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

Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

1.3K
The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters...
1.3K
Formulating and Validating Nursing Diagnosis I01:26

Formulating and Validating Nursing Diagnosis I

2.7K
A nursing diagnosis is written when the nurse recognizes a cluster of essential patient data indicating health problems treated with independent nursing interventions. The standardized terminologies of a nursing diagnosis help nurses identify and treat patients' problems. Every electronic health record that uses nursing diagnosis must employ standard diagnostic terminology. Developing an efficient, individualized care plan begins with accurate nursing diagnoses.
There are thirteen domains...
2.7K
Formulating and Validating Nursing Diagnosis II01:25

Formulating and Validating Nursing Diagnosis II

2.8K
Nursing diagnoses represent a problem validated by major defining characteristics. There are four categories of nursing diagnoses: problem-focused, risk, health promotion or wellness, and syndrome. The anatomy of a nursing diagnosis includes three components: problem statement or diagnostic label, defining characteristics, and related factors.
Risk nursing diagnoses represent clinical judgments of an individual, family, or community more vulnerable to developing the health problem than others...
2.8K
Prevalence and Incidence01:08

Prevalence and Incidence

558
In statistical epidemiology and health sciences, two essential metrics—prevalence and incidence—are fundamental for understanding disease dynamics within a population. These measures enable public health officials, epidemiologists, and researchers to assess the burden of diseases, allocate resources effectively, and design impactful public health policies and interventions.
Prevalence indicates the proportion of individuals in a population who have a specific disease or health...
558
Nursing Diagnosis01:22

Nursing Diagnosis

2.7K
Following assessment, a nursing diagnosis is the next step in the nursing process. It begins after the nurse has collected and recorded the patient data. The purpose of diagnosing is to identify how the client responds to actual or potential health processes, identify factors that bestow or that cause health problems, the etiologies, and identify resources or strengths the individual, group, or community can draw on to prevent or resolve problems.
The nursing diagnosis focuses on evidence-based...
2.7K
Skin Cancer01:30

Skin Cancer

4.2K
Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...
4.2K

You might also read

Related Articles

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

Sort by
Same author

Clinical, in vitro, and in vivo evidence of WAPL as a cohesinopathy-associated gene and phenotypic driver of 10q22.3q23.2 genomic disorder.

American journal of human genetics·2026
Same author

On the Path to Pediatric Genetics: Reflections From Future Clinicians.

American journal of medical genetics. Part A·2026
Same author

Fertility treatment and risk of cerebral palsy: has the association changed in Australia?

Human reproduction (Oxford, England)·2026
Same author

TrialR: critical enablers and the need for reusable Rare Disease Clinical Trial infrastructure in Western Australia.

Orphanet journal of rare diseases·2026
Same author

Ophthalmic manifestations of mitochondrial disorders.

Progress in retinal and eye research·2026
Same author

Translating multi-omics into healthcare: requisites for scalable and equitable implementation.

Human genomics·2026

Related Experiment Video

Updated: Jul 9, 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

14.5K

Surfacing undiagnosed disease: consideration, counting and coding.

Megan F Baxter1,2, Michele Hansen3,4, Dylan Gration4

  • 1Emergency Department, Perth Children's Hospital, Perth, WA, Australia.

Frontiers in Pediatrics
|November 29, 2023
PubMed
Summary

The diagnostic odyssey for people living with rare diseases (PLWRD) is often prolonged, delaying treatment and impacting well-being. This article proposes a coding framework to improve identification and tracking of undiagnosed rare diseases throughout the patient journey.

Keywords:
ICD-11diagnostic codingdiagnostic odysseykey timepointsrare diseasered flags

More Related Videos

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

33.8K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K

Related Experiment Videos

Last Updated: Jul 9, 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

14.5K
Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

33.8K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K

Area of Science:

  • Medical Diagnostics
  • Rare Diseases
  • Health Informatics

Background:

  • The diagnostic odyssey for people living with rare diseases (PLWRD) is frequently extended.
  • Reasons include delayed consideration of rare diseases and difficulties in systematically tracking undiagnosed conditions.
  • This delay leads to isolation, uncertainty, treatment delays, and increased complication risks, impacting patient and family well-being.

Purpose of the Study:

  • To identify critical time points for considering a rare disease diagnosis.
  • To outline elements for an operational classification of undiagnosed rare diseases during the diagnostic odyssey.
  • To advocate for a comprehensive coding framework to support PLWRD.

Main Methods:

  • Literature review and conceptual analysis of the diagnostic journey for rare diseases.
  • Discussion of current challenges in rare disease diagnosis and tracking.
  • Proposal for a novel coding framework to address identified gaps.

Main Results:

  • Key diagnostic milestones and decision points within the odyssey were highlighted.
  • Essential components for classifying undiagnosed rare diseases were identified.
  • The potential benefits of a unified coding framework for PLWRD and the healthcare community were discussed.

Conclusions:

  • A systematic approach and a robust coding framework are crucial for improving the diagnostic odyssey for PLWRD.
  • Implementing such a framework can enhance early diagnosis, facilitate targeted treatments, and improve patient outcomes.
  • This initiative aims to reduce diagnostic delays and improve the overall care for individuals with rare diseases.