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Related Concept Videos

Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

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 assessment...
Data Validation01:03

Data Validation

Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
Type II Diabetes Mellitus III: Clinical Manifestations and Diagnosis01:25

Type II Diabetes Mellitus III: Clinical Manifestations and Diagnosis

Type 2 diabetes mellitus develops gradually and is often asymptomatic in early stages.Clinical ManifestationsWhen symptoms appear, they include fatigue, blurred vision, pruritus, delayed wound healing, and recurrent infections, particularly candidal infections. Peripheral neuropathy may present as numbness or tingling in the extremities. Classic hyperglycemia symptoms—polyuria, polydipsia, and polyphagia—are less common. Most patients are overweight and frequently have associated hypertension...
Formulating and Validating Nursing Diagnosis II01:25

Formulating and Validating Nursing Diagnosis II

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...
Formulating and Validating Nursing Diagnosis I01:26

Formulating and Validating Nursing Diagnosis I

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 for...
Diabetes Mellitus: Type 2 and Gestational01:22

Diabetes Mellitus: Type 2 and Gestational

Type 2 diabetes, characterized by insulin resistance, arises when the insulin receptors on cells lose responsiveness to insulin, diminishing the cell's capacity to take up glucose, resulting in elevated blood glucose levels. To receive a diagnosis of Type 2 diabetes, a series of blood glucose tests are necessary to assess whether the blood glucose falls within normal parameters. If the result is out of the normal range, a patient may be diagnosed as prediabetic or diabetic, depending on the...

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Updated: Jun 14, 2026

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

Validating ICD coding algorithms for diabetes mellitus from administrative data.

Guanmin Chen1, Nadia Khan, Robin Walker

  • 1Department of Community Health Sciences, University of Calgary, Canada. guchen@ucalgary.ca

Diabetes Research and Clinical Practice
|April 6, 2010
PubMed
Summary
This summary is machine-generated.

Accurate identification of diabetes in administrative data is possible using a specific case definition. This method, involving physician claims or hospital records with relevant International Classification of Disease (ICD) codes, proved highly valid.

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Improving IV Insulin Administration in a Community Hospital
12:08

Improving IV Insulin Administration in a Community Hospital

Published on: June 11, 2012

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Last Updated: Jun 14, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

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Published on: January 8, 2020

Improving IV Insulin Administration in a Community Hospital
12:08

Improving IV Insulin Administration in a Community Hospital

Published on: June 11, 2012

Area of Science:

  • Health Informatics
  • Epidemiology
  • Public Health

Background:

  • Administrative data are crucial for health research and surveillance.
  • Accurate identification of disease cohorts from administrative data is essential for reliable epidemiological studies.
  • Diabetes mellitus is a significant public health concern requiring robust data for monitoring.

Purpose of the Study:

  • To validate International Classification of Disease (ICD) 9 and 10 coding algorithms for diabetes detection in administrative databases.
  • To compare the accuracy of various algorithms using physician charts as the gold standard.
  • To assess algorithm validity across different time periods and geographic regions.

Main Methods:

  • A validation study comparing administrative data with physician chart reviews.
  • Inclusion of 50 patient charts per clinic from urban and rural general practitioners in Canada (2001, 2004).
  • Utilized ICD-9 code 250.xx and ICD-10 codes E10.x-E14.x for diabetes identification in administrative data.

Main Results:

  • Diabetes prevalence was 8.1% in clinic charts.
  • The algorithm '2 physician claims within 2 years or 1 hospitalization with relevant ICD codes' demonstrated high validity (sensitivity 92.3%, specificity 96.9%).
  • Algorithm performance remained consistent across time periods and regions after adjusting for demographic and comorbidity factors.

Conclusions:

  • A reliable case definition for identifying diabetes in administrative data was established.
  • The validated algorithm, "2 physician claims within 2 years or 1 hospital discharge abstract record with diagnosis codes 250.xx or E10.x-E14.x", enables accurate cohort identification.
  • This method supports the use of administrative data for diabetes research and surveillance.