Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Reliability and Validity01:29

Reliability and Validity

12.7K
Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
12.7K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

161
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
161
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

121
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
121
Data Validation01:03

Data Validation

5.0K
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...
5.0K
Data Reporting and Recording01:24

Data Reporting and Recording

4.6K
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...
4.6K
Observational Studies01:11

Observational Studies

8.2K
Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One...
8.2K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Medication-Wide Association Study of Alzheimer's Disease and Related Dementias: Identifying Drug Candidates from Electronic Health Records through Explainable AI.

medRxiv : the preprint server for health sciences·2026
Same author

Characteristics and Outcomes of Over 1 Million Veterans With Heart Failure Phenotyped Using Artificial Intelligence Approaches: the National DCVA-HF Registry.

Journal of cardiac failure·2026
Same author

Beware the Little Foxes that Spoil the Vines: Small Inconsistencies in Clinical Data Can Distort Machine Learning Findings.

Fortune journal of health sciences·2026
Same author

The Association of Iatrogenic Withdrawal With Opioid and Benzodiazepine Weaning in Children With Bronchiolitis: A Single-Center, Retrospective Cohort Study, 2012-2022.

Critical care explorations·2026
Same author

Target-Dose Versus Below-Target-Dose ACE Inhibitors and Lower Risk of Kidney Failure in U.S. Veterans with HFrEF.

European journal of heart failure·2026
Same author

Serum Magnesium and Outcomes in U.S. Veterans with Heart Failure.

The American journal of medicine·2026
Same journal

Towards responsible digital health implementation: A mixed-methods exploratory study developing a tool to assess workforce experience.

Digital health·2026
Same journal

From pharmacometric foundations to emerging artificial intelligence applications: A bibliometric analysis of model-informed precision dosing for anti-infective therapy (2005-2025).

Digital health·2026
Same journal

Multi-parameter prediction of extubation failure using spontaneous breathing trial and post-spontaneous breathing trial rest period data.

Digital health·2026
Same journal

Feasibility and behavioral impact of a wearable-supported digital health intervention on attitudes and behaviors toward sleep and physical activity: A proof-of-concept study in Kobe City.

Digital health·2026
Same journal

Summary of evidence on nutritional adherence in cardiac rehabilitation among patients with coronary heart disease after PCI based on mobile healthcare systems.

Digital health·2026
Same journal

Challenges of patient-facing generative artificial intelligence in hypertension care: A cross-platform evaluation of the quality, readability, and actionability of LLM-Generated patient education materials.

Digital health·2026
查看所有相关文章

相关实验视频

Updated: Jun 8, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.4K

在观察性研究中,ICD代码是否可靠? 评估数据质量的编码一致性

Stuart J Nelson1, Ying Yin1,2, Eduardo A Trujillo Rivera1,2

  • 1Biomedical Informatics Center, George Washington University, Washington, DC, USA.

Digital health
|November 4, 2024
PubMed
概括
此摘要是机器生成的。

在电子健康记录 (EHR) 中,国际疾病分类 (ICD) 代码分配在 ICD-9-CM 到 ICD-10-CM 过渡期间显示出显著的变化. 这种跨时间和地点的不一致性影响了患者队列和表型的可靠性.

关键词:
疾病的国际分类.临床编码 临床编码数据的准确性数据的准确性

更多相关视频

Isokinetic Robotic Device to Improve Test-Retest and Inter-Rater Reliability for Stretch Reflex Measurements in Stroke Patients with Spasticity
08:40

Isokinetic Robotic Device to Improve Test-Retest and Inter-Rater Reliability for Stretch Reflex Measurements in Stroke Patients with Spasticity

Published on: June 12, 2019

7.4K
Observational Study Protocol for Repeated Clinical Examination and Critical Care Ultrasonography Within the Simple Intensive Care Studies
10:38

Observational Study Protocol for Repeated Clinical Examination and Critical Care Ultrasonography Within the Simple Intensive Care Studies

Published on: January 16, 2019

20.1K

相关实验视频

Last Updated: Jun 8, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.4K
Isokinetic Robotic Device to Improve Test-Retest and Inter-Rater Reliability for Stretch Reflex Measurements in Stroke Patients with Spasticity
08:40

Isokinetic Robotic Device to Improve Test-Retest and Inter-Rater Reliability for Stretch Reflex Measurements in Stroke Patients with Spasticity

Published on: June 12, 2019

7.4K
Observational Study Protocol for Repeated Clinical Examination and Critical Care Ultrasonography Within the Simple Intensive Care Studies
10:38

Observational Study Protocol for Repeated Clinical Examination and Critical Care Ultrasonography Within the Simple Intensive Care Studies

Published on: January 16, 2019

20.1K

科学领域:

  • 医疗信息学 医疗信息学
  • 医疗编码系统 医学编码系统
  • 电子健康记录 (EHR) 是一种电子健康记录.

背景情况:

  • 国际疾病分类 (ICD) 代码对于EHR中的患者队列创建和表型定义至关重要.
  • 不一致的ICD代码分配可能会损害来自EHR数据的研究结果的有用性和可靠性.

研究的目的:

  • 在从ICD-9-CM过渡到ICD-10-CM期间评估ICD代码分配的可靠性.
  • 调查美国卫生系统内ICD代码使用的时间和地理变化.

主要方法:

  • 使用通用等价映射 (GEM) 表来对等的ICD代码进行集群.
  • 采用深度学习和统计模型来分析来自美国退伍军人管理局中央数据仓库的EHR数据.
  • 在过渡期和在个别VA设施中检查了ICD代码分配的变化.

主要成果:

  • 许多经常使用的ICD代码集群在ICD-9-CM和ICD-10-CM分配之间存在很大的偏差.
  • 手动审查显示66%的样本代码集群存在问题变化,其中37%缺乏明确的解释.
  • 观察到的编码模式在不同的护理地点有很大差异.

结论:

  • 跨时间和位置的ICD代码分配的变化引发了人们对基于EHR的队列和表型的语义可靠性的担忧.
  • 由于观察到的编码不一致性,研究人员必须仔细考虑和定义队列选择和表型定义.
  • 过渡到ICD-10-CM突出了在保持在EHR中保持一致的ICD代码使用方面的潜在挑战.