施和最终停用特定实验室信息系统的活动。


1.8 Audience-_This guide has been created with the needs of the following stakeholders in mind: (1) end users of laboratory informaties tools, (2) implementers of laboratory informatics tools, (3) quality personnel, (4) information technology personnel, (5) laboratory informatics tools vendors, (6) instrument vendors, (7) individuals who approve laboratory informatics tools funding, (8) laboratory informatics applications support specialists, and (9) software test/validation specialists. Information contained in this guide will benefit a broad audience of people who work in or interact with a laboratory. New users can use this guide to understand the purpose and functions of the wide variety of laboratory informatics tools as well as the interactions between these tools with external systems. The guide can also help prospective users in understanding terminology, configurations, features, design, benefits, and costs of these different laboratory informatics tools.


1.8受众-_本指南的创建考虑了以下利益相关者的需求：(1)实验室信息工具的终端用户，(2)实验室信息学工具的实施者，(3)质量人员，(4)信息技术人员，(5)实验室信息学工具供应商，(6)仪器供应商，(7)批准实验室信息学工具资助的个人，(8)实验室信息学应用支持专家，(9)软件测试/验证专家。本指南中包含的信息将使在实验室工作或与实验室互动的广泛受众受益。新用户可以使用本指南了解各种实验室信息学工具的目的和功能以及这些工具与外部系统之间的相互作用。该指南还可以帮助潜在用户理解这些不同实验室信息学工具的术语、配置、功能、设计、益处和成本。


Individuals who are purchasing specific tools may also use this guide to identify functions that are recommended for specific laboratory environments. Research and development staff of different commercial laboratory informatics systems vendors may use the guide as a tool to evaluate, identify, and potentially improve the capabilities of their products. The vendors' sales staff may use the guide to represent functions of their laboratory informatics products to prospective customers in more generic and product-neutral terms.


购买特定工具的个人也可以使用本指南来确定推荐用于特定实验室环境的功能。不同商业实验室信息学系统供应商的研究和开发人员可以使用该指南作为评价、识别和潜在提高其产品能力的工具。供应商的销售人员可以使用该指南以更通用和产品中立的术语向潜在客户表示其实验室信息学产品的功能。


1.9 Out of Scope_-This guide does not attempt to define the boundaries of laboratory informatics, as they continue to evolve and blur between the different types of tools; rather, it focuses on the functionality that is provided by laboratory informatics as a whole.


1.9范围外_-本指南并不试图定义实验室信息学的边界，因为它们在不同类型的工具之间不断演变和模糊；相反，它侧重于实验室信息学作为一个整体提供的功能。


1.10 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.


1.10本国际标准是根据世界贸易组织技术性贸易壁垒(TBT)委员会发布的《国际标准制定原则、指南和建议的决定》中确立的国际公认的标准化原则制定的。

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3. Terminology

术语
3.1 Definitions-_-This guide defines the majority of terminology used in the field of laboratory informatics. Users of this guide should request a terminology list from each vendor with a cross reference to the terms used in this guide.

3.1定义-_-本指南定义了实验室信息学领域中使用的大多数术语。本指南的用户应要求每个供应商提供一份术语列表，并交叉引用本指南中使用的术语。

3.2 Definitions of Terms Specific to This Standard: 3.2.1 artificial intelligence, Al, n- behavior by machines or computers versus the natural intelligence of humans and animals.

3.2本标准专用术语的定义：3.2.1机器或计算机的人工智能、AI-n 机器或计算机的行为与人类和动物的自然智力的比较。

3.2.1.1 Discussion--In the computer science arena, any device that perceives its environment and takes action to maximize success in achieving a goal is exhibiting AI. Machine learning is an application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

3.2.1.1讨论-在计算机科学领域，任何感知其环境并采取行动最大限度地成功实现目标的设备均显示AI。机器学习是人工智能的一种应用，它为系统提供了从经验中自动学习和改进而不被明确编程的能力。

3.2.2 chromatography data system, CDS, n- computer system used to acquire, analyze, store, and report information from chromatographic instruments.




3.2.2色谱数据系统、CDS，n-从色谱仪器中采集、分析、储存和报告信息的计算机系统。


3.2.3 cloud computing, p term generally used to refer to software applications that are delivered as a software service through remote hosting using the public internet (public cloud) or within the users' network environment (private cloud).

3.2.3云计算，p-改术语通常指通过使用公共互联网（公用云）的远程托管或在用户网络环境（私用云）内作为软件服务提供的软件应用程序。

3.2.3.1 Discussion-_Essentially, the difference between cloud computing and traditional application deployment is that the application's users may not be responsible for the installation and maintenance of the computing infrastructure and application software.



3.2.3.1讨论-_本质上，云计算和传统应用程序部署之间的区别在于应用程序的用户可能不负责安装和维护计算基础设施和应用软件。


3.2.4 corrective and preventative action, CAPA, n CAPA applications are used to collect information, analyze information, identify and investigate product and quality problems, and take appropriate and effective corrective or preventive (or both) action to prevent their recurrence.

3.2.4纠正和预防措施、CAPA n-CAPA应用于收集信息、分析信息、识别和调查产品和质量问题，并采取适当和有效的纠正或预防（或两者）措施以防止其再次发生。

3.2.4.1 Discussion- Verifying or validating corrective and preventive actions, communicating corrective and preventive action activities to responsible people, providing relevant information for management review, and documenting these activities are essential in dealing effectively with product and quality problems, preventing their recurrence, and preventing or minimizing device failures.


3.2.4.1讨论-验证或确认纠正和预防措施，向负责人传达纠正和预防措施活动，为管理审查提供相关信息，并记录这些活动对于有效处理产品和质量问题、防止其再次发生至关重要，并防止或最小化器械故障。


3.2.5 cybersecurity, n--set of technologies, practices, and processes used to protect computers, networks, programs, and data from attack, damage, exploitation, and unauthorized access.


3.2.5网络安全，用于保护计算机、网络、程序和数据不受攻击、损害、利用和未经授权访问的一系列技术、做法和流程。

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3.2.6 data exchange standardization, n-as defined by the International Organization for Standardization (ISO) in ISO/HL7 27932, the process of agreeing on standards, which represent the common language that allows the exchange of data between disparate data systems.


3.2.6数据交换标准化，n-根据国际标准化组织(ISO)在ISO/HL7 27932中的定义，即商定标准的过程，其代表允许不同数据系统之间交换数据的通用语言。


3.2.6.1 Discussion-_-The goals of standardization are to achieve comparability, compatibility, and interoperability between independent systems, to ensure compatibility of data for comparative statistical purposes, and to reduce duplication of effort and redundancies. A data standard often includes data elements, data element definitions, and such agreements as formats, message structures, and vocabulary. In the context of this paper, a standard is a specification or requirement and is not synonymous with a policy, procedure, guideline, framework, technique, or best practice. Adopting standards has the potential to improve interoperability and reduce costs by facilitating the ability of networked laboratories to coordinate activities during public health incidents where surge capacity may be required (for example, national response and readiness). Adopting standards may reduce the costs of laboratory informatics system implementation and vendor/developer support.


3.2.6.1讨论-_-实现独立系统之间的可比性、兼容性和互操作性，确保用于比较统计目的的数据兼容性，并减少工作重复和冗余是标准化的目标。数据标准通常包括数据元素、数据元素定义以及格式、消息结构和词汇等协议。在本文的上下文中，标准是质量标准或要求，不是政策、程序、指南、框架、技术或最佳实践的同义词。采用标准有可能通过促进网络化实验室在可能需要激增能力的公共卫生事件期间协调活动的能力（例如，国家反应和准备），提高互操作性并降低成本。采用标准可以降低实验室信息学系统实施和供应商/开发者支持的成本。


3.2.7 data integrity, n- extent to which data are attributable complete, consistent, accurate, and reliable throughout the data life cycle.


3.2.7数据完整性，整个数据生命周期中数据可归因的完整、一致、准确和可靠程度。


3.2.8 electronic document management system. EDMS, n-computer system used to store, catalog, review/approve, retrieve, view, and print digital documents.


3.2.8电子文件管理系统。EDMS，用于存储、编目、审查/批准、检索、查看和打印数字文件的计算机系统。


3.2.8.1 Discussion-Modern EDMS applications typically provide the ability to manage a document throughout its lifecycle, including document initiation, multiple review levels, version control, security, and archiving of historical versions of documents.


3.2.8.1讨论-现代EDMS应用程序通常提供在整个生命周期内管理文件的能力，包括文件启动、多个审查级别、版本控制、安全性和文件历史版本的存档。


3.2.9 electronic laboratory notebook, ELN, n software program designed to replace paper laboratory notebooks; an electronic system on which to create, store, retrieve, and share fully electronic records in ways that meet all legal, regulatory, technical, and scientific requirements.


3.2.9电子实验室记录本、ELN, n-软件程序，旨在取代纸质实验室记录本；以符合所有法律、法规、技术和科学要求的方式创建、存储、检索和共享完整电子记录的电子系统。


3.2.9.1 Discussion-_Laboratory notebooks, in general, are used by scientists, engineers, and technicians to document research, experiments, and procedures performed in a laboratory. A laboratory notebook is often maintained to be a legal document and may be used in a court of law as evidence. Similar to an inventor's notebook, the laboratory notebook is also often referred to in patent prosecution and intellectual property litigation.


3.2.9.1讨论-实验室记录本通常由科学家、工程师和技术人员用于记录在实验室中进行的研究、实验和程序。实验室记录本通常作为法律文件保存，可在法院用作证据。与发明人的笔记本相似，实验室笔记本在专利申请和知识产权诉讼中也经常被提及。


3.2.10 electronic signature, n- electronic representation of a handwritten signature.


3.2.10电子签名，n-手写签名的电子形式。


3.2.11 enterprise resource planning, ERP, n-computer system to integrate different types of data such as inventory levels, product orders, manufacturing capacity, inspection results, accounting data, and customer relationship management information from organizations within an enterprise (company), facilitating the flow of information between various business functions across a company as well as with outside business
partners.


3.2.11企业资源规划、ERP、n-计算机系统，整合企业（公司）内部组织的库存水平、产品订单、生产能力、检验结果、会计数据、客户关系管理信息等不同类型的数据，促进不同业务职能部门之间的信息在公司内部以及外部业务合作伙伴之间的流动。


3.2.12 good automated manufacturing practice forum, GAMP Forum, n-volunteer group under the auspices of the International Society of Pharmaceutical Engineering (ISPE) for writing guidance for the validation of computerized systems used in the regulated portions of the pharmaceutical and allied industries and it is specifically designed to aid suppliers and users in the pharmaceutical industry.


3.2.12自动化生产质量管理规范论坛，GAMP论坛，由国际制药工程学会(ISPE)主持的志愿者小组，负责编写制药和相关行业监管部分所用计算机化系统的验证指南，该小组专门设计用于帮助制药行业的供应商和用户。


3.2.13 integration broker, n- messaging application that can receive or extract data from a source system at the appropriate time. transform the data, and route the reformatted data to the target node.


3.2.13集成代理、n-消息应用程序，可以在适当的时间从源系统接收或提取数据。转换数据，并将重新格式化的数据传输到目标节点。


3.2.13.1 Discussion-An integration broker application can also provide a repository for archiving, searching, and retrieving these messages.


3.2.13.1讨论-集成代理应用程序还可以提供存储库，用于存档、搜索和检索这些消息。


3.2.14 internet of things, loT, n system of objects- computing devices, machines, objects, people, animals, and so forth- that can connect to a network and communicate among themselves, often without human intervention.


3.2.14物联网，IoT，n-对象系统-计算设备、机器、对象、人、动物等，可以连接到网络并在自己之间进行通信，通常不需要人为干预。


3.2.14.1 Discussion--An loT device is an object operating within that system


3.2.14.1讨论-物联网设备是在该系统内运行的对象


3.2.15 laboratory execution system, LES, n- computer system used in the laboratory at the analyst work level to aid in step enforcement for laboratory test method execution.


3.2.15实验室执行系统，LES，实验室中在分析员工作强度使用的计算机系统，以帮助实验室检测方法执行的步骤执行。


3.2.15.1 Discussion--LES focus on step execution of defined laboratory test methods. The LES is typically used in quality control laboratories that have defined test methods, The functionality of a LES and a laboratory information management system (LIMS) overlaps in the areas of result entry, instrument integration, and specification flagging. Deployment options include LES and LIMS systems deployed as an integrated solution, LIMS-only, or LES only (for limited functions).


3.2.15.1讨论-LES重点关注规定实验室检测方法的步骤执行。LES通常用于质量控制实验室，这些实验室具有确定的检测方法。LES和实验室信息管理系统(LIMS)的功能在结果输入、仪器集成和质量标准标记领域重叠。部署选项包括作为集成解决方案部署的LES和LIMS系统、仅LIMS或仅LES（用于有限的功能）。


3.2.16 laboratory informatics, n- term used to describe the specialized application of information technology aimed at optimizing laboratory operations.


