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相关概念视频

How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
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How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
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Data Reporting and Recording01:24

Data Reporting and Recording

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Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
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Data Validation01:15

Data Validation

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Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
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Data Validation01:03

Data Validation

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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
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Data Collection II01:29

Data Collection II

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The nursing history captures and records the patient's health status, so that a care plan evolves to meet the patient's individual needs. The nursing health history is a part of the initial assessment. A comprehensive history covers all health dimensions and plays a significant role in the assessment process. A comprehensive history includes the patient's biographical information, reasons for seeking health care, expectations, present and past health history, medications, and...
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相关实验视频

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Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
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在数据表示和合规性测量中遵守数据驱动的指导方针:范围审查.

Minh Trang Hoang1, Candice Donnelly1, Christina Igasto1,2

  • 1Biomedical Informatics and Digital Health, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia, 61 401333970.

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概括

衡量在医疗保健中遵守最佳实践标准是一项挑战. 本综述综合了指导方针的可计算表示和遵守测量的方法,强调了需要上下文意识的方法来确保临床相关性.

关键词:
最好的做法是最好的做法.临床决策支持 临床决策支持临床实践指南 临床实践指南计算机可解释的指导方针.电子医疗记录 电子医疗记录符合指导方针的合规性.

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科学领域:

  • 医疗保健信息学 医疗保健信息学
  • 临床决策支持系统 临床决策支持系统
  • 医疗保健服务研究 医疗服务研究

背景情况:

  • 最佳实践标准旨在标准化护理和改善患者的治疗结果.
  • 临床实践存在差异,并非所有偏差都是不适当的.
  • 由于表示和数据忠实性的限制,测量标准的遵守是具有挑战性的.

研究的目的:

  • 调查和综合有关指南建议可计算表现的文献.
  • 探索检测和量化偏离最佳实践标准的方法.

主要方法:

  • 根据阿克西和奥马利框架和PRISMA-ScR指南进行范围审查.
  • 在2025年11月搜索了五个数据库 (Ovid Medline,EMBASE,IEEE Xplore,科学网,Scopus).
  • 包括描述可计算的标准表示或使用患者数据评估遵守标准的研究.

主要成果:

  • 包括24项研究,其中58%测量了坚持.
  • 心血管疾病是最常见的焦点 (54%).
  • 标准是使用BPMN,本体学,FHIR或混合方法正式制定的;基于规则的对齐是遵守测量的常见情况.
  • 大多数模型缺乏上下文敏感性和患者特异性因素,导致临床偏差未解决.

结论:

  • 在计算机可解释的表示和临床上有意义的坚持测量方面,仍然存在挑战.
  • 目前的方法侧重于技术调整而不是临床相关性,数据质量受到限制.
  • 需要在临床工作流程中整合上下文意识的标准化建模,以区分合理的和不合理的偏差,以获得更安全的患者护理.