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Related Concept Videos

How Data are Classified: Categorical Data01:11

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
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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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Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
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Data Validation01:03

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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
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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...
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Veracity in big data: How good is good enough.

Andrew P Reimer1, Elizabeth A Madigan2

  • 1Case Western Reserve University, USA; Cleveland Clinic, USA.

Health Informatics Journal
|February 2, 2018
PubMed
Summary
This summary is machine-generated.

Data veracity in research using electronic medical records and administrative data is crucial. Establishing trust requires attention to data provenance, cross-validation, and context for reliable big data insights.

Keywords:
clinical decision-makingdata qualitydatabases and data miningdecision support systemselectronic health records

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Area of Science:

  • Health Informatics
  • Big Data Research
  • Data Science

Background:

  • Veracity, a key component of big data, is critical for research utilizing electronic medical record (EMR) data.
  • The increasing use of EMR and administrative data in research necessitates a thorough understanding of data quality and trustworthiness.
  • Previous research has highlighted the 'five V's' of big data, with veracity gaining prominence in health research contexts.

Purpose of the Study:

  • To explore the concept of data veracity in the context of EMR and administrative data for research.
  • To discuss the implications of data quality for the reliability of research findings derived from these data sources.
  • To propose key considerations for establishing data veracity in health research.

Main Methods:

  • This perspective article reviews existing literature and concepts related to big data veracity.
  • It analyzes the applicability of the 'good enough' principle for EMR data in research settings.
  • The article proposes a framework for assessing data veracity.

Main Results:

  • EMR data, deemed 'good enough' for clinical practice, can be considered 'good enough' for specific research applications.
  • Establishing data veracity requires a multi-faceted approach beyond simple data collection.
  • Three primary issues are identified as critical for ensuring data veracity: data provenance, cross-validation, and context.

Conclusions:

  • Data veracity is a fundamental consideration for the responsible use of EMR and administrative data in research.
  • Implementing robust methods for assessing data provenance, cross-validation, and context is essential.
  • Ensuring data veracity enhances the reliability and validity of research outcomes derived from big data in healthcare.