3.2.16实验室信息学，n-用于描述信息技术的专业应用，旨在优化实验室操作。


3.2.16.1 Discussion- That technology includes informatics tools used within laboratory environments to collect, store, process, analyze, report, and archive data and information from the laboratory and supporting processes. Laboratory informatics includes the effective use of critical data management systems, the electronic delivery of results to customers, and the use and integration of supporting systems (for example, train
ing and policy management). Examples of primary laboratory informatics tools include, LIMS, LES, CDS, ELN, laboratory information systems (LIS), and scientific data management systems (SDMS).


3.2.16.1讨论-该技术包括在实验室环境中使用的信息学工具，用于收集、存储、处理、分析、报告和归档来自实验室和支持过程的数据和信息。实验室信息学包括有效使用关键数据管理系统、向客户电子交付结果以及使用和集成支持系统（例如，培训和政策管理）。主要实验室信息学工具的示例包括LIMS、LES、CDS、ELN、实验室信息系统(LIS)和科学数据管理系统(SDMS)。

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3.2.17 laboratory informatics tools configuration, n-refers to the process of changing the functions of any laboratory informatics tool to match the business processes used in a particular laboratory, and it does not involve the use of writing software code either via a recognized software language or a language provided by the informaties application supplier.


3.2.17实验室信息学工具配置，n-是指改变任何实验室信息学工具的功能以匹配特定实验室使用的业务流程的过程，它不涉及通过公认的软件语言或信息协议应用程序供应商提供的语言编写软件代码。


3.2.17.1 Discussion- This is a GAMP Category 4 software and is defined as "Configured software including, LIMS, SCADA, DCS, CDS, etc." Such configuration typically involves using an interface provided by the vendor to enter information that describes the types of samples, analytical methods, specifications, and so forth, used in the laboratory. It may also involve the configuration of options and businesses rules within the tool.


3.2.17.1讨论-这是GAMP第4类软件，定义为“可配置的软件，包括LIMS、SCADA、DCS、CDS等”此类配置通常涉及使用供应商提供的界面输入描述实验室中使用的样本类型、分析方法、质量标准等信息。它还可能涉及工具中选项和业务规则的配置。


3.2.18 laboratory, informatics tools customization. n-refers to the process of changing the functions of any laboratory informatics tool to match the business processes used in a particular laboratory.


3.2.18实验室信息学工具定制。n-是指改变任何实验室信息学工具的功能以匹配特定实验室使用的业务流程的过程。


3.2.18.1 Discussion- This is different from the previously mentioned tools configuration in that customization involves writing software code either via a recognized software language or a language provided by the informatics application supplier. This is a GAMP 5 software category. Such customization typically involves adding tables, modifying table structures, and writing code or programs to alter the behavior of any laboratory informatics tool.


3.2.18.1讨论-这与之前提到的工具的配置不同，因为定制涉及通过公认的软件语言或信息学应用程序供应商提供的语言编写软件代码。这是GAMP 5软件类别。这种定制通常涉及添加表格、修改表格结构和编写代码或程序，以改变任何实验室信息学工具的行为。


3.2.19 laboratory information management system, LIMS, n-(1) computer software and hardware that can acquire, analyze, report, and manage data and information in the laboratory; (2) computer software that is used in the laboratory for the management of samples, test results, laboratory users, instruments, standards, and other laboratory functions such as invoicing, plate management, product/material stability programs, and work flow automation; and (3) a class of application software which handles storing and managing of information generated by laboratory processes.


3.2.19实验室信息管理系统，LIMS，n-(1)可以采集、分析、报告和管理实验室中数据和信息的计算机软件和硬件；(2)实验室中用于管理样品、检测结果、实验室用户、仪器、标准品、以及其他实验室功能，如开票、印版管理、产品/材料稳定性计划和工作流程自动化；以及 (3)一类用于存储和管理实验室流程生成的信息的应用软件。


3.2.19,1 Discussion--These systems are used to manage laboratory processes, including master data definition, sample management and chain of custody, work assignment, instrument and equipment management, standard and reagent management, scheduled sample collection and testing, result entry, capture of results from instruments, result review, reporting, trending, and business rule enforcement. These systems interface with laboratory instruments (for example, CDS and other information systems such as ERP, LIS, or manufacturing execution systems MES]). A LIMS is a highly flexible application, which can be configured or customized to facilitate a wide variety of laboratory workflow models.


3.2.19,1讨论-这些系统用于管理实验室过程，包括主数据定义、样本管理和监管链、工作分配、仪器和设备管理、标准品和试剂管理、计划样本采集和检测、结果录入、仪器结果采集、结果审查、报告、趋势分析，和业务规则执行。这些系统与实验室仪器（例如，CDS和其他信息系统，如ERP、LIS或生产执行系统【MES】）连接。LIMS是一个高度灵活的应用程序，它可以被配置或定制，以方便各种各样的实验室工作流程模型。


3.2.20 laboratory information system, LIS, n class of application software that supports clinical laboratories by helping laboratory personnel manage the quality and integrity of test samples, departmental workflow functions, result review processes, reports of finalized results, interpretations, and diagnoses.


3.2.20实验室信息系统，LIS，n-通过帮助实验室人员管理检测样本的质量和完整性、部门工作流程功能、结果审查流程、最终结果报告、判读和诊断来支持临床实验室的一类应用软件。


3.2.20.1 Discussion-_These systems often interface with instruments and other information systems such as hospital information systems (HIS). A LIS is a highly configurable application and often includes laboratory-specific electronic medical records, direct clinician access via secure web connections, billing modules for laboratories performing commercial testing, sophisticated interface engines for routing orders and results to external systems, and on-board image archival systems for pathology images. Patient confidentiality and HIPAA requirements define unique security functionality for a LIS. The College of American Pathologists (CAP)
publishes LIS product guides?* that list current LIS in the market.


3.2.20.1讨论-_这些系统通常与仪器和其他信息系统（如医院信息系统[HIS]）连接。LIS是一个高度可配置的应用程序，通常包括实验室特定的电子病历、临床医生通过安全的网络连接直接访问、用于执行商业测试的实验室的计费模块、用于向外部系统发送订单和结果的复杂接口引擎，以及用于病理图像的机载图像存档系统。患者机密性和HIPAA的要求定义了LIS的独特安全功能。美国病理学家协会(CAP)发布LIS产品指南，列出市场上当前LIS。


3.2.21 lean laboratory, n-set of management and organizational processes that enables efficient testing flow, leveled workloads, visual work assignment and tracking, and the elimination of waste.


3.2.21精益实验室，n-能够实现高效的检测流程、平衡的工作负荷、可视化的工作分配和跟踪以及废物清理的一组管理和组织流程。


3.2.21.1 Discussion-Lean laboratory designs yield productive, high-quality laboratory environments that are sometimes supported by laboratory informatics tools.


3.2.21.1讨论-精益实验室设计可产生高产、高质量的实验室环境，有时可通过实验室信息学工具提供支持。


3.2.22 mapping tools, n- graphical data mapping, conversion, and integration applications that map data between any combination of XML. database, flat file. EDI, Excel (OOXML), XBRL, or web service, or both, then transforms data or autogenerates data integration code for the execution of recurrent conversions.


3.2.22映射工具、n-图形数据映射、转换和在XML的任何组合之间映射数据的集成应用程序。数据库，flat文件。EDI，Excel(OOXML), XBRL或web服务，或两者兼而有之，然后转换数据或自动生成数据集成代码以执行定期转换。


3.2.23 master data, n- represents the business objects which are agreed on and shared across the enterprise.


3.2.23主数据，n-代表企业内部约定和共享的业务对象。


3.2.23.1 Discussion--It can include relatively static reference, transactional, unstructured, analytical, and hierarchical data, as well as associated metadata. Examples of master data include product specifications, test method steps (to capture intermediate and final results), laboratory calculations, instrument information, and standard and reagent information.


3.2.23.1讨论-它可以包括相对静态的参考、事务、非结构化、分析和分层数据，以及相关的元数据。主数据的示例包括产品质量标准、检测方法步骤（采集中间和最终结果）、实验室计算、仪器信息以及标准品和试剂信息。


3.2.24 metadata, n- (1) data about data and (2) information that describes another set of data.


3.2.24元数据，(1)关于数据的数据和(2)描述另一组数据的信息。


3.2.24.1 Discussion-_Additional information about the data that provides context and meaning, including how, when, and by whom it was collected, and its relationship to the subject or test. Metadata in any laboratory informatics tool's context typically includes all data that supports a test result that is recorded in this tool. For example, a pH test includes a pH result that can be supported by metadata, including what instrument was used, what the calibration date of the instrument was, what standard buffer solutions (reagents) were used to calibrate the pH probe sensor, the expiration dates for the standard solutions, and the temperature of the solution at time of measurement.


3.2.24.1讨论-_提供背景和意义的其他数据信息，包括如何、何时、由谁收集以及与受试者或试验的关系。任何实验室信息学工具上下文中的元数据通常包括支持该工具中记录的测试结果的所有数据。例如，pH检测包括元数据支持的pH结果，包括使用的仪器、仪器的校准日期、用于校准pH探针传感器的标准缓冲溶液（试剂）、标准品溶液的失效日期，和测量时溶液的温度。


3.2.25 sample registration, n- -process of recording incoming sample information in a given laboratory informatics tool.


3.2.25样品登记，在给定实验室信息学工具中记录来料样品信息的过程。


3.2.26 scientific data management system, SDMS, n- computer system used to capture, centrally store, catalog, and manage data generated in a laboratory environment.


3.2.26科学数据管理系统，SDMS，n-计算机系统，用于采集、集中存储、编目和管理实验室环境中生成的数据。


3.2.26.1 Discussion--These data are then available for reuse and integration with other laboratory informatics systems. An example of an SDMS is an electronic repository for reports from laboratory informatics systems. The SDMS may include raw data file storage and archiving of data, It may also provide e-signature functionality for review/approval.


3.2.26.1讨论-这些数据可重复使用，并与其他实验室信息系统整合。SDMS的一个例子是实验室信息学系统报告的电子存储库。SDMS可包括原始数据文件存储和数据存档，还可提供电子签名功能以供审查/批准。


3.2.27 spectroscopic data systems, n computer systems used to collect, process, visualize, interpret, store, and report information from spectroscopic instruments.


3.2.27光谱数据系统，n-计算机系统，用于从光谱仪器中收集、处理、可视化、解释、存储和报告信息。

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4. Significance and Use
4. 意义和用途

4.1 Relevance--This guide is intended to educate the intended audience on many aspects of laboratory informatics. Specifically, the guide may:



4.1相关性-本指南旨在对预期受众进行有关实验室信息学许多方面的教育。具体来说，该指南可以：


4.1.1 Help educate new users of laboratory informatics;
4.1.2 Help educate general audiences in laboratories and other organizations that use laboratory informatics;
4.1.3 Help educate instrument manufactures and producers of other commonly interfaced systems;
4.1.4 Provide standard terminology that can be used by laboratory informatics vendors and end users;
4.1.5 Establish a minimum set of requirements for primary laboratory informatics functions;
4.1.6 Provide guidance on the tasks performed and documentation created in the specification, evaluation. cost justification, implementation, project management, training, and documentation of laboratory informatics;
4.1.7 Provide high-level guidante for the integration of laboratory informatics and other software tools.


4.1.1帮助教育实验室信息学的新用户；
4.1.2帮助教育实验室和其他使用实验室信息学的组织中的普通受众；
4.1.3帮助教育仪器制造商和其他常用接口系统的生产者；
4.1.4提供实验室信息学供应商和终端用户可以使用的标准术语；
4.1.5建立主要实验室信息学功能的最低要求;
4.1.6提供执行任务和质量标准、评估中创建文件的指南。成本论证、实施、项目管理、实验室信息学的培训和记录；
4.1.7为实验室信息学和其他软件工具的集成提供高级指南。


4.2 How to be Used--This guide is intended to be used by all stakeholders involved in any aspect of laboratory informatics implementation, use, or maintenance.


4.2如何使用-本指南旨在供参与实验室信息学实施、使用或维护任何方面的所有利益相关者使用。


4.2.1 It is intended to be used throughout the laboratory informatics life cycle by individuals or groups responsible for laboratory informatics implementation and use, including specification, build/configuration, validation, use. upgrades, and retirement/decommissioning.


4.2.1旨在整个实验室信息学生命周期中，由负责实验室信息学实施和使用的个人或团体使用，包括质量标准、构建/配置、验证、使用。升级和报废/停用。


4.2.2 This guide also provides an example of a laboratory informatics functional requirements checklist that can be used to guide the purchase, upgrade, or development of a laboratory informatics system


4.2.2本指南还提供了实验室信息学功能要求检查表范例，可用于指导实验室信息学系统的购买、升级或开发

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5. Elements of Laboratory Informatics
5.实验室信息学要素

5.1 Laboratory Informatics Elements Overview——Laboratory informatics is used to help laboratory personnel better collect, process, analyze, report, store, and share the data and information derived from the laboratory and its supporting processes. These processes are often an integral part of a laboratory's workflow and include activities such as registration of samples or experiments, or both, assignment of tests, entry of results, review and approval of results, and reporting. Laboratory informatics' scope encompasses multiple technical solutions or systems that are responsible for streamlining these and other laboratory processes.


5.1 实验室信息学要素概览——实验室信息学用于帮助实验室人员更好地收集、处理、分析、报告、存储和共享来源于实验室及其支持过程的数据和信息。这些流程通常是实验室工作流程的组成部分，包括样本或实验的注册、或两者兼有、检测分配、结果录入、结果审查和批准以及报告等活动。实验室信息学的范围包括负责简化这些和其他实验室流程的多种技术解决方案或系统。


Laboratory informatics is not solely about software managing laboratory data; it has many elements, some of which integrate or cross over with business management and other third-party tools. Those elements are also becoming increasingly complex, both in functionality and interoperability. Outside of standard laboratory information management systems (LIMS) and laboratory information systems (LIS), elements such as field data capture systems, advanced analytics tools, and artificial intelligence continue to shape the field of laboratory informatics.


实验室信息学不仅仅是关于管理实验室数据的软件；它有许多要素，其中一些要素与业务管理和其他第三方工具集成或交叉。这些要素在功能和互操作性方面也越来越复杂。在标准的实验室信息管理系统(LIMS)和实验室信息系统(LIS)之外，现场数据采集系统、先进的分析工具和人工智能等要素不断塑造实验室信息学领域。


The division between these and other system categories continues to soften as functionality continues to be added to each of them. LIMS were originally created to address laboratories' need to manage laboratory operations and data, provide traceability for all laboratory samples and equipment, and ensure that laboratory procedures are followed.


随着功能不断被添加到每个系统类别中，这些系统类别和其他系统类别之间的划分继续模糊化。最初创建LIMS是为了解决实验室管理实验室操作和数据的需要，提供所有实验室样品和设备的可追溯性，并确保遵循实验室程序。


Electronic laboratory notebooks (ELNs), on the other hand, were originally created to meet scientists' need to document their experimental design, execution, and conclusion in an electronic format instead of in a paper notebook.


而电子实验室记录本(ELN)最初是为了满足科学家以电子格式而不是纸质记录本记录其实验设计、执行和结论的需要而创建的。


The scientific data management system (SDMS) was created to provide a repository of all scientific data files and results regardless of instrument type.


创建科学数据管理系统(SDMS)，是为了有一个所有科学数据文件和结果的存储库，无论仪器类型是什么。


The current definitions of each of these system categories are far more encompassing and continue to evolve as the boundaries between categories continue to blur. That blurring of laboratory informatics elements, as well as their potential integration with enterprise elements----both within organizations and with customers of laboratory information----are illustrated in Fig. 1.


现如今对这些系统类别中每一类的定义都要包罗万象得多，并且也在随着类别之间的界限不断模糊而不断演变。实验室信息学元素之间的模糊，以及它们与企业元素的潜在整合——无论是在组织内还是与实验室信息的客户——如图1所示。


Laboratory informatics and all it encompasses is shown with the large yellow circle on the left, while the internal business systems that support laboratories are found associated with the blue circle on the right. Surrounding both is a bubble representing third-party interactions with both laboratory generated data and business data. The figure highlights the wide variety of crossover and interactions that can occur both within and external to laboratory informatics. Laboratory informatics applications are also taking on some of the functionality of internal business systems. (See 7.6-7.11 for more information on integration cases.)


实验室信息学及其包含的所有内容在左侧显示为大黄色圆圈，而支持实验室的内部业务系统在右侧显示为蓝色圆圈。一个泡泡包围了两者，代表第三方与实验室生成的数据和业务数据的互动。该图强调了实验室信息学内部和外部都可能发生的各种各样的交叉和相互作用。实验室信息学应用也在承担内部业务系统的一些功能。（有关集成用例的更多信息，参见7.6-7.11。）

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5.1.1 Core Systems - These laboratory systems most often provide the outward face of laboratory informatics and include LIMS, LIS, laboratory execution systems (LES), ELN, SDMS, and chromatography data systems (CDS). Not all systems will necessarily appear in a laboratory together; some are more typical to certain laboratory types than others. However, they usually play a key role in a laboratory's research or analysis activities, or both, and represent the key software systems with which laboratory personnel and customers of the laboratory may interact. From research samples and clinical specimens to outlined experiments and raw instrument data, these core systems fill a vital role in the laboratory informatics sphere.


5.1.1核心系统-这些实验室系统最常提供实验室信息学的外表面，包括LIMS、LIS、实验室执行系统(LES)、ELN、SDMS和色谱数据系统(CDS)。并非所有系统都必然出现在同一个实验室中；有些系统对某些实验室类型比其他系统更典型。然而，它们通常在实验室的研究或分析活动中起着关键作用，或两者兼而有之，代表了实验室人员和实验室客户可能互动的关键软件系统。从研究样本和临床标本到概述的实验和原始仪器数据，这些核心系统填补了实验室信息学领域的重要作用。


5.1.1.1 LIMS, LIS, and LES are alike in many regards in that they all act as core systems in a laboratory and handle data capture, analysis, review, storage, and reporting. These systems integrate in variable degrees with analytical instruments, automated tools, and other software systems, and they provide certain levels of regulated, industry-standard security for the data generated and transferred from its integrations. However, these systems also have fundamental differences that place them in specific use cases. A LIMS has been traditionally used to process and report on batches of samples from research, quality control, and manufacturing laboratories, all of which handle mostly anonymous, complex laboratory data. A LIS has normally been used in the clinical context of specimens and patients, and a LES is most often adopted in automated and regulated manufacturing environments where quality control. process control, test step execution, and instrument interface and calculation validation support laboratory testing.


5.1.1.1 LIMS、LIS和LES在许多方面都是相似的，它们都作为实验室的核心系统，处理数据采集、分析、审查、存储和报告。这些系统与分析仪器、自动化工具和其他软件系统进行了不同程度的集成，它们为其集成生成和传输的数据提供了一定水平的受监管的、行业标准的安全保证。然而，这些系统也存在根本差异，将其置于特定使用场景中。LIMS传统上用于处理和报告来自研究、质量控制和生产实验室的样本批次，所有这些实验室大多处理匿名、复杂的实验室数据。LIS通常用于标本和患者的临床环境中，LES最常用于质量控制的自动化和监管生产环境中。过程控制、测试步骤执行、仪器接口和计算确认支持实验室测试。


5.1.1.2 An ELN largely serves as an electronic replacement for the traditional paper laboratory notebook associated with scientists and technicians in research-driven environments. The ELN may be tailored to the individual researcher, large collaborative research efforts, or both. It may also be designed to manage the activities related to a specific scientific discipline or application, or it may be cross-disciplinary, supporting data
of all types. Traditionally used to document experiments and analysis, and act as intellectual property protection, the paper laboratory notebook has fallen out of favor with some laboratories that prefer the ELN's ability to improve search, support collaboration, and limit siloed data. Additional features of an ELN include data import, content linking, preformatted and customizable templates, and messaging.


5.1.1.2 ELN主要作为研究驱动的环境中与科学家和技术人员相关的传统纸质实验室笔记本的电子替代。ELN可以为个体研究人员、大型合作研究工作或两者量身定制。它还可以设计用于管理与特定科学学科或应用相关的活动，或者可以是跨学科、所有类型的支持性数据。传统上用于记录实验和分析，并作为知识产权保护，纸质实验室笔记本已经在一些偏好ELN改善搜索、支持协作和限制孤岛数据的能力的实验室失宠。ELN的其他特性包括数据导入、内容链接、预格式化和可定制的模板以及消息传送。


5.1.1.3 An SDMS is designed primarily to consolidate data and manage knowledge-based assets. The SDMS has typically excelled at handling unstructured files such as images, spreadsheets, raw instrument files, and PDFs. A set of agreed-upon rules in the system dictate how incoming data is processed and structured in the SDMS, acting as a gatekeeper for what is captured and how (including the application of metadata). An SDMS can be integrated with a LIMS, ELN, and so forth, to create a common repository for a laboratory's data, which can then be further associated with specific projects, experiments, or locations, or combinations thereof, or its data can simply be archived for long-term storage.


5.1.1.3 SDMS的主要目的是合并数据和管理知识资产。SDMS通常擅长处理非结构化文件，如图像、电子表格、原始仪器文件和PDF。系统中的一组预先制定规则规定了如何在SDMS中处理和结构化传入的数据，作为捕获内容和方式（包括元数据的应用）的守门人。SDMS可以与LIMS、ELN等集成，为实验室的数据创建一个公共存储库，然后可以进一步与特定的项目、实验或位置或其组合相关联，或者其数据可以简单地存档以便长期存储。


5.1.1.4 A CDS is designed for collecting, processing, and analyzing samples run on instruments managing chromatography techniques such as high-performance liquid chromatography (HPLC), ion chromatography (IC), gas chromatography (GC), size-exclusion chromatography (SEC), and affinity chromatography. The CDS typically consists of a combination of hardware and software connecting the instrument to the system and is computationally intensive, rapidly generating large data sets in laboratory environments. Complex algorithms and mathematical transformations of data can be performed within a CDS, which directly supports a wide range of chromatography instruments with bidirectional control of instrument settings (that is, temperature, pressure, and detector wavelength). The CDS typically provides sample handing (auto samplers), auto injection, mobile phase controls (temperature/pressure). detector control (wavelength), data collection (data points from one or more detectors), data analysis (for example, calibration curves, peak detection, and integration), reporting, and audit trail support. The CDS can be deployed as a standalone system or in larger configurations supporting multiple instruments, sites, and geographic regions. The CDS is typically interfaced
to other laboratory informatics tools (that is, LIMS, ELN, and SDMS) in which sample information is passed between the LIMS and the CDS, and the CDS test results (that is, peak areas) are passed back to the LIMS for final reporting. The CDS can also integrate with multiple analytical techniques in which data are passed between different instruments (such as a liquid chromatography-mass spectrometry (LC-MS) instrument]. The software is typically treated as a separate laboratory informatics element with its own IT infrastructure, including data acquisition modules (to attach to chromatography instruments) and administration, configuration, and security access controls.

5.1.1.4 CDS设计用于收集、处理和分析在高效液相色谱法(HPLC)、离子色谱法(IC)、气相色谱法(GC)、分子排阻色谱法(SEC)和亲和层析等仪器上运行的样品。CDS通常由连接仪器和系统的硬件和软件组合组成，计算密集，在实验室环境中快速生成大数据集。在CDS内可以进行复杂的算法和数据的数学转换，CDS直接支持广泛的色谱仪器，仪器设置（即温度、压力和检测器波长）双向控制。CDS通常提供样品处理（自动进样器）、自动进样、流动相控制（温度/压力）。检测器控制（波长）、数据采集（一个或多个检测器的数据点）、数据分析（例如，校准曲线、峰检测和积分）、报告和审计跟踪支持。CDS可以作为独立系统或更大的配置部署，支持多个仪器、研究中心和地理区域。CDS通常与其他实验室信息学工具（即LIMS、ELN和SDMS）连接，其中样品信息在LIMS和CDS之间传递，CDS检测结果（即峰面积）传递回LIMS进行最终报告。CDS还可以与多种分析技术整合，其中数据在不同仪器（如液相色谱-质谱法(LC-MS)仪器）之间传递。该软件通常被视为一个独立的实验室信息学元素，具有自己的it基础设施，包括数据采集模块（连接到色谱仪器）以及管理、配置和安全访问控制。


5.1.1.5 Bioinformatics and genomic applications are used to collect and analyze biological data, including genomic data. Genomic instruments generate much larger amounts of data compared to traditional laboratory instruments. Genomic data is often analyzed using specialized, proprietary bioinformatics algorithms.


5.1.1.5生物信息学和基因组应用用于收集和分析生物学数据，包括基因组数据。与传统的实验室仪器相比，基因组仪器产生的数据量要大得多。基因组数据通常使用专门的、专有的生物信息学算法进行分析。

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5.1.2 Instrument Data Systems-Some laboratory systems are purpose-made for specific instruments, with the CDS being a common case. Other examples include data acquisition systems (DAQ) for calibration equipment and digital oscillo-scope software. These systems excel at "talking" with specific instrument, capturing the data, and providing custom analysis tools related to the captured data. In the case of chromatography, a CDS will often both control a chromatography or spectroscopy instrument and provide a visual report of the chromatogram with contextual meaning. DAQ for calibraton instruments may include additional functionality such as
real-time data visualization and certificate generation.


5.1.2仪器数据系统-一些实验室系统是专门为特定仪器定制的，CDS是常见情况。其他例子包括用于校准设备的数据采集系统(DAQ)和数字示波器软件。这些系统擅长与特定仪器“交谈”，捕获数据，并提供与捕获数据相关的自定义分析工具。对于色谱法，CDS通常将控制色谱法或光谱学仪器，并提供具有上下文含义的色谱图目视报告。校准仪器的DAQ可能包括其他功能，如实时数据可视化和证书生成。


5.1.3 Advanced Analytics Tools - This element represents a broad category of advanced analytical tools used in laboratories. Examples include the scientific field of genomics in which advanced tools are used to study complete genome (genetic material within an organism). High-throughput genome analyzer instruments automate process steps to provide high volume parallel operations that combine DNA molecules, primers, polymerase amplification, imaging, and computational functions to yield low-cost DNA sequencing outputs. Advanced tools are also used to automate DNA sequence assembly (reconstruction of an original DNA sequence). Advanced annotation tools/instruments are used to annotate DNA sequences by identifying portions of the genome that do and do not code for proteins, including supporting biological information.


5.1.3高级分析工具-该要素代表实验室中使用的一大类高级分析工具。例子包括基因组学的科学领域，其中使用先进的工具来研究完整的基因组（生物体内的遗传物质）。高通量基因组分析仪仪器自动化流程步骤，提供结合DNA分子、引物、聚合酶扩增、成像和计算功能的高容量平行操作，以产生低成本的DNA测序输出。高级工具也用于自动化DNA序列组装（重建原始DNA序列）。高级注释工具/仪器用于通过识别基因组中编码和不编码蛋白质的部分来注释DNA序列，包括支持生物学信息。


5.1.4 Field Data Capture Systems--Field data capture and analysis is important to several industries, including many which do their own laboratory testing, Along with its base functionality, these systems are also often capable of integrating supervisory control and data acquisition (SCADA)-related infrastructure data (for example, from a wastewater or oil and gas stream) with manually collected field worker data to improve test data analysis and decision support. They also typically allow native mobile apps to integrate, improving data analysis and reducing manual data input time.


5.1.4现场数据采集系统--现场数据采集和分析对几个行业很重要，包括许多自己做实验室测试的行业，除了其基础功能，这些系统还经常能够整合监督控制和数据采集(SCADA)相关的基础设施数据（例如，来自废水或油气流），手动收集现场工作人员数据，以改善试验数据分析和决策支持。它们通常还允许本地移动app集成，改善数据分析并减少手动数据输入时间。

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5.1.2 Instrument Data Systems-Some laboratory systems are purpose-made for specific instruments, with the CDS being a common case. Other examples include data acquisition systems (DAQ) for calibration equipment and digital oscillo-scope software. These systems excel at "talking" with specific instrument, capturing the data, and providing custom analysis tools related to the captured data. In the case of chromatography, a CDS will often both control a chromatography or spectroscopy instrument and provide a visual report of the chromatogram with contextual meaning. DAQ for calibraton instruments may include additional functionality such as real-time data visualization and certificate generation.


5.1.2仪器数据系统-一些实验室系统是专门为特定仪器定制的，CDS是常见情况。其他例子包括用于校准设备的数据采集系统(DAQ)和数字示波器软件。这些系统擅长与特定仪器“交谈”，捕获数据，并提供与捕获数据相关的自定义分析工具。对于色谱法，CDS通常将控制色谱法或光谱学仪器，并提供具有上下文含义的色谱图目视报告。校准仪器的DAQ可能包括其他功能，如实时数据可视化和证书生成。


5.1.3 Advanced Analytics Tools - This element represents a broad category of advanced analytical tools used in laboratories. Examples include the scientific field of genomics in which advanced tools are used to study complete genome (genetic material within an organism). High-throughput genome analyzer instruments automate process steps to provide high volume parallel operations that combine DNA molecules, primers, polymerase amplification, imaging, and computational functions to yield low-cost DNA sequencing outputs. Advanced tools are also used to automate DNA sequence assembly (reconstruction of an original DNA sequence). Advanced annotation tools/instruments are used to annotate DNA sequences by identifying portions of the genome that do and do not code for proteins, including supporting biological information.


5.1.3高级分析工具-该要素代表实验室中使用的一大类高级分析工具。例子包括基因组学的科学领域，其中使用先进的工具来研究完整的基因组（生物体内的遗传物质）。高通量基因组分析仪仪器自动化流程步骤，提供结合DNA分子、引物、聚合酶扩增、成像和计算功能的高容量平行操作，以产生低成本的DNA测序输出。高级工具也用于自动化DNA序列组装（重建原始DNA序列）。高级注释工具/仪器用于通过识别基因组中编码和不编码蛋白质的部分来注释DNA序列，包括支持生物学信息。


5.1.4 Field Data Capture Systems--Field data capture and analysis is important to several industries, including many which do their own laboratory testing, Along with its base functionality, these systems are also often capable of integrating supervisory control and data acquisition (SCADA)-related infrastructure data (for example, from a wastewater or oil and gas stream) with manually collected field worker data to improve test data analysis and decision support. They also typically allow native mobile apps to integrate, improving data analysis and reducing manual data input time.


5.1.4现场数据采集系统--现场数据采集和分析对几个行业很重要，包括许多自己做实验室测试的行业，除了其基础功能，这些系统还经常能够整合监督控制和数据采集(SCADA)相关的基础设施数据（例如，来自废水或油气流），手动收集现场工作人员数据，以改善试验数据分析和决策支持。它们通常还允许本地移动app集成，改善数据分析并减少手动数据输入时间。

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图一：实验室信息学系统整合概念模型

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5.1.5 Laboratory Support Systems--These systems are typically ancillary to the core systems, filling in functionality gaps that the core systems do not provide. Quality management software is one such example, used to formulate quality policy and objectives, standard operating procedures, and the required records used for the quality certification process. Middleware that can handle auto-validation of samples represents another
example. The following may all be considered part of this category:


5.1.5实验室支持系统-这些系统通常是核心系统的辅助，填补了核心系统无法提供的功能空白。质量管理软件就是这样一个例子，用于制定质量政策和目标、标准操作规程以及用于质量认证过程的所需记录。可以处理样本自动验证的中间件可以作为另一个示例。以下均可视为该类别的一部分：


5.1.5.1 Artificial intelligence (AI) tools and algorithms are being used by researchers to inspect data better and make discoveries in the laboratory, while laboratory tools such as freezers, incubators, and air-cleaning systems are becoming "smarter" with added sensors that can feed data to one or more software systems for monitoring.


5.1.5.1人工智能(AI)工具和算法正被研究人员用来更好地检查数据，并在实验室中做出发现，而冷冻箱、培养箱和空气净化系统等实验室工具随着增加的传感器可以将数据反馈给一个或多个软件系统进行监测而变得“更智能”。


5.1.5.2 Batch and lot management tools assist with the creation, genealogy, review, and disposition of samples en masse, in the process assigning the same properties and process tracking for increased efficiency.


5.1.5.2批次和亚批管理工具协助整体地创建、谱系、审查和处置样品，在过程中分配相同的属性和过程跟踪，以提高效率。


5.1.5.3 Capacity planning and laboratory scheduling improves the efficiency of laboratory workflow, allowing laboratory personnel to use better the time and resources of available personnel and equipment. Such tools take into account scheduled instrument maintenance and planned time off of researchers.


5.1.5.3能力规划和实验室安排改善了实验室工作流程的效率，使实验室人员能够更好地利用可用人员和设备的时间和资源。此类工具考虑了计划的仪器维护和研究人员计划的休息时间。


5.1.5.4 Compliance management, whether embedded in a core system or installed as a support system, helps keep laboratories of all types on track with complying with government regulations. Aside from the typical audit trails and electronic signatures, compliance management tools also may assist with risk assessments, business governance, and contractual obligation management.


5.1.5.4合规管理，无论是嵌入核心系统还是作为支持系统安装，都有助于使所有类型的实验室保持符合政府法规的轨道。除了典型的审计跟踪和电子签名之外，合规管理工具还可以协助进行风险评估、业务管理和合同义务管理。


5.1.5.5 Data integrity is a core competency and expectation of laboratory informatics solutions. Data integrity includes the maintenance and assurance of the accuracy and consistency of data over its entire life cycle. Data integrity is a critical design requirement that touches every element of laboratory informatics that stores, processes, or retrieves laboratory data. (See Section 10 for more on data integrity.)


5.1.5.5数据完整性是实验室信息学解决方案的核心能力和期望。数据完整性包括在其整个生命周期内维护和保证数据的准确性和一致性。数据完整性是一个关键的设计要求，涉及实验室信息学中存储、处理或检索实验室数据的每个要素。（有关数据完整性的更多信息，请参见第10节。）


5.1.5.6 Human resource management encompasses the activities associated with managing the data surrounding laboratory personnel. This includes the management of analyst training records, qualifications, certifications, and performance.


5.1.5.6人力资源管理包括与管理实验室人员数据有关的活动。这包括分析员培训记录、资质、认证和绩效的管理。


5.1.5.7 Instrument and equipment management tools help to determine on- or offline status, assist with calibration management, update service and preventative maintenance schedules, and present the qualification status of instruments for proper safety and functionality.


5.1.5.7仪器和设备管理工具有助于确定在线或离线状态，协助校准管理，更新服务和预防性维护计划，并提供仪器的合格状态，以确保其具有适当的安全性和功能性。


5.1.5.8 Instrument data capture and control can provide some of the greatest benefits to both laboratory efficiency and data quality and integrity. Traditionally, laboratory informatics tools have allowed direct data capture from simple instruments, indirect data capture through instrument data file parsing, and, less frequently, bi-directional control of simple and file-based instruments. Although it is common for laboratory informatics solutions to have integrated instrument integration tools, there are also standalone instrument integration systems that may act as a data capture hub for the laboratory by connecting many instruments to a single application. These hub systems may then be interfaced to laboratory informatics solutions to provide data from multiple instruments via a single system-to-system interface. Instrument integration may also be available as a service, hosted in the cloud or on-premises, and newer instrumentation and devices are available that take advantage of the internet of things (loT) web connection capabilities that share their data.


5.1.5.8仪器数据采集和控制可以为实验室效率以及数据质量和完整性提供一些最大的益处。传统上，实验室信息学工具允许从简单的仪器中直接采集数据，通过仪器数据文件解析间接采集数据，并且较少对简单和基于文件的仪器进行双向控制。虽然实验室信息学解决方案有集成的仪器集成工具是很常见的，但也有独立的仪器集成系统，可能通过将许多仪器连接到单个应用程序上，作为实验室的数据捕获枢纽。然后，可将这些枢纽系统连接到实验室信息学解决方案，通过单个系统-系统接口提供来自多台仪器的数据。仪器集成也可以作为服务提供，托管在云或本地，并且可以使用新的仪器和设备，利用共享其数据的物联网 (loT) 网络连接能力。


5.1.5.9 Inventory management involves the handling of controlled substances, reagents, and retention samples, and it may also handle stability analysis.


5.1.5.9库存管理包括管制物质、试剂和留样的处理，也可以处理稳定性分析。


5.1.5.10 Investigation management is important to researchers and manufacturers in the pharmaceutical, medical device, and biologics industries as part of a regulated process that strives to ensure the safety and compliance of the components, containers, in-process materials, and finished products of those industries. This includes the management of the FDA's out-of-specification (OOS) requirements, internal out-of-trend
(OOT) requirements, process validation efforts, and any corrective and preventative action (CAPA) efforts related to investigations.


5.1.5.10调查管理对于制药、医疗器械和生物制剂行业的研究人员和生产商非常重要，作为监管流程的一部分，努力确保这些行业的组件、容器、过程材料和成品的安全性和合规性。这包括FDA OOS要求的管理、内部OOT要求、工艺验证工作以及与调查相关的任何CAPA工作。


5.1.5.11 Process improvement is generally thought of as a series of discussions and changes towards making the laboratory leaner, often through the concept of continuous improvement. However, visual project management "dashboards" that collate performance data and visually present it may be integrated with other laboratory informatics tools.


5.1.5.11 流程改进通常被认为是为了使实验室更加精简而进行的一系列讨论和改变，通常是通过持续改进的概念。然而，整理表现数据并直观呈现的可视化项目管理“dashboard”可能与其他实验室信息学工具集成。


5.1.5.12 Scheduled event management is enhanced through the use of event management and planning software or functionality. In particular, laboratories with shifts or rotations that move laboratory personnel around in various sections benefit from tools that assist with routine and nonroutine work.


5.1.5.12通过使用事件管理和规划软件或功能，加强计划事件管理。特别是轮班或轮换的实验室，使实验室人员在各个部分四处流动，受益于辅助常规和非常规工作的工具。


5.1.5.13 Scientific data management does not necessarily need to be limited to an SDMS. Other core, instrument data, and stand-alone laboratory systems are designed to manage a wide variety of instrument raw data files, documents, and experiments for the purpose of improved data homogenization, sharing, and mining.


5.1.5.13科学数据管理不一定仅限于SDMS。其他核心、仪器数据和独立的实验室系统旨在管理各种各样的仪器原始数据文件、文件和实验，目的是改进数据同质化、共享和挖掘。


5.1.5.14 Standard and reagent management encompasses verifying the storage, quantity, on-order, and expiry status of a wide variety of inventoried additives, chemicals, and calibration standards.


5.1.5.14标准品和试剂管理包括验证各种库存添加剂、化学品和校准标准品的储存、数量、订购和失效状态。


5.1.5.15 Statistical trending and control charting is part of the data analysis and visualization efforts of a software system. These tools allow laboratory personnel to improve decision making and process development, among other things.


5.1.5.15统计趋势和控制图是软件系统数据分析和可视化工作的一部分。这些工具允许实验室人员改善决策和过程开发等。


5.1.5.16 Systems integration functionality allows laboratory informatics systems, business management systems (for example, enterprise resource planning (ERP)), manufacturing systems [for example, manufacturing execution system (MES)], process management tools (for example, SCADA), document management systems, statistical analysis tools, and more to connect with each other and limit the amount of "siloed" or isolated data in an organization.

5.1.5.16系统集成功能允许实验室信息学系统、业务管理系统（例如，企业资源规划(ERP)）、生产系统[例如，生产执行系统(MES)]、过程管理工具（例如，SCADA）、文件管理系统、统计分析工具、以及更多的相互连接，并限制组织中“孤岛”或孤立数据的数量。

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5.1.6 Artificial Intelligence--The application of Al in clinical, research, and industrial laboratories is still in relative infancy, though laboratory personnel and researchers alike are increasingly finding ways to integrate Al into laboratory workflow. With Al's ability to "learn" from user interactions, data feeds, and more to improve or make insights into laboratory processes and data, the technology will likely continue to find ways into the laboratory. Machine learning tools that analyze structured data and natural language processing tools that extract information from unstructured data are already finding their way into some clinical laboratory work
flows.


5.1.6人工智能--Al在临床、研究和工业实验室中的应用仍处于相对起步阶段，尽管实验室人员和研究人员都越来越多地找到将Al整合到实验室工作流程中的方法。随着Al能够从用户互动、数据提要等中“学习”，改善或洞察实验室过程和数据，该技术将可能继续找到进入实验室的方法。分析结构化数据的机器学习工具和从非结构化数据中提取信息的自然语言处理工具已经在寻找进入一些临床实验室工作流程的途径。


5.1.7 Platform and Administration Support--Platform and administration support tools not only help laboratory informatics end users develop and maintain important documentation, they also allow systems administrators and IT personnel to provide a better, more secure experience. These tools may be built into a core system or act as standalone components of an interconnected web of systems. Functionality among these
tools includes document management (for standard operating procedures, specifications, training material, and so forth), configuration management (for handling all aspects of master data, electronic signatures, and so forth), system validation and commission (for requirements, specifications, test scripts, regression and stress tests, and so forth), system administration, and electronic security and privacy management.


5.1.7平台和管理支持--平台和管理支持工具不仅帮助实验室信息学终端用户开发和维护重要文档，还允许系统管理员和IT人员提供更好、更安全的体验。这些工具可以内置到核心系统中，也可以作为互连系统网络的独立组件。这些工具中的功能包括文档管理（用于标准操作程序、规格、培训材料等）、配置管理（用于处理主数据、电子签名等的所有方面）、系统验证和调试（用于需求、规格、测试脚本、回归和应力测试，等）、系统管理、电子安全和隐私管理。


5.1.8 Data Warehouse---An SDMS is a type of data warehouse in which a series of agreed-upon rules dictate how the incoming data is processed and structured. But other forms of data warehousing exist as well, which both clinical and nonclinical laboratories alike are using it viably. The late-binding or "data lake" approach to data storage represents a more modern architecture that takes in data of various structural states, tags it with appropriate metadata, and flattens it, leaving it alone otherwise until queried or used, or both. These data storage systems may be hosted locally or offered as a service over the cloud.


5.1.8数据仓库——SDMS是一种数据仓库，其中一系列预先制定的规则规定如何处理和结构化传入的数据。但也存在其他形式的数据仓库，临床和非临床实验室都在使用它。数据存储的后期绑定或“数据湖”方法代表了一种更现代的体系结构，它接受各种结构状态的数据，用适当的元数据标记它，并将其展平，否则将其单独保留，直到查询或使用，或两者兼而有之。这些数据存储系统可以在本地托管，也可以作为云上的服务提供。


5.1.9 Web Portals---These tools allow third parties to access, in a controlled fashion, one or more laboratory informatics data repositories or data management systems. They are often part of an existing system such as a LIMS but may also be standalone with a connection to one or more databases. The level of access granted to individuals or groups are controlled by the web portal's administrative tools.


5.1.9入口网站--这些工具允许第三方以受控方式访问一个或多个实验室信息学数据存储库或数据管理系统。它们通常是现有系统（如LIMS）的一部分，但也可以是连接到一个或多个数据库的独立系统。授予个人或团体的访问级别由门户网站的管理工具控制。

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5.2 Laboratory Informatics Systems Evolution----Laboratory informatics systems have evolved over time, with developers adding and expanding functions as capabilities and needs change. LIMS and instrument data systems such as CDS began by performing simple laboratory functions. Over time, additional software tools entered the laboratory, and existing software products added functionality. Fig. 2 shows a timeline for the development of software products designed to meet the needs of the laboratory community. The expanding breadth of tools available illustrates the increased functionality and complexity of laboratory informatics solutions. The laboratory informatics solutions illustrated in this figure are examples, however, and do not imply that these are the only tools available.


5.2实验室信息学系统进化--随着时间的推移，实验室信息学系统不断进化，随着能力和需求的变化，开发人员增加和扩展功能。LIMS和CDS等仪器数据系统从执行简单的实验室功能开始。随着时间的推移，额外的软件工具进入实验室，现有的软件产品增加了功能。图2显示了设计用于满足实验室区域需求的软件产品的开发时间表。可用工具的广度不断扩展说明了实验室信息学解决方案的功能和复杂性的增加。然而，本图中所示的实验室信息学解决方案是示例，并不意味着这些是唯一可用的工具。


5.3 Laboratory Informatics Functions---Laboratory informatics systems contain a rich set of functions. In Fig. 3, those functions using the primary functional categories that can be found in laboratory informatics systems are illustrated. At the center of these laboratory informatics functions is core laboratory testing (items labeled as C-x), including sample/experiment registration, sample management, testing, result review, sample approval, and reporting. Core laboratory testing is then supported by extended functions (items labeled as E-x), including planning and scheduling, instrument management, reagent and standard management, material disposition, experiment approvals, reporting, and trending. Finally, those core and supporting functions are propped up by laboratory informatics platform and administration support functions (items labeled as S-x and D-x), including master data management. Note that the labels assigned to these various sections directly tie into the items found in the requirements checklist of Appendix X1.


5.3实验室信息学功能--实验室信息学系统包含了丰富的功能。在图3中，说明了使用实验室信息学系统中的主要功能类别的功能。这些实验室信息学功能的核心是核心实验室检测（标记为C-x的项目），包括样本/实验登记、样本管理、检测、结果审查、样本批准和报告。然后，核心实验室检测得到扩展功能（项目标记为E-x）的支持，包括计划和调度、仪器管理、试剂和标准管理、材料处置、实验批准、报告和趋势分析。最后，这些核心和支持功能由实验室信息学平台和管理支持功能（标记为S-x和D-x的项目）支撑，包括主数据管理。请注意，分配给这些不同部分的标签直接与附录X1的要求检查表中的项目相关联。


5.4 Laboratory Informatics Functions for Specific Industries--Laboratory informatics encompasses many technical solutions such as LIMS, LIS, ELN, and CDS, which aim to improve laboratory operations. However, not all laboratory workflows are the same. Laboratories in specific industries may require additional functionality to meet their workflow requirements. Some laboratory informatics systems vendors may design their solutions to meet the needs of many laboratory types, while others may design theme for a particular industry segment with particular functionality needs. An environmental laboratory, for example, may require tracking of sample containers, processing of samples in batches with control samples, instrument integration, multiple levels of review, and specific reporting requirements. In Fig. 4, some of the additional functions that may be required to address the needs of specific laboratory types are illustrated. The functions illustrated are over and above the basic laboratory workflow and are by no means an exhaustive list; these are merely examples of possible additional functionalities.


5.4特定工业的实验室信息学功能--实验室信息学包括许多技术解决方案，如LIMS、LIS、ELN和CDS，旨在改善实验室操作。然而，并非所有的实验室工作流程都是相同的。特定行业的实验室可能需要额外的功能来满足其工作流程要求。一些实验室信息学系统供应商可以设计他们的解决方案来满足许多实验室类型的需求，而另一些供应商可以为具有特定功能需求的特定行业细分市场设计主题。例如，环境实验室可能需要跟踪样本容器、分批处理样本与对照样本、仪器集成、多层次审核和特定的报告要求。在图4中，说明了解决特定实验室类型需求可能需要的一些附加功能。图示的功能超出了基本实验室工作流程，绝不是详尽列表；这些只是可能的附加功能的示例。

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5.5 Laboratory Informatics Implementation and Technological Considerations--As with most any other software system, implementing laboratory informatics software in a laboratory requires at least a minimum set of plans and considerations. What operating systems are supported? What instruments shall be connected? Does the system need to be customized frequently? These types of questions quickly multiply as system complexity increases, operational needs expand, and integration needs become more complex. This line of questioning can become so complicated that functional requirements checklists are required to make sense of how a laboratory informatics solution fits into organizational workflow. As such, gaining a clearer understanding of a laboratory informatics solution's life cycle, as well as the technological elements that go into its development and implementation, is vital to its successful operation in the laboratory.


5.5实验室信息学实施和技术考量--与大多数其他软件系统一样，在实验室中实施实验室信息学软件至少需要一套最低限度的计划和考量。支持哪些操作系统？应连接哪些仪器？系统是否需要经常定制？这些类型的问题随着系统复杂性的增加、操作需求的扩大和集成需求变得更加复杂而迅速增加。这些问题可能变得如此复杂，以至于需要功能需求检查表来理解实验室信息学解决方案如何适合组织工作流程。因此，更清楚地了解实验室信息学解决方案的生命周期，以及进入其开发和实施的技术要素，对其在实验室中的成功运行至关重要。


5.5.1 The implementation life cycle of laboratory informatics systems is not unlike other software systems, though it comes with its own intricacies, including regulatory and "best practice" considerations specific to clinical, research, and other types of laboratories. The laboratory informatics system life cycle is described in more detail in Section 8.


5.5.1实验室信息学系统的实施生命周期与其他软件系统不同，尽管它有其自身的复杂性，包括针对临床、研究和其他类型实验室的监管和“最佳实践”考虑。实验室信息学系统的生命周期详见第8节。


5.5.2 The technological elements of laboratory informatics systems and their design and deployment are worth noting. Technologies such as cloud computing and application programming interfaces (API) are continuing to shape system development and implementation across a wide variety of laboratory types. Key factors such as hardware infrastructure, application platform, and integration needs further drive decisions of how a system should be developed and deployed, thus influencing the use of technological elements towards those goals. The following may all be considered technological elements relevant to laboratory informatics systems' development and deployment:


5.5.2实验室信息学系统的技术要素及其设计和部署值得注意。云计算和应用程序编程接口(API)等技术正在继续塑造跨越各种实验室类型的系统开发和实现。硬件基础设施、应用平台和集成等关键因素需要进一步推动如何开发和部署系统的决策，从而影响技术要素对这些目标的使用。以下均可视为与实验室信息系统开发和部署相关的技术要素：


5.5.2.1 APIs allow software components to communicate clearly with each other, improving the way laboratory informatics applications are built and used. They not only allow core developers to simplify programming but also allow end users who need to customize aspects of how the software is used (and outputs data) a simpler way to do that. Some software vendors may ship their laboratory informatics offering with an API or software development kit (SDK) so the end user can simplify how the software exchanges data with or calls background services from other systems in the laboratory, as well as customize the software in other ways.


5.5.2.1 API允许软件组件彼此清晰地沟通，改进实验室信息学应用的构建和使用方式。它们不仅允许核心开发人员简化编程，还允许需要定制软件如何使用（和输出数据）方面的最终用户以更简单的方式这样做。一些软件供应商可以提供API或软件开发工具包(SDK)来提供实验室信息学服务，以便最终用户可以简化软件如何与实验室中的其他系统交换数据或调用后台服务，以及以其他方式定制软件。


5.5.2.2 Artificial intelligence (AI) and its application in the laboratory continue to grow with the use of smart objects, machine learning applied to laboratory data, and monitoring of laboratory processes and resources. See Section 11 for additional details on this subject.


