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Related Concept Videos

Archival Research01:40

Archival Research

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Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships. For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and...
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Methods of Documentation I: Source-Oriented Records01:18

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Source-oriented records, or SOR, are medical record-keeping organized by the data source. The SOR system was first developed in the mid-1900s to organize the growing patient data in hospitals and other healthcare facilities.
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Purpose of Health Records I01:11

Purpose of Health Records I

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The vital purpose of health records is to provide a complete and accurate account of a patient's medical history, including communication, diagnostic and therapeutic orders, care planning, research, and quality review.
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Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
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Methods of Documentation VII: EMR01:30

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Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare...
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Classification is the process of organizing organisms into hierarchically inclusive groups based on their phenotypic similarities or evolutionary relationships. A species comprises one or more strains, and closely related species are grouped into genera. Genera are further classified into families, families into orders, orders into classes, and so forth, up to the domain level, which is the broadest taxonomic rank derived from a combination of phenotypic and genotypic data.The nomenclature of...
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Understanding the Nature of Metadata: Systematic Review.

Hannes Ulrich1,2, Ann-Kristin Kock-Schoppenhauer1, Noemi Deppenwiese3

  • 1IT Center for Clinical Research, University of Lübeck, Lübeck, Germany.

Journal of Medical Internet Research
|January 11, 2022
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Summary
This summary is machine-generated.

This study clarifies metadata definitions and challenges in data reuse. Harmonized definitions and a new schema improve metadata classification and generation for better data integration across research areas.

Keywords:
data classificationdata identificationdata integrationmetadatametadata definitionsystematic review

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Area of Science:

  • Data Science
  • Information Science
  • Research Methodology

Background:

  • Metadata is crucial for describing data but its definition and use are often ambiguous.
  • Lack of clear metadata definitions hinders effective data processing and management.
  • Ambiguity in metadata terminology complicates its application in diverse research fields.

Purpose of the Study:

  • To define metadata clearly and understand challenges associated with its reuse.
  • To address the ambiguity surrounding the term 'metadata' and its practical applications.
  • To explore the impact of inconsistent metadata definitions on data integration and research.

Main Methods:

  • A systematic literature search was conducted following PRISMA guidelines.
  • Five research questions guided the review of metadata characteristics, standards, use cases, and problems.
  • A harmonization process established clear definitions for metadata processing, focusing on data integration.

Main Results:

  • 81 peer-reviewed papers from the last decade were analyzed.
  • The review identified various metadata standards, use cases, and encountered problems.
  • Harmonized definitions facilitated answering research questions on metadata application in different research areas.

Conclusions:

  • Meaningful metadata creation is challenging but essential for data identification and processing.
  • The study uncovered numerous metadata standards, use cases, problems, and solutions.
  • Harmonized definitions and a new schema can enhance metadata classification and generation through shared understanding.