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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...
Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

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 illness...
Methods of Documentation II: POMR01:26

Methods of Documentation II: POMR

The Problem-Oriented Medical Record (POMR) revolutionized medical record-keeping by introducing a systematic approach focusing on the patient's problems rather than merely listing symptoms. Dr. Lawrence Weed's introduction of this method in the 1960s marked a significant advancement in medical documentation. The POMR framework consists of four key components: the database, problem list, plan of care, and progress notes.
Health Information Technology and Healthcare Information System01:30

Health Information Technology and Healthcare Information System

Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...

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Related Experiment Video

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

Issues in multiple imputation of missing data for large general practice clinical databases.

Louise Marston1, James R Carpenter, Kate R Walters

  • 1Department of Primary Care and Population Health, University College London, Rowland Hill Street, London NW32PF, UK. l.marston@ucl.ac.uk

Pharmacoepidemiology and Drug Safety
|March 23, 2010
PubMed
Summary
This summary is machine-generated.

Missing data in primary care databases are common. Multiple imputation (MI) suggests smoking and alcohol data are missing not at random, unlike height, weight, and blood pressure.

Related Experiment Videos

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

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

Published on: January 8, 2020

Area of Science:

  • Clinical Informatics
  • Biostatistics
  • Public Health Research

Background:

  • Missing data present a significant challenge in clinical databases, potentially biasing research findings.
  • Understanding missing data patterns is crucial for accurate analysis of electronic health records.
  • Primary care databases are valuable resources, but data completeness requires careful evaluation.

Purpose of the Study:

  • To investigate missing data patterns in a UK primary care database (THIN).
  • To compare these patterns with nationally representative health surveys (HSE, BRHS).
  • To explore the utility of multiple imputation (MI) for addressing missing data in this context.

Main Methods:

  • Quantified missing health indicators in 488,384 patients from the THIN database (2004-2006).
  • Compared data distributions with 14,142 participants from the Health Survey for England (HSE) and 4,252 men from the British Regional Heart Study (BRHS).
  • Developed and applied multiple imputation (MI) models to estimate missing values.

Main Results:

  • Missing data ranged from 22% (smoking) to 38% (height) among newly registered patients.
  • Distributions of height, weight, and blood pressure were comparable across datasets.
  • Post-MI, THIN showed higher percentages of smokers and non-drinkers, and lower percentages of ex-smokers and heavy drinkers compared to national datasets.

Conclusions:

  • Findings suggest that missing smoking and alcohol data are likely 'missing not at random' (MNAR).
  • Height, weight, and blood pressure data appear to be 'missing at random' (MAR).
  • Further research is needed to identify appropriate imputation methods for MNAR variables like smoking and alcohol in primary care databases.