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Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

1.8K
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...
1.8K
Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

966
The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
For example, a patient with a chronic...
966
Formulating and Validating Nursing Diagnosis I01:26

Formulating and Validating Nursing Diagnosis I

4.1K
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...
4.1K
Nursing Diagnosis01:22

Nursing Diagnosis

4.4K
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...
4.4K
Cancer Survival Analysis01:21

Cancer Survival Analysis

787
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
787
Formulating and Validating Nursing Diagnosis II01:25

Formulating and Validating Nursing Diagnosis II

4.1K
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...
4.1K

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Related Experiment Video

Updated: Feb 18, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Network-based analysis of diagnosis progression patterns using claims data.

Eugene Jeong1, Kyungmin Ko2, Seungbin Oh1

  • 1Ajou University School of Medicine, Department of Biomedical Informatics, Suwon, 16499, Republic of Korea.

Scientific Reports
|November 16, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel disease progression network, incorporating age and gender, to reveal complex human disease relationships and transitions. The network identifies key risk factors, advancing precision medicine.

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Area of Science:

  • Medical Informatics
  • Network Science
  • Computational Biology

Background:

  • Existing disease network models often overlook critical risk factors like age and gender.
  • Understanding disease relationships is crucial for medical research and patient care.

Purpose of the Study:

  • To construct and analyze a diagnosis progression network incorporating demographic and clinical data.
  • To identify disease associations and progression patterns influenced by age, gender, and disease class.

Main Methods:

  • Utilized large-scale healthcare claims data to build a scale-free diagnosis progression network.
  • Analyzed network structure to identify key diagnostic links and influential risk factors.

Main Results:

  • The constructed network exhibits scale-free properties, with a few diagnoses linking to many others.
  • Demonstrated that gender, age, and disease class significantly shape the disease network's structure.
  • The network allows for estimating disease transition directionality, strength, and timing.

Conclusions:

  • The diagnosis progression network offers a novel methodology for uncovering disease relationships beyond genomic data.
  • This approach facilitates the identification of new disease connectivities and progression pathways.
  • The findings contribute to advancing precision medicine by considering individual patient characteristics.