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Related Concept Videos

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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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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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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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Effective communication is the foundation of a good organization. Communication is the lifeblood of an organization that connects the group with messages. In an organization, communication occurs in upward, downward, and horizontal lines. Downward communication travels from the administrative and senior levels to the staff through official channels such as manuals, rules and regulations, and organizational charts. Staff members initiate upward communication, which is addressed to executives and...
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How Data are Classified: Numerical Data00:59

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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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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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Big Data for Public Health Policy-Making: Policy Empowerment.

Laura Mählmann1,2, Matthias Reumann1,3, Nikolaos Evangelatos1,4

  • 1United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (UNU-MERIT), Maastricht University, Maastricht, the Netherlands.

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Summary
This summary is machine-generated.

Health data cooperatives (HDC) are crucial for leveraging big data in public health policy. Empowering citizens with owned health data through HDCs enables better policy monitoring and evaluation.

Keywords:
Big data analyticsCross-border healthcareData linkageE-healthEthical and regulatory frameworksHealth data cooperativesPreventionPublic health policiesSecondary use of data

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Area of Science:

  • Digital Health
  • Public Health Policy
  • Health Data Governance

Background:

  • Digitization offers vast opportunities for healthcare data collection and analysis.
  • Existing data landscapes are heterogeneous, posing computational, algorithmic, and technological challenges.
  • Regulatory, normative, governance, and policy constraints hinder data utilization.

Purpose of the Study:

  • To highlight the role of health data cooperatives (HDC) in public health policy-making.
  • To explore how HDCs can exploit diverse and emerging health data sources.
  • To address the challenges in leveraging big data for transformative digital health impact.

Main Methods:

  • Conceptualizing health data cooperatives (HDC) as a model for data governance.
  • Advocating for treating research data as a public good.
  • Promoting citizen empowerment through citizen-owned health data.

Main Results:

  • HDCs can overcome data heterogeneity and governance challenges.
  • Accessible and shared data are essential for realizing the full potential of big data.
  • HDCs enable citizens to participate in improving their health outcomes.

Conclusions:

  • The HDC model fosters citizen participation and data sharing for health improvement.
  • HDCs empower public health institutions to monitor policy impact and population risk.
  • Digital health transformation relies on accