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Related Concept Videos

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.
Here's a breakdown of how health records serve these purposes:
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Data Validation01:03

Data Validation

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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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Data Collection II01:29

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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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Health Literacy01:21

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Health literacy is an individual's or a community's capacity to comprehend, receive, read, and use relevant healthcare information and services. The World Health Organization (WHO, 2018) defines health literacy as the cognitive and social skills that determine the ability of individuals to gain access to, understand, and use information in ways that promote and maintain good health. As a result, the WHO helps individuals manage long-term health concerns, participate in preventative...
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Data Collection I01:30

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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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Data Quality in Health Research: Integrative Literature Review.

Filipe Andrade Bernardi1, Domingos Alves1, Nathalia Crepaldi1

  • 1Ribeirão Preto School of Medicine, University of Sao Paulo, Ribeirão Preto, Brazil.

Journal of Medical Internet Research
|October 31, 2023
PubMed
Summary

Reliable health data is crucial for decision-making and improving healthcare services. This review identifies digital health technologies that enhance health research data quality, highlighting the need for standardized practices.

Keywords:
artificial intelligencedata qualitydatabasedecision-makingdigital governancedigital healthe-managementhealth datahealth serviceshealth stakeholdershealth systemreliabilityresearchresearch networkreview

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Area of Science:

  • Health Informatics
  • Data Science
  • Public Health Research

Background:

  • Reliable health data is essential for evidence-based decision-making and service delivery improvement.
  • Data quality management ensures the integrity and trustworthiness of collected health information.
  • Advancements in scientific research aim to improve data quality, reproducibility, and secure data sharing.

Approach:

  • An integrative literature review was conducted to identify and evaluate digital health technology interventions.
  • A systematic search of six major electronic databases was performed in January 2022.
  • The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were utilized.

Key Points:

  • 33 papers published between 2017-2021 from 22 countries were included.
  • Significant variability and lack of consensus exist in assessing health data quality domains and metrics.
  • Effective data quality strategies include business intelligence, statistical analysis, data mining, and qualitative methods.

Conclusions:

  • Barriers to health data quality are multifaceted, encompassing technical, economic, ethical, and organizational factors.
  • Data quality processes, from collection to analysis, are critical for research outcomes and healthcare decision-making.
  • Standardized practices and collaborative efforts are necessary to enhance health data quality.