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Related Concept Videos

Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

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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.
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Diagnostic and Statistical Manual of Mental Disorders (DSM)01:27

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The Diagnostic and Statistical Manual of Mental Disorders (DSM) serves as the primary classification system for mental health disorders, providing standardized diagnostic criteria for clinicians and researchers. First published by the American Psychiatric Association (APA) in 1952, the DSM has undergone several revisions to reflect evolving psychiatric understanding. The fifth edition, DSM-5, released in 2013, introduced key updates that expanded diagnostic categories and modified diagnostic...
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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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Data Reporting and Recording01:24

Data Reporting and Recording

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Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
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Confounding in Epidemiological Studies01:27

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Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
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Formulating and Validating Nursing Diagnosis I01:26

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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...
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Coding Fairness: Detecting Demographic-Related Coding Discrepancies in ICD Code Assignments.

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Summary
This summary is machine-generated.

Electronic medical record coding errors can introduce bias. This study found significant coding discrepancies across demographic groups, highlighting the need for fairness in clinical data.

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Area of Science:

  • Biomedical Informatics
  • Artificial Intelligence in Healthcare
  • Health Equity Research

Background:

  • Coded clinical data, particularly International Classification of Diseases (ICD) codes, are vital for biomedical research, cohort assembly, and AI modeling.
  • Existing research acknowledges coding errors in electronic medical records, but the impact of potential biases on fairness, especially in AI applications, is under-explored.
  • Ensuring fairness in AI research is increasingly important, necessitating an examination of biases within the coded clinical data used for model development.

Purpose of the Study:

  • To assess coding fairness across demographic subgroups within the Veterans Health Administration Clinical Data Warehouse.
  • To evaluate potential biases in International Classification of Diseases (ICD) codes by comparing AI-generated phenotypes with ICD-based phenotypes.
  • To identify demographic-related discrepancies in clinical data coding.

Main Methods:

  • Utilized a race- and sex-agnostic artificial intelligence (AI) phenotyping model.
  • Analyzed coding fairness across 203 ICD code blocks within the Veterans Health Administration Clinical Data Warehouse.
  • Compared AI-generated phenotypes against ICD-based phenotypes to identify discrepancies.

Main Results:

  • Variability in coding consistency was observed across demographic subgroups, including sex, race, and ethnicity.
  • Over 50% of the analyzed ICD code blocks showed statistically significant differences in discrepancies between AI-generated and ICD-based phenotypes across demographic groups.
  • These findings indicate a notable presence of demographic-related coding disparities.

Conclusions:

  • Demographic-related coding discrepancies exist within large-scale clinical data.
  • The study underscores the critical need to address these biases to ensure fairness in AI research and clinical informatics.
  • Recognizing and mitigating coding disparities is essential for equitable healthcare data utilization.