5.5.2.2人工智能(AI)及其在实验室中的应用随着智能对象的使用、应用于实验室数据的机器学习以及实验室过程和资源的监测而持续增长。有关该主题的更多详细信息，请参见第11节。


5.5.2.3 Authentication mechanisms and protocols such as single sign-on (SSO) and OAuth are important to the security of a laboratory informatics application, and the decision of which to use is not taken lightly. These mechanisms and protocols help define how information is securely stored, accessed, and propagated. In some cases, regulations such as the Health Insurance Portability and Accountability Act (HIPAA) may further dictate selection, particularly in cases in which electronic-protected health information (PHI) is involved.


5.5.2.3认证机制和协议，如单一登录(SSO)和OAuth，对实验室信息学应用的安全性很重要，使用这些机制和协议的决定并不掉以轻心。这些机制和协议有助于定义信息如何安全存储、访问和传播。在某些情况下，健康保险流通与责任法案(HIPAA)等法规可能进一步规定选择，尤其是涉及电子保护健康信息(PHI)的情况。


5.5.2.4 Business intelligence (BI) and big data technology components increasingly find their way into or tangentially connected to laboratory informatics. This includes data warehousing, data mining, data analytics, data structuring, and web portal components that aid in the processing, managing, visualization, and dissemination of all sorts of data.


5.5.2.4业务智能(B1)和大数据技术组件越来越多地找到进入或间接地连接实验室信息学的途径。这包括数据仓库、数据挖掘、数据分析、数据结构和门户网站组件，帮助处理、管理、可视化和传播各种数据。


5.5.2.5 Cloud computing and storage offers a virtualized alternative to how laboratory informatics applications and data are leveraged. Whether because of a lack of information technology resources and internal expertise or a desire to reduce operating costs, informatics vendors and businesses alike are turning software into a service, accessible via the internet or over a private network. This technology has benefits and drawbacks, though it has also become more refined over the years. (See 7.1 for more.)


5.5.2.5云计算和存储为如何利用实验室信息学应用和数据提供了一种虚拟化的替代方法。无论是由于缺乏信息技术资源和内部专业知识，还是为了降低运营成本，信息学供应商和企业都在将软件变成一种服务，通过互联网或私人网络访问。这种技术有好处和缺点，尽管多年来它也变得更加精细。（更多详情请参见7.1）


5.5.2.6 Data migration tools represent another important component of laboratory informatics deployment; many systems are not put into operation with a blank database. These tools are often standalone, meaning they do not become a permanent part of the overall informatics infrastructure, and they also may have data cleaning and transformation functionality to ensure data is in the right form when it arrives to its destination.


5.5.2.6数据迁移工具代表了实验室信息学部署的另一个重要组成部分；许多系统投入运行的时候并非是空白数据库。这些工具通常是独立的，这意味着它们不会成为整体信息学基础设施的永久部分，它们还可能具有数据清理和转换功能，以确保数据到达目的地时处于正确的形式。


5.5.2.7 loT devices have made their way to laboratories, promising greater productivity and insights. Collections of networkable sensors and devices give laboratories the option to interconnect, monitor, and make insights into various data streams, whether located in-house or thousands of miles away. However, implementing loT in the laboratory comes with its own considerations, including communication methods, security, and quality of data. (See 7.2 for more.)


5.5.2.7物联网已进入实验室，有望提高生产率并提供更多信息。可联网的传感器的和设备的组合使实验室可以选择互连、监测和深入了解各种数据流，无论是位于内部还是数千英里以外。然而，在实验室中实现物联网有自己的考虑，包括通信方法、安全性和数据质量。（更多详情请参见第7.2节）


5.5.2.8 Software or technology stacks are essentially a collection of software subsystems or components that allow for the development and operation of a complete laboratory informatics solution. These subsystems and components are often chosen together frequently because of a solid history of integrating well, as well as the availability of abundant documentation to support the implementation of the stack. A bioinformatics application, for example, may be built on a LAMP stack that includes Linux, Apache HTTP Server, MySQL, and Python.


5.5.2.8软件或技术堆栈本质上是允许开发和操作完整实验室信息学解决方案的软件子系统或组件的集合。这些子系统和组件通常经常一起选择，因为其有着良好的集成合作的历史，以及支持堆栈实现的丰富文档的可用性。例如，生物信息学应用程序可以构建在LAMP堆栈上，该堆栈包括Linux、Apache HTTP Server、MySQL和Python。

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5.6 Laboratory Informatics: Standardization, Authentication, and Integration--Several trends in laboratory informatics development, implementation, and use have slowly emerged, with a reasonable chance of impacting how laboratories manage data and its security. The concepts of data standardization, blockchain-based authentication, and end-to-end data integration are briefly described here.


5.6实验室信息学：标准化、认证和集成--实验室信息学发展、实施和使用的几个趋势慢慢出现，有合理的机会影响实验室如何管理数据及其安全性。这里简要描述了数据标准化、基于块链的身份验证和端到端数据集成的概念。


5.6.1 Laboratory Informatics data standardization efforts have been active across multiple years and in different analytical techniques and industry segments. The adoption of data standards covering different laboratory instruments, different computer architectures, databases, and operating systems across different industry segments and laboratory types has been a slow tedious process. The demand and requirements for
improved data-sharing mechanisms have expanded, driving further discussion and work efforts concerning electronic data formats. Data sources across a single scientific enterprise may number in the tens, making data sharing more difficult across hundreds of other such enterprises, many of which may include older or proprietary data sources that are not easily used by other entities. The durability of analytical data over long time
periods is a growing concern. Data sources may be unorthodox too, such as from social media, focus groups, or regulatory filings in addition to traditional laboratory instrument data outputs. Emphasis on better managing, sharing, and mining this data has grown considerably, such that funding agencies, data organizations, standard organizations (like AST) and academic publishers are beginning to provide recommendations and requirements for shared laboratory data and how it is formatted, stored, and publicly accessed. Standardized electronic formats are important to laboratories of all types, especially public health and pharmaceutical laboratories. Groups such as Observational Health Data Sciences and Informatics are promoting data standardization efforts and data models such as the Observational Medical Outcomes Partnership (OMOP) Common Data Model in recognition of this growing need. The OMOP data model attempts to transform, characterize, and analyze a wide variety of varying data sources associated with health care delivery and research "to
ensure that research methods can be systematically applied to produce meaningfully comparable and reproducible results." Ongoing ATM efforts supporting laboratory data standards include the Analytical Information Markup Language (AnIML), an open XML-based format for analytical and biological data. The format supports data from many analytical and laboratory process techniques, enabling the creation of generic tools that can handle data from any instrument. Industry-specific consortiums (and their associated test method standards) have also made ongoing efforts to advance the management of laboratory data, though some require a license fee/commercial approach to generate data using standard formats.

5.6.1实验室信息学数据标准化工作多年来一直活跃在不同的分析技术和行业细分领域。采用涵盖不同实验室仪器、不同计算机架构、数据库和操作系统的数据标准跨越不同的行业细分领域和实验室类型一直是一个缓慢繁琐的过程。改进数据共享机制的需求和要求已经扩大，推动了关于电子数据格式的进一步讨论和工作努力。单个科学企业的数据源可能以几十为单位，使得其他数百家此类企业的数据共享更加困难，其中许多可能包括其他实体不容易使用的较老或专有数据源。长期分析数据的持久性越来越受到关注。数据来源也可能是非正统的，例如来自社交媒体、焦点小组或监管文件以及传统的实验室仪器数据输出。强调更好地管理、共享和挖掘这些数据已经有了长足的发展，这样资助机构、数据组织、标准组织（如AST）和学术出版商开始为共享的实验室数据以及如何格式化、存储和公开访问提供建议和要求。标准化的电子格式对所有类型的实验室都很重要，尤其是公共卫生和制药实验室。观察性健康数据科学和信息学等团体正在促进数据标准化工作和观察性医疗结果伙伴关系(OMOP)共同数据模型等数据模型，以认识到这种日益增长的需求。OMOP数据模型试图转换、表征和分析与医疗保健提供和研究相关的各种不同的数据源，“以确保研究方法可以系统地应用，以产生有意义的可比较和可重现的结果。”正在进行的支持实验室数据标准的ATM工作包括分析信息标记语言(AnIML)，这是一种基于XML的开放格式，用于分析和生物数据。该格式支持来自许多分析和实验室工艺技术的数据，从而能够创建可处理任何仪器数据的通用工具。行业特定协会（及其相关试验方法标准）也在不断努力推进实验室数据管理，尽管一些协会要求许可费/商业方法以标准格式生成数据。


5.6.2 Blockchain-based authentication and identity management was developed in the late 2000s, though it is in its infancy in relation to laboratory and medical data systems. The technology acts as a distributed ledger that enables multiple computer servers to access, reconcile, and lend integrity to a data record. Used initially with the management of digital currency records, some in the medical and laboratory communites are proposing the use of blockchains to improve data portability and security, particularly in the scope of electronic health records (EHR) and LIS. As data integrity, sharing, and portability take on greater importance for laboratories, blockchain technology offers a solution by storing, distributing, and allowing access to records such that all participants can interact without pre-existing trust (because of eryptographic techniques) while maintaining clear audit trails.


5.6.2基于区块链的认证和身份管理是在21世纪初末发展起来的，尽管在实验室和医疗数据系统方面还处于起步阶段。该技术作为一个分布式分类帐，使多个计算机服务器能够访问、协调和提供数据记录的完整性。最初与数字货币记录的管理一起使用，一些医疗和实验室社区提出使用区块链来提高数据的便携性和安全性，特别是在电子健康记录(EHR)和LIS的范围内。由于数据完整性、共享和可移植性对实验室更为重要，因此区块链技术提供了一种解决方案，通过存储、分发和允许访问记录，使得所有参与者都可以在没有预先存在的信任（因为原型技术）的情况下进行互动，同时保持清晰的审计跟踪。


5.6.3 End-to-end data integration in laboratory informatics is more a concept than a reality, yet years of informatics software development have seen the functionality of what was previously found in several systems make its way into a single system. The functionality, for example, of a LIS, traditionally reserved for clinical use, has found its way into what was traditionally a more research-based LIMS. This convergence of system functionality continues, with enterprise resource planning functions finding their way into laboratory informatics applications as well. Most commonly we find separate systems integrated via APIs and data transfer protocols; however, differences in data formats between systems and unelear data standards make merging laboratory and business data (to mine it effectively) more challenging.


5.6.3实验室信息学中的端到端数据集成更多的是一个概念而不是现实，然而多年的信息学软件开发已经看到了之前在几个系统中才有的功能集成在一个单一系统中。例如，LIS的功能，传统上保留用于临床，已经找到了进入传统上更基于研究的LIMS的方法。系统功能的这种趋同继续存在，企业资源规划功能也在实验室信息学应用中找到了自己的路。最常见的是，我们发现通过API和数据传输协议集成的单独系统；然而，系统和单线数据标准之间数据格式的差异使得合并实验室和业务数据（有效挖掘）更具挑战性。

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6. Laboratory Informatics Workflow and Sample Life Cycle

6.实验室信息学工作流程和样本生命周期



6.1 End-to-End Business Integration---Laboratory informatics solutions are central to the broader business that it supports, as illustrated in Fig. 5. Independent drivers create demand that feed into the laboratory environment. These factors are typically industry specific and may include clinical protocols, research projects, sales, production planning, stability and environmental monitoring, and investigations. Auxiliary and
concurrent workflows also exist that are driven by information flowing in or out of the active laboratory informatics workflow. Testing status, intermediate/in-process results distribution, investigation requirements,
instrument calibration and maintenance, standard and reagent management, and document management (for example, SOPs, change control, and validaton) are parallel processes that interface with the informatics
solution. Upon completion of laboratory testing, a series of broad workflows within the larger business may be initiated that provide feedback to the internal business or external customers. Data analytics and trending, performance metrics, dispositioning of samples, and data archiving are internally focused, while certificates of analysis (COAs), sales and distribution, sample storage, and external communication distributions may be delivered externally for use by customers.


