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Related Concept Videos

Data Collection I01:30

Data Collection I

Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of data...
Data Collection II01:29

Data Collection II

The nursing history captures and records the patient's health status, so that a care plan evolves to meet the patient's individual needs. The nursing health history is a part of the initial assessment. A comprehensive history covers all health dimensions and plays a significant role in the assessment process. A comprehensive history includes the patient's biographical information, reasons for seeking health care, expectations, present and past health history, medications, and family,...
Data Collection III01:05

Data Collection III

The physical assessment examines the patient for objective data that defines the patient's condition, and aids in formulating the nursing care plan. The purpose of physical assessment is a health status appraisal, which includes identifying health problems, and establishing a database for nursing intervention.
The principles to begin the physical assessment include conducting a comprehensive or problem-related history in a quiet, well-lit room, emphasizing privacy and comfort for the patient.
Methods of Documentation IV: Focus Charting01:26

Methods of Documentation IV: Focus Charting

Focus Charting, also known as the focus charting system or "focus documentation," is a systematic documentation approach used in healthcare to organize patient information in medical records.
It typically involves three columns for recording information:
Formats for Nursing Documentation01:28

Formats for Nursing Documentation

Nursing documentation encompasses various formats designed to capture precise patient data, facilitate communication among healthcare team members, and ensure comprehensive and accurate patient records. Let's explore each of these formats in detail:
Nursing Assessment Form:
• A nursing assessment form is a foundational document that captures detailed patient data from physical assessments and nursing histories.
• It includes patient demographics, medical history, current medications, vital...
Data Collection by Observations01:08

Data Collection by Observations

Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...

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Related Experiment Video

Updated: Jun 4, 2026

Bringing the Clinic Home: An At-Home Multi-Modal Data Collection Ecosystem to Support Adaptive Deep Brain Stimulation
06:32

Bringing the Clinic Home: An At-Home Multi-Modal Data Collection Ecosystem to Support Adaptive Deep Brain Stimulation

Published on: July 14, 2023

Distributed cognition artifacts on clinical research data collection forms.

Meredith Nahm1, Vickie D Nguyen, Elie Razzouk

  • 1University of Texas, School of Health Information Sciences, Houston TX.

Summit on Translational Bioinformatics
|February 25, 2011
PubMed
Summary
This summary is machine-generated.

Medical record abstraction errors may stem from high cognitive load. This study reveals many data elements require significant working memory, potentially explaining abstraction inaccuracies.

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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

Related Experiment Videos

Last Updated: Jun 4, 2026

Bringing the Clinic Home: An At-Home Multi-Modal Data Collection Ecosystem to Support Adaptive Deep Brain Stimulation
06:32

Bringing the Clinic Home: An At-Home Multi-Modal Data Collection Ecosystem to Support Adaptive Deep Brain Stimulation

Published on: July 14, 2023

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

Area of Science:

  • Cognitive Science
  • Health Informatics
  • Medical Data Management

Background:

  • Medical record abstraction is crucial for secondary data use but suffers from high error rates.
  • Cognitive factors contributing to these errors remain understudied.
  • Existing research lacks systematic evaluation of cognitive demands in abstraction processes.

Purpose of the Study:

  • To systematically evaluate cognitive demands in medical record abstraction using representational analysis.
  • To assess the extent of external cognitive support provided by clinical research data collection forms.
  • To identify potential cognitive explanations for medical record abstraction errors.

Main Methods:

  • Employed the theory of distributed representation and representational analysis.
  • Evaluated cognitive demands for data elements in a sample of clinical research data collection forms.
  • Assessed the external cognitive support offered by these forms.

Main Results:

  • High cognitive load was identified in 61% of sampled data elements, with 9% being exceedingly high.
  • Data collection forms inadequately supported external cognition for complex data elements.
  • High working memory demands were linked to errors in elements requiring interpretation, comparison, mapping, or calculation.

Conclusions:

  • High cognitive load, particularly working memory demands, is a likely contributor to medical record abstraction errors.
  • The representational analysis method can identify data elements posing significant cognitive challenges.
  • Optimizing data collection forms to reduce cognitive load may improve abstraction accuracy.