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

Methods of Documentation VII: EMR01:30

Methods of Documentation VII: EMR

821
Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare...
821
Pulse rhythm01:30

Pulse rhythm

749
Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
749
Methods of Documentation V: CBE01:23

Methods of Documentation V: CBE

865
Charting by Exception, or CBE, is a method of documentation used in healthcare, particularly in nursing, that focuses on documenting only significant or abnormal findings rather than recording every detail. This approach aims to streamline the documentation process, improve efficiency, and ensure that healthcare providers can quickly identify deviations from normalcy in patient assessments.
In CBE, healthcare professionals establish predefined standards of practice that define what constitutes...
865
Documentation in Long-Term and Home Healthcare Setting01:29

Documentation in Long-Term and Home Healthcare Setting

866
Documentation in long-term care facilities and home healthcare settings is crucial for ensuring continuous, coordinated, and comprehensive care for patients. Each setting has its specific documentation processes and tools:
Long-Term Care Facilities
866
Methods of Documentation I: Source-Oriented Records01:18

Methods of Documentation I: Source-Oriented Records

1.1K
Source-oriented records, or SOR, are medical record-keeping organized by the data source. The SOR system was first developed in the mid-1900s to organize the growing patient data in hospitals and other healthcare facilities.
In an SOR, each discipline involved in patient care maintains a separate medical record section. This record-keeping method enables easy tracking of patient progress and ensures healthcare staff have access to up-to-date information.
Key Attributes include the following:
1.1K
Flow Sheet01:17

Flow Sheet

1.4K
Flowsheets are valuable tools in nursing documentation. They enable healthcare professionals to efficiently record and monitor various patient assessments and measurements in a consolidated format.
Here's a closer look at the examples of flowsheets commonly used by nurses:
Graphic Sheet Documentation:
1.4K

You might also read

Related Articles

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

Sort by
Same author

Dementia diagnostic deserts: workforce and geographic inequities in the diagnostic pathway for cognitive impairment.

Health affairs scholar·2026
Same author

Diabetes educational interventions in care homes: a scoping review.

BMC medical education·2026
Same author

Research Code Sharing in Support of Gold Standard Science.

Journal of diabetes science and technology·2026
Same author

Natural Language Processing for Automated Extraction of Continuous Glucose Monitoring Data.

Diabetes care·2025
Same author

Experience of Using Wearable Devices for Dietary Management for Chinese Americans With Type 2 Diabetes: One-Group Prospective Cohort Study.

JMIR diabetes·2025
Same author

Co-Design and Mixed-Methods Evaluation of a Digital Diabetes Education Intervention for Nursing Homes: Study Protocol.

Nursing reports (Pavia, Italy)·2025
Same journal

A Pilot Study on Disposal Practices and Environmental Awareness of Insulin-Related Devices Among People With Diabetes.

Journal of diabetes science and technology·2026
Same journal

Quality of In-Use Insulin Under Real-World Storage Conditions in Mwanza, Tanzania.

Journal of diabetes science and technology·2026
Same journal

Continuous Glucose Monitoring Metrics for Predicting Adverse Neonatal Outcomes in Individuals Undergoing Gestational Diabetes Screening.

Journal of diabetes science and technology·2026
Same journal

Diabetes Technologist: Optimal Use of Technology in Everyday Practice.

Journal of diabetes science and technology·2026
Same journal

AI-Driven Diabetes Care and Its Relevance in the Philippine Context: Opportunities and Persistent Digital Barriers.

Journal of diabetes science and technology·2026
Same journal

Ease of Use, Ease of Learning, and Convenience of the CagriSema Dual-Chamber Pen: Results From a Usability Study in Adults With Overweight, Obesity, or Type 2 Diabetes.

Journal of diabetes science and technology·2026
See all related articles

Related Experiment Video

Updated: May 22, 2025

Simple Continuous Glucose Monitoring in Freely Moving Mice
03:25

Simple Continuous Glucose Monitoring in Freely Moving Mice

Published on: February 24, 2023

5.1K

Classifying Continuous Glucose Monitoring Documents From Electronic Health Records.

Yaguang Zheng1, Eduardo Iturrate2, Lehan Li3

  • 1Rory Meyers College of Nursing, New York University, New York, NY, USA.

Journal of Diabetes Science and Technology
|March 12, 2025
PubMed
Summary
This summary is machine-generated.

Accurate classification of continuous glucose monitoring (CGM) Ambulatory Glucose Profile (AGP) reports in electronic health records (EHR) is crucial. An automated algorithm achieved high sensitivity (95.0%) and specificity (91.7%) in identifying AGP reports.

Keywords:
continuous glucose monitoringdiabetes mellituselectronic health recordnatural language processing

More Related Videos

Improving IV Insulin Administration in a Community Hospital
12:08

Improving IV Insulin Administration in a Community Hospital

Published on: June 11, 2012

18.5K
Glycemic Impact on Knee Osteoarthritis Symptoms on Physical, Radiographic, and Inflammatory Markers among Individuals Aged 50 and Over with Diabetes
07:22

Glycemic Impact on Knee Osteoarthritis Symptoms on Physical, Radiographic, and Inflammatory Markers among Individuals Aged 50 and Over with Diabetes

Published on: March 7, 2025

163

Related Experiment Videos

Last Updated: May 22, 2025

Simple Continuous Glucose Monitoring in Freely Moving Mice
03:25

Simple Continuous Glucose Monitoring in Freely Moving Mice

Published on: February 24, 2023

5.1K
Improving IV Insulin Administration in a Community Hospital
12:08

Improving IV Insulin Administration in a Community Hospital

Published on: June 11, 2012

18.5K
Glycemic Impact on Knee Osteoarthritis Symptoms on Physical, Radiographic, and Inflammatory Markers among Individuals Aged 50 and Over with Diabetes
07:22

Glycemic Impact on Knee Osteoarthritis Symptoms on Physical, Radiographic, and Inflammatory Markers among Individuals Aged 50 and Over with Diabetes

Published on: March 7, 2025

163

Area of Science:

  • Medical Informatics
  • Diabetes Technology

Background:

  • Increasing clinical use of continuous glucose monitoring (CGM) leads to more CGM documents in electronic health records (EHR).
  • Lack of standardization in storing CGM data within EHRs poses challenges for data retrieval and analysis.

Purpose of the Study:

  • To evaluate the sensitivity and specificity of classification criteria for CGM Ambulatory Glucose Profile (AGP) reports.
  • To assess the accuracy of an automated algorithm in distinguishing AGP reports from other CGM-related documents in EHRs.

Main Methods:

  • A document classification algorithm was developed, incorporating image processing, optical character recognition, and keyword analysis.
  • The algorithm processed 2244 documents, classifying them as CGM AGP reports, non-CGM reports, or uncertain.
  • Manual review by two experts was performed on a subset of documents (62 reports) to validate the algorithm's performance, calculating sensitivity and specificity.

Main Results:

  • Out of 2244 documents, 46.5% were identified as CGM AGP reports, while 52.1% were other clinical documents.
  • The expert reviewers achieved 100% sensitivity and 98.4% specificity in document evaluation.
  • The automated classification algorithm demonstrated a sensitivity of 95.0% and a specificity of 91.7% when compared to manual review.

Conclusions:

  • Approximately half of CGM-related documents in EHRs are AGP reports valuable for clinical practice and research.
  • The other half consist of various non-AGP clinical documents, highlighting the heterogeneity of stored CGM data.
  • Standardization of CGM document storage in EHRs is necessary for improved data management and utilization.