6.1端到端业务整合--实验室信息学解决方案是其所支持的更广泛业务的核心，如图5所示。独立的驱动创造了进入实验室环境的需求。这些因素通常具有行业特异性，可能包括临床方案、研究项目、销售、生产计划、稳定性和环境监测以及调查。还存在由流入或流出活动实验室信息学工作流程的信息驱动的辅助和并发工作流程。检测状态、中间/过程中结果分布、调查要求、仪器校准和维护、标准和试剂管理以及文件管理（例如，SOP、变更控制和验证）是与信息学解决方案连接的并行过程。实验室检测完成后，可在较大的业务中启动一系列广泛的工作流程，向内部业务或外部客户提供反馈。数据分析和趋势分析、性能指标、样本处置和数据存档均在内部进行，而检验报告(COA)、销售和分销、样本储存和外部通信分销可在外部交付供客户使用。


6.2 Laboratory Informatics Workflow Introduction--The laboratory informatics workflow model shown in Fig. 6 provides a generic representation of the workflow in a typical analytical laboratory. Workflow steps shown are aligned with the system functions described elsewhere in this guide. The purpose of the workflow diagram is to depict the laboratory informatics functions and interaction points with typical laboratory work processes. Specific laboratory workflow requirements may vary widely from one laboratory to another, as well as from one industry to another. For example, in a research and development (R&D) organization, the laboratory process typically starts with experimental design. Information input for experimental design may come from literature or from computational models based on tools such as computational chemistry and bioinformatics. However, before implementing a laboratory informatics solution, care should be taken to define and document completely the unique requirements and workflow model for the laboratory in question. This should include clearly delineating the functional boundaries of the laboratory informatics tool(s) being implemented. To achieve a successful implementation and use of a laboratory informatics solution, it shall be properly configured to support the target workflow and requirements. In addition, there is normally a significant amount of static or master data required to be imported into the system before use. Once configured, the laboratory informatics solution is able to support the laboratory workflow and sample life-cycle processes.


6.2实验室信息学工作流程介绍--图6所示的实验室信息学工作流程模型提供了典型分析实验室工作流程的通用表示。显示的工作流程步骤与本指南其他地方描述的系统功能一致。工作流程图的目的是描述具有典型实验室工作流程的实验室信息学功能和交互点。具体的实验室工作流程要求可能因实验室而异，也可能因行业而异。例如，在研发(R&D)组织中，实验室过程通常从实验设计开始。实验设计的信息输入可能来自文献或基于计算化学和生物信息学等工具的计算模型。但是，在实施实验室信息学解决方案之前，应注意定义和完整记录所讨论实验室的独特要求和工作流程模型。这应包括清楚地描述正在实施的实验室信息学工具的功能边界。为成功实现和使用实验室信息学解决方案，应对其进行适当配置，以支持目标工作流程和要求。此外，通常需要在使用前将大量的静态或主数据导入到系统中。一旦配置，实验室信息学解决方案能够支持实验室工作流程和样本生命周期过程。


6.2.1 Laboratory Informaties Workflow Model-Defining the correct workflow model for the laboratory is essential to a successful laboratory informatics implementation and deployment. The laboratory informatics workflow model should support the business processes, which vary significantly in the level of flexibility they need to accommodate from industry to industry. At one end of the spectrum, discovery research requires a highly flexible process to support constantly changing directions and methodologies. In these laboratories, only high-level policies exist in data governance. There are usually limited SOPs on how scientists conduct their research. The data model shall be able to capture different ways of describing a data entity. In the middle of the spectrum, many laboratories opt for data models that are procedure-centric (that is, test methods are defined from approved external procedures and SOPs) in which the requestor selects the appropriate tests based on knowledge of which procedures are appropriate for the sample in question. This model relies on the experience of the user and has great flexibility for the R&D laboratory or laboratories in which a wide variety of samples are submitted. At the other end of the spectrum, the business process for quality control laboratories is sample or product specific. That is, a suite of "approved" tests are bundled together and typically always applied to one sample type or product. This model removes the dependency upon the requestor to select the appropriate tests when submitting the sample for analysis and improve compliance to testing protocols.


6.2.1实验室信息工作流程模型-为实验室定义正确的工作流程模型对于实验室信息学的成功实施和部署至关重要。实验室信息学工作流模型应支持业务流程，业务流程在行业间需要适应的灵活性水平上存在显著差异。一方面，发现研究需要一个高度灵活的过程来支持不断变化的方向和方法。在这些实验室中，只有高级政策存在于数据治理中。关于科学家如何进行研究的SOP通常有限。数据模型应能够捕获描述数据实体的不同方式。出于中间状态的情况是，许多实验室选择以程序为中心的数据模型（即，试验方法由批准的外部程序和SOP定义），其中申请人根据对所讨论样品适用程序的了解选择适当的试验。这种模式依靠用户的经验，对于研发实验室或提交各种各样样本的实验室具有很大的灵活性。另一个方面的情况是，质量控制实验室的业务流程是样本或产品特定的。也就是说，将一组“批准”的测试捆绑在一起，通常始终应用于一种样本类型或产品。该模型消除了申请人在提交样本进行分析时选择适当检测的依赖性，并提高了对检测方案的依从性。


6.2.2 Types of Data- The technology used by a laboratory informatics solution varies with each vendor and platform. However, laboratory informatics databases are typically divided into two broad areas: (1) static or master data in which descriptive information is defined to prepare the system for use (for example, users, locations, profiles, tests, calculations, specifications, and related information; commonly found in "look up/reference/dictionary" tables) and (2) dynamic data in which sample and result/determination information is stored as samples are logged and results are entered. The terms "static" and "dynamic" represent a general characterization of laboratory informatics data, reflecting the frequency of change. The laboratory informatics implementation team should assess the current full scope of laboratory information and workflow to gather the data necessary to set up the system to support their laboratory processes.


6.2.2数据类型-实验室信息学解决方案使用的技术因每个供应商和平台而异。然而，实验室信息学数据库通常分为两个广泛的领域：(1)静态或主数据，其中描述性信息被定义以便为系统的使用做准备（例如，用户、位置、配置文件、测试、计算、规范和相关信息；常见于“查找/参考/词典”表）和(2)动态数据，其中样本和结果/测定信息在样本记录和结果输入时被存储。术语“静态”和“动态”代表实验室信息学数据的一般表征，反映了变化频率。实验室信息学实施团队应评估当前实验室信息和工作流程的全部范围，以收集建立系统支持其实验室流程所需的数据。


6.2.3 Statuses---Laboratory informatics solutions are generally capable of maintaining information on the status of various items or activities as they progress through their associated workflows. Status information is normally updated automatically as each laboratory transaction takes place. This status information provides a representation in the system of the progress of items and activities through their defined workflows or life cycles. This ability to track status of work items in a central solution is one of the essential benefits of managing a laboratory with informatics solutions. For example, experiments, orders, samples, and individual tests or determinations are all items that may have a status associated with them in the solution to indicate where they sit in the progression of their workflow. In addition to using statuses to track progress, laboratory informatics solutions may also allow certain system actions to be taken or not taken by end users based upon the status of an item. For example, some solutions may be configured such that samples or orders should not be approved until all tests and samples are reviewed. Individual functions/workflows may also be configured to have certain actions automatically triggered upon a change in status. For example, some solutions may be configured such that samples are automatically moved to an approved status once all tests on that sample are reviewed and have passed defined acceptance criteria. Statuses are also useful within these solutions to filter information or objeets for actions. For example, some solutions may be configured such that samples can be made available for testing or placement on an electronic worklist only if they have been received in the laboratory as indicated by their status.


6.2.3状态--实验室信息学解决方案通常能够在各种项目或活动通过其相关工作流进行时维护关于其状态的信息。状态信息通常在每次实验室活动时自动更新。该状态信息在系统中提供了项目和活动通过其定义的工作流程或生命周期的进度表示。这种在中心解决方案中跟踪工作项目状态的能力是用信息学解决方案管理实验室的基本好处之一。例如，实验、订单、样本和单独测试或测定都是可能在解决方案中具有与其相关状态的项目，以指示它们在工作流进程中的位置。除了使用状态跟踪进度外，实验室信息学解决方案还可能允许最终用户根据项目状态采取或不采取某些系统操作。例如，一些解决方案的配置可能会使样本或订单在所有测试和样本审核之前不被批准。个别功能/工作流也可以配置为在状态变化时自动触发某些操作。例如，可以配置一些解决方案，使样本的所有测试都经过审查并通过规定的验收标准后，样本自动移动到批准状态。在这些解决方案中，状态也有助于过滤信息或行动目标。例如，可以配置一些解决方案，以便只有在实验室收到样本（根据其状态指示）后，才可以将样本用于检测或放置在电子工作表上。


6.2.4 Data Load and Migration--A laboratory informatics solution is capable of maintaining a broad range of business and laboratory data required for the effective operation of the laboratory. Laboratory informatics solutions contain data that not only reflect the current operational state of the laboratory but also historical information on past performance and events. When implementing a laboratory informatics solution in a
previously manual environment or replacing a legacy electronic system, it should be determined how much and what type (if any) of historical data should be carried forward (that is, loaded, "migrated," or re-entered) into the new laboratory informatics solution. Static data are normally loaded into the laboratory informatics solution (either manually or through electronic means) as part of preparing the system for use. The decision on how to deal with historical dynamic data should be evaluated on the basis of risk, value, specific customer requirements, and record retention policies. Appropriate strategies for dealing with this data include migration, preservation, and archiving. In cases in which a new solution is replacing an existing laboratory informatics solution, it may be possible to use technical means to migrate dynamic data from the legacy
laboratory informatics solution to the new target solution. Migration of data needs to be carefully analyzed and planned, including considering the hardware and software differences (such as database type) of the originating and destined locations. The plan should include steps to verify that the data are successfully migrated to the new database without error.

6.2.4数据加载和迁移--实验室信息学解决方案能够维护实验室有效运行所需的广泛业务和实验室数据。实验室信息学解决方案包含的数据不仅反映了实验室的当前运行状态，还反映了过去的表现和事件的历史信息。当在先前手动环境中实施实验室信息学解决方案或替换旧版电子系统时，应确定应将多少和何种类型（如果有）的历史数据结转（即加载、“迁移”或重新输入）到新的实验室信息学解决方案中。静态数据通常加载到实验室信息学解决方案（手动或通过电子方式）中，作为系统准备使用的一部分。应根据风险、价值、特定客户要求和记录保留政策评估如何处理历史动态数据的决定。处理该数据的适当策略包括迁移、保存和存档。在新解决方案正在取代现有实验室信息学解决方案的情况下，可以使用技术手段将动态数据从遗留的实验室信息学解决方案迁移到新的目标解决方案。需要对数据的迁移进行仔细分析和计划，包括考虑原点和目的地的硬件和软件差异（如数据库类型）。该计划应包括验证数据成功迁移到新数据库而没有错误的步骤。

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6.3 Sample and Test Life Cycle:


6.3样品和试验生命周期：


6.3.1 Sample Registration--Sample registration may precede or follow physical sample collection and arrival in the laboratory. The initiation of a request for testing/sampling generally starts the sample workflow process. Sample requests may include manual forms, electronic forms, phone requests, web requests, process-driven requests, time- or calendar-based requests, ad-hoc requests, and system-generated requests. Information obtained from the sample request should include biographical, client, test, and safety information. Some laboratory informatics solutions allow both the laboratory to pre-log or post-log samples and the client to pre-log samples through a self-service request interface. In some business processes, the sample request needs to be approved by project leaders before the samples are retrieved for the study to ensure that appropriate samples are allocated to high-priority projects.


6.3.1样品登记-样品登记可以在物理样品采集和到达实验室之前或之后进行。检测/采样请求的启动通常会启动样本工作流过程。样本请求可能包括人工形式、电子形式、电话请求、网络请求、流程驱动请求、基于时间或日历的请求、临时请求和系统生成的请求。从样品申请中获得的信息应包括样品生平信息、客户信息、检测信息和安全性信息。一些实验室信息学解决方案允许实验室预记录或后记录样本，客户通过自助请求界面预记录样本。在一些业务流程中，在取回样本进行研究之前，样本请求需要得到项目负责人的批准，以确保将适当的样本分配给高优先级项目。


6.3.2 Sample Identification--The laboratory informatics solution should assign a unique number to each sample registered (that is, submitted for testing). The unique number can be a system-generated sequential integer or a user-defined sequence. Multiple samples submitted together for testing should be logically "linked" in the laboratory informatics solution (for example, all samples for a particular lot or research project).The system will normally provide functionality to capture descriptive information about the sample(s) such as who submitted the sample(s), cost, sample description, storage conditions, and what tests are to be performed on the sample. Other information may also be important. such as the priority of the tests, what level of accuracy and precision of testing is needed, What hazards the sample might present to laboratory personnel, what approximate levels of components are expected, and what should be done with the sample when analysis is complete.


6.3.2样品识别-实验室信息学解决方案应为每个注册的样品（即提交检测的样品）分配一个唯一的编号。唯一数字可以是系统生成的顺序整数或用户定义的序列。在实验室信息学解决方案中，同时提交用于检测的多个样本应具有逻辑上的“链接”（例如，特定批次或研究项目的所有样本）。系统通常将提供采集样本描述性信息的功能，例如提交样本的人员、成本、样本描述、储存条件以及对样本进行的检测。其他信息也可能很重要。例如，检测的优先级、需要的检测准确度和精密度水平、样本可能对实验室工作人员造成的危害、预期组分的大致水平以及分析完成后应该对样本进行的操作。


6.3.2.1 A confirmation report is often issued (sometimes emailed) to assure requestors that the system accepted the sample request and may accompany the physical samples as they are delivered to the testing laboratory. Often, laboratory informatics solution statuses are updated for the sample/order and may be used to record the fact that an order was made (for keeping operational metrics) and when it was made so the
system can track the time intervals for the remaining steps of the process. This will also allow laboratory management to determine turnaround time, sample status, and various overdue conditions.


