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Related Concept Videos

Purpose of Health Records II01:19

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Health records serve various essential purposes in the healthcare system. Here are some key purposes:
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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...
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People can go to great lengths to protect their self-image and present themselves in ways that they want others to see them. Sociologist Erving Goffman presented the idea that a person is like an actor on a stage. Calling his theory dramaturgy, Goffman believed that we use “impression management” to present ourselves to others as we hope to be perceived. Each situation is a new scene, and individuals perform different roles depending on who is present (Goffman, 1959). Think about...
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Data Collection III01:05

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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.
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Related Experiment Video

Updated: Mar 13, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
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Person-generated Data in Self-quantification. A Health Informatics Research Program.

Kathleen Gray1, Fernando J Martin-Sanchez, Guillermo H Lopez-Campos

  • 1Kathleen Gray, The University of Melbourne, Health and Biomedical Informatics Centre, 202 Berkeley Street, Melbourne VIC 3010, Australia,

Methods of Information in Medicine
|October 27, 2016
PubMed
Summary
This summary is machine-generated.

This study outlines a health informatics research program to generate evidence on self-quantification technologies. It provides lessons for optimizing digital health data and outcomes.

Keywords:
Autoexperimentationconsumer participationparticipatory healthresearch designself-quantificationtelemedicine

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Area of Science:

  • Health Informatics
  • Digital Health
  • Biomedical Informatics

Background:

  • Consumer technologies (mobile, wearable, ambient) and e-services in healthcare lack sufficient evidence of efficacy.
  • Self-quantification and digital health phenomena are rapidly evolving beyond traditional healthcare informatics.
  • There is a need to generate robust evidence on health data processing in self-quantification.

Purpose of the Study:

  • To describe an approach for building a health informatics research program.
  • To generate evidence on health data and information processing in self-quantification.
  • To investigate associated healthcare and health outcomes.

Main Methods:

  • Summarized relevant health informatics research approaches.
  • Presented an example program from the Health and Biomedical Informatics Centre (HaBIC).
  • Described the program's infrastructure, models, design, reporting, and knowledge sharing.

Main Results:

  • Identified key outcomes from integrative approaches to managing data from self-monitoring devices.
  • Highlighted lessons for consumers, developers, clinicians, and researchers.
  • Demonstrated the value of multi-angle investigations in digital health data management.

Conclusions:

  • Health informatics must adapt to emerging self-quantification and digital health trends.
  • Further large-scale, multi-center collaborations are needed to optimize self-quantification.
  • Continued research is essential for advancing digital health and informatics practices.