Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

714
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
714
Classification of Leukocytes01:30

Classification of Leukocytes

1.6K
Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
1.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Charting the future of ACMI: a report from the 2025 ACMI symposium.

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

Assessing the Effectiveness and Scalability of Fast Healthcare Interoperability Resource-Based Granular Data Segmentation Technology.

Applied clinical informatics·2026
Same author

Advancing Health Equity Through Substance Use Medical Record Data Sharing: Insights from Healthcare Providers.

International journal of environmental research and public health·2025
Same author

Balancing Privacy, Trust, and Equity: Patient Perspectives on Substance Use Disorder Data Sharing.

International journal of environmental research and public health·2025
Same author

Ingredient-based method to create medication lists and support granular data segmentation.

Health informatics journal·2025
Same author

Empowering diversity: striving for inclusivity by leveraging the American Medical Informatics Association's "For Your Informatics" Podcast.

JAMIA open·2024
Same journal

Disparities in Activation and Use of Patient Portals Among Spanish-Speaking Patients.

Applied clinical informatics·2026
Same journal

Real-World Utilization of a Hospital-Integrated Internet Hospital in Henan Province, China: A 1-Year Observational Study.

Applied clinical informatics·2026
Same journal

From Pandemic Response to Kill the Clipboard: Patient-Controlled Sharing of Health Data Using International Patient Summary (IPS) and QR codes.

Applied clinical informatics·2026
Same journal

Usage of and Satisfaction with Artificial Intelligence-Generated Draft Replies to Patient Portal Messages.

Applied clinical informatics·2026
Same journal

Automating Ambulatory Central Line Data Capture and Calculations.

Applied clinical informatics·2026
Same journal

Effectiveness of Interruptive Clinical Decision Support Alerts on Intravenous vs. Oral Acetaminophen Prescribing.

Applied clinical informatics·2026
See all related articles

Related Experiment Video

Updated: May 27, 2025

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

24.5K

FHIR Granular Sensitive Data Segmentation.

Preston Lee1,2, Daniel Mendoza1, Martha Kaiser1

  • 1Arizona State University, College of Health Solutions, Phoenix, Arizona, United States.

Applied Clinical Informatics
|February 19, 2025
PubMed
Summary
This summary is machine-generated.

Patients gain control over sensitive health data sharing with new consent-respecting technology. This system uses advanced Health Level 7 (HL7) standards for granular data segmentation, improving privacy and physician alignment.

More Related Videos

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

8.8K
Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

12.1K

Related Experiment Videos

Last Updated: May 27, 2025

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

24.5K
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

8.8K
Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

12.1K

Area of Science:

  • Health Informatics
  • Medical Record Management
  • Health Data Privacy

Background:

  • Patients desire enhanced control over sensitive medical record sharing due to stigma.
  • Current technologies require updates to newer standards for better alignment with physician categorization of sensitive data.

Purpose of the Study:

  • To deploy and pilot test open-source Fast Healthcare Interoperability Resources (FHIR)-based data segmentation technologies.
  • To involve physicians in designing a decision engine supporting various confidence levels for data sharing.

Main Methods:

  • Developed a web-based Patient Portal and Clinical Decision Support (CDS) granular data segmentation Engine.
  • Utilized FHIR R5, Consent resource type, and CDS Hooks for data sensitivity labeling and redaction.
  • Implemented configurable confidence threshold cutoffs for nuanced data categorization.

Main Results:

  • Deployed a system enabling patients to make consent-based granular data choices for sensitive information, such as substance use records.
  • The engine supports advanced HL7 standards, moving beyond binary categorizations to reflect physician confidence levels.
  • Engineering choices prioritized adaptability, reusability, and scalability for the technology.

Conclusions:

  • The developed data segmentation technologies update existing software with the latest HL7 standards.
  • The system better reflects how physicians categorize sensitive medical information using varying confidence levels.
  • Open-source code was shared via the HL7 FHIR Foundry to promote reusability.