6.3.2.1通常会发布确认报告（有时通过电子邮件发送），以确保申请人系统接受样品请求，并可在将物理样品交付至检测实验室时随附该确认报告。通常情况下，会更新样本/订单的实验室信息学解决方案状态，并可用于记录下订单的事实（用于保持操作指标）以及何时下订单，以便系统能够跟踪剩余流程步骤的时间间隔。这也将允许实验室管理人员确定周转时间、样本状态和各种逾期情况。


6.3.3 Sample Collection---Sample collection may be a manual, automated, or robotic process. Sample collection logistics may become more efficient by having the laboratory informatics solution print collection lists and generate labels (for example, bar codes) for the sample containers. Sample collection can precede or follow sample registration as defined by the laboratory's workflow. The laboratory informatics solution can provide information on how to collect samples, specific sample plans, container and preservation requirements, safety information [such as safety data sheets (SDSs)], sample storage requirements, and sample routing information. Chain of custody for samples is often tracked by the laboratory informatics solution, generally for location and status information. Chain-of-custody may be required to provide documented evidence of control and traceability of sample containers and their contents. Examples of situations in which chain-of-custody requirements may be required include handling of controlled substances, pieces of evidence (forensic) supporting legal court cases, or radioactive materials. Note that this functionality may not have all legal chain of custody requirements for specified sample types as defined by governmental or law enforcement agencies. The implementation team should review these requirements carefully during the planning/implementation phase.


6.3.3样品采集-样品采集可以是手动、自动或机器人过程。通过拥有实验室信息学解决方案打印采集列表并为样本容器生成标签（例如条形码），样本采集物流可能会变得更有效。样本采集可以在实验室工作流程规定的样本登记之前或之后进行。实验室信息学解决方案可以提供关于如何采集样本、具体样本计划、容器和保存要求、安全信息[如安全数据表(SDS)]、样本储存要求和样本路由信息的信息。样本的保管链通常由实验室信息学解决方案跟踪，通常用于位置和状态信息。监管链可能需要提供样品容器及其内容物控制和可追溯性的书面证据。可能需要监管链要求的情况示例包括处理管制物质、支持法律法庭案件的证据（法医）或放射性材料。请注意，该功能可能不具备政府或执法机构定义的特定样本类型的所有法律监管链要求。实施团队应在计划/实施阶段仔细审查这些要求。


6.3.4 Sample Receipt--The physical receipt of samples in the laboratory may be recorded in the system and may also include initial sample checking and labeling. Sample orders or groups of samples may be reviewed against customer or project sampling requirements. Additional information such as the number (or quantities/amount) of samples received and the arrival time may be recorded and the status of samples may be
updated for the sample/order from "logged" to "received." Where collection lists are used, a "missed sample" report should be used to indicate those samples that were not received as expected.


6.3.4样品接收-实验室中样品的实体接收可以记录在系统中，还可以包括初始样品检查和贴标。可根据客户或项目抽样要求审查样本订单或样本组。可以记录额外信息，如接收的样本数量（或数量/数量）和到达时间，样本/订单的状态可以从“记录”更新为“接收”。当使用采集列表时，应使用“缺失样本”报告指出未按预期接收的样本。


6.3.4.1 The laboratory informatics solution may be configured to specify the aliquot requirements for a sample based on the tests to be performed. Upon sample receipt, any issues, such as an unexpected color or physical state, may be noted and recorded within the sample record. The laboratory informatics solution should be flexible enough to allow preliminary sample treatment, such as addition of a preservative, to be
performed and documented.


6.3.4.1实验室信息学解决方案可以配置为根据要进行的测试指定样品的等分要求。样品接收后，可能会发现一些问题，如非预期的颜色或物理状态，并记录在样品记录中。实验室信息学解决方案应足够灵活，以允许进行初步样本处理，例如添加防腐剂，并进行记录。


6.3.4.2 Store/Retrieve Sample----An often overlooked benefit of using a laboratory informatics solution is the ability to manage inventories for reference samples, laboratory reagents, standards, QC samples, time-based samples (shelf-life stability), and sample storage requirements in addition to normal samples. Inventory functions may provide critical business information with respect to resource and consumables management as well. This could include such information as expiration dates, vendor information, restock quantities, and so forth.


6.3.4.2储存/检索样本--使用实验室信息学解决方案经常被忽视的一个好处是，除了正常样本外，还能够管理参照样本、实验室试剂、标准品、QC样本、基于时间的样本（保质期稳定性）和样本储存要求的库存。库存功能还可以提供资源和耗材管理方面的关键业务信息。这可能包括失效日期、供应商信息、再库存数量等信息。


6.3.5 Sample Distribution---Distribution processes often include important laboratory informatics solution functions such as work lists, resource allocation, sample routing, and sample storage location tracking or chain of custody.


6.3.5样本分发---分发过程通常包括重要的实验室信息学解决方案功能，如工作列表、资源分配、样本途径和样本存储位置跟踪或监管链。


6.3.5.1 The laboratory informatics solution should provide a listing of all the tests that are to be performed, the amount of material required, and where samples are to be sent. The date and time of sample distribution is important since it designates when the sample becomes available to the various laboratory workstations for analysis. Sample status may be updated to indicate samples are available for analysis at this time as well.


6.3.5.1实验室信息学解决方案应提供待执行的所有测试的列表、所需材料的数量和将发送样本的位置。样本分发的日期和时间很重要，因为它指定了样本何时可用于各种实验室工作站进行分析。可更新样本状态，以表明此时也可获得样本进行分析。


6.3.6 Work Assignment---Once samples arrive in the laboratory, the work should be scheduled and allocated against available resources, people, or equipment, or combinations thereof. Resource availability and management may be handled through the laboratory informatics solution, if configured to capture this information. By utilizing the laboratory informatics solution appropriately, resources may be forecast,  allocated, and tracked to improve the overall efficiency of the laboratory. The laboratory informatics solution may also be configured, in some instances, to group samples automatically into runs or sequences and schedule work (tests) for each sample/order, as well as be configured to allow authorized users to perform these functions manually.


6.3.6工作任务分配-样品到达实验室后，应根据可用资源、人员或设备或其组合安排和分配工作。如果改系统被配置为可以捕获该信息，则可通过实验室信息学解决方案处理资源可用性和管理。通过适当利用实验室信息学解决方案，可以预测、分配和跟踪资源，以提高实验室的整体效率。在某些情况下，还可以配置实验室信息学解决方案，将样本自动分组为运行或序列，并为每个样本/订单安排工作（检测），以及配置为允许授权用户手动执行这些功能。

火页仔

6.4 Analysis:


分析


6.4.1 Sample Preparation--Most samples require some preparation before analysis. The laboratory informatics solution may be configured to provide sample preparation directions for these preliminary processing and sample preparation steps; however this information may also be available in the form of standard operating procedures, technical documents, or work instructions stored externally to the laboratory informatics solution. In addition, it may be configured to capture who prepared the sample and when sample preparation was completed.


6.4.1样品制备-大多数样品在分析前需要进行一些制备。可对实验室信息学解决方案进行配置，为这些初步处理和样本制备步骤提供样本制备指导；但是，该信息也可以标准操作规程、技术文件或存储在实验室信息学解决方案外部的工作说明的形式提供。此外，还可以配置为采集样品制备人和样品制备完成的时间。


6.4.2 Sample Analysis--Analysis activities will vary from laboratory to laboratory. Depending upon the laboratory's requirements and data model, a common objective is to record as much of the information gathered during this phase as possible within the laboratory informatics solutions. In general, the analysis phase contains the following subparts:


6.4.2样本分析-分析活动因实验室而异。根据实验室的要求和数据模型，一个共同的目标是在实验室信息学解决方案中记录该阶段收集的尽可能多的信息。一般而言，分析阶段包含以下子部分：


6.4.2.1 Perform Test--Test results/determinations are the main output of the analysis process. Intermediate and final test results for the samples, standards, and their associated QC samples may be reported out in hard copy, electronic formats, or both. In addition, the measurement process may produce values for additional internal blanks, standards, and instrument self-checks. The definition of what is the laboratory's "raw data and what needs to be retained for legal evidence may be defined differently for each client or agency involved and should be a fundamental part of the data model design process. The laboratory informatics solution should provide a means of contemporaneously capturing all activities undertaken during test performances, ensuring accuracy, traceability of actions, originality, and authenticity.


6.4.2.1进行检测-检测结果/测定是分析过程的主要输出。样品、标准品及其相关QC样品的中间和最终检测结果可以硬拷贝、电子格式或两者形式报告。此外，测量过程可能会产生额外的内部空白、标准品和仪器自检的检测值。实验室“原始数据”的定义和法律证据需要保留的定义可能对每个相关客户或机构有不同的定义，并且应该是数据模型设计过程的基本部分。实验室信息学解决方案应提供一种同时采集测试执行期间进行的所有活动的方法，确保准确性、行动的可追溯性、独创性和真实性。


6.4.2.2 Retest Loop--Retests can be initiated at multiple points in the laboratory informatics solution workflow. A retest is defined as one or more additional determinations on the original sample/order container. These retests would normally be ordered if a given test was suspected to fail for reasons that may include failed quality control parameters, instrument malfunction, or technical judgment. The laboratory informatics solution should document each retest along with an appropriate justification.


6.4.2.2复检循环--在实验室信息学解决方案工作流程中，可在多个点启动复检。复检定义为对原始样本/订单容器进行一次或多次额外测定。如果由于质量控制参数不合格、仪器故障或技术判断等原因怀疑给定检测失败，通常会安排这些复检。实验室信息学解决方案应记录每次复检以及适当的理由。


6.4.2.3 Resample Loop--Resamples can also be initiated at multiple points in the laboratory informatics solution workflow. A resample is defined as one or more additional samples. The laboratory informatics solution needs to establish forward and backward links to samples that are added by way of the resample loop. These resamples would normally be ordered if a given sample was compromised during initial testing (for example, sample tube dropped or contents spilled). They also would be reordered when a sample was suspected to have failed for reasons that include (1) insufficient amount of the sample was available for a retest, (2) technical judgment that the original sample was not appropriate for the test performed, or (3) test failure shall be confirmed/overcome.


6.4.2.3重新采样循环--在实验室信息学解决方案工作流程中，也可以在多个点启动重新采样。重新采样定义为一个或多个额外样本。实验室信息学解决方案需要建立通过重新采样回路添加的样本的正向和反向链接。如果初始测试期间给定样本受损（例如，样本管掉落或内容物溢出），通常会订购这些重新采样。当怀疑样品不合格时，也将对样品进行重新排序，原因包括(1)可用于复检的样品量不足，(2)技术判断原始样品不适合进行检测，或(3)应确认/克服检测失败。


6.4.3 Data Capture--The results of the analysis should be captured within the laboratory informatics solution. The amount and type of supporting data to include with the result data should be carefully evaluated and defined during the data model design, allowing for reconstruction of the critical activities that led to the test results. When a test result/determination is captured, the statuses of the sample/order and result determination should be updated. The associated date/time records should also be captured so that statistics of work accomplished and test progress can be tracked. The laboratory informatics solution should have validated electronic audit trails that record information about each transaction, both for initial entries as well as modifications to entries.


6.4.3数据采集-应在实验室信息学解决方案中采集分析结果。在数据模型设计过程中，应仔细评价并定义结果数据中包含的支持性数据量和类型，允许重建导致检测结果的关键活动。当获取测试结果/测定结果时，应更新样本/顺序和结果测定的状态。还应获取相关日期/时间记录，以便跟踪所完成工作和测试进度的统计。实验室信息学解决方案应具有经验证的电子审计跟踪，记录每次交易的信息，包括初始条目和条目修改。


6.4.3.1 While data capture may be a manual process in which results and parameters are manually entered by an analyst, the true power, efficieney, and data integrity benefits of laboratory informatics lies in electronic data capture. This can involve automated capture of instrument data files and data from simple devices, automatic extraction of information from one part of the laboratory informatics implementation and transfer into another one, and automatic issuing of reports. In cases in which instruments are bi-directionally interfaced to laboratory informatics solutions, the sequence of unknown samples and control standards may be transferred to the instrument to streamline instrument setup before analysis.


6.4.3.1虽然数据采集可能是分析员手动输入结果和参数的手动过程，但实验室信息学的真正功效、效率和数据完整性优势在于电子数据采集。这可能涉及从简单设备中自动捕获仪器数据文件和数据，从实验室信息学实施的一部分自动提取信息并转移到另一部分，以及自动发布报告。在仪器与实验室信息学解决方案双向连接的情况下，分析前可将未知样本和对照标准品的序列转移到仪器中以简化仪器设置。