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

Purpose of Health Records II01:19

Purpose of Health Records II

1.0K
Health records serve various essential purposes in the healthcare system. Here are some key purposes:
1.0K
Legal Guidelines for Documentation01:06

Legal Guidelines for Documentation

1.4K
The legal guidelines for nursing documentation are essential for ensuring accurate, professional, and ethical recording of patient care. The guidelines are discussed here:
1.4K
Purpose of Health Records I01:11

Purpose of Health Records I

1.3K
The vital purpose of health records is to provide a complete and accurate account of a patient's medical history, including communication, diagnostic and therapeutic orders, care planning, research, and quality review.
Here's a breakdown of how health records serve these purposes:
1.3K
Health Information Technology and Healthcare Information System01:30

Health Information Technology and Healthcare Information System

915
Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
915
Ethical Standards II01:23

Ethical Standards II

795
Ethical standards are the backbone of nursing practice, guiding nurses as they interact with patients, families, and colleagues. These standards are crucial for providing safe, empathetic care centered on the patient's needs.
Nurses are entrusted with upholding various ethical principles and standards. Nurses forge solid therapeutic relationships using trust, empathy, autonomy, confidentiality, and professional competence.
Confidentiality is crucial, embodying respect for individual privacy...
795
Guidelines and Strategies for Safe Computer Charting01:18

Guidelines and Strategies for Safe Computer Charting

874
The guidelines and strategies provided by the American Nurses Association (ANA) and the Canadian Nurses Association (CNA) offer essential principles for ensuring safe and secure computer charting systems in healthcare settings. Let's break down each recommendation:
Maintain Confidentiality and Security:
874

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

Rural-Urban Differences in Pediatric Asthma Presentation and Outcomes, 2016-2021.

Hospital pediatrics·2026
Same author

Rural-Urban Differences in Hypertension Prevalence and Control in a Large Regional Health System Cohort.

Journal of clinical medicine·2026
Same author

Long COVID Persistence and Surveillance Gaps Across 58 US Hospitals.

JAMA network open·2026
Same author

Decision support systems (DSS) for predicting hypertensive events using real-world telemonitoring data.

International journal of medical informatics·2026
Same author

Opportunities for informatics to improve patient experiences: observations and reflections of ACMI fellows.

Journal of the American Medical Informatics Association : JAMIA·2026
Same author

A conceptual model for provider scheduling: insights from an EHR implementation.

Journal of the American Medical Informatics Association : JAMIA·2026

相关实验视频

Updated: Sep 10, 2025

The Participant-Reported Implementation Update and Score PRIUS: A Novel Method for Capturing Implementation-Related Data Over Time
06:05

The Participant-Reported Implementation Update and Score PRIUS: A Novel Method for Capturing Implementation-Related Data Over Time

Published on: February 19, 2021

1.4K

设计中的隐私:使用混合人机系统进行交互记录链接的案例研究

Hye-Chung Kum1, Eric Ragan2, Mahin Ramezani1

  • 1Population Informatics Lab, Department of Health Policy and Management, School of Public Health, Texas A&M University, College Station, TX, USA; Department of Computer Science and Engineering; Texas A&M University, College Station, TX, USA.

International journal of medical informatics
|August 22, 2025
PubMed
概括

像MiNDFIRL这样的交互记录链接 (RL) 系统可以提高患者匹配的准确性. 这种混合人机方法尽量减少数据披露,同时减少现实世界健康数据链接中的错误.

关键词:
数据细分互动记录链接患者匹配设计中的隐私现实世界数据记录链接

更多相关视频

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios
06:02

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

Published on: October 6, 2020

2.4K
RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

3.1K

相关实验视频

Last Updated: Sep 10, 2025

The Participant-Reported Implementation Update and Score PRIUS: A Novel Method for Capturing Implementation-Related Data Over Time
06:05

The Participant-Reported Implementation Update and Score PRIUS: A Novel Method for Capturing Implementation-Related Data Over Time

Published on: February 19, 2021

1.4K
Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios
06:02

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

Published on: October 6, 2020

2.4K
RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

3.1K

科学领域:

  • 卫生信息学
  • 数据链接
  • 保护隐私的技术

背景情况:

  • 如果没有一个共同的标识符, 准确匹配不同数据源的患者是具有挑战性的.
  • 使用混合人机系统的交互记录链接 (RL) 对于高质量的患者匹配至关重要.
  • 在RL期间尽量减少信息披露对于患者的隐私至关重要.

研究的目的:

  • 介绍和评估混合原型软件系统MiNDFIRL (互动记录链接的最低必要披露).
  • 证明MiNDFIRL能够最大限度地提高连接准确度,同时尽量减少信息披露.
  • 在实例研究中评估MiNDFIRL的有效性.

主要方法:

  • 进行了两项用户研究,涉及10,000对电子病历数据和18,240个患者生成的数据.
  • 使用自动化RL,然后由12名审查员使用MiNDFIRL进行人工审查.
  • 员工达成共识以解决审查员的分歧,并进行半结构面试以获得系统反.

主要成果:

  • 随机森林算法发现了388个 (EHR) 和539个 (患者生成的) 匹配,另外还有303个和187个对需要手动审查.
  • 手动审查证实了232个 (EHR) 和84个 (患者生成的) 不确定对的额外真实联系.
  • MiNDFIRL仅使用30%的可用识别信息来正确分类77% (EHR) 和45% (患者生成) 的不确定的联系,其中姓名和电子邮件最常见.

结论:

  • 具有按需访问和掩盖的混合人机系统减少了RL中的披露风险.
  • 在真实世界患者数据链接中,MiNDFIRL有效地减少了假阳性和假阴性.
  • 风险量化和互动审查是记录链接中准确性和隐私平衡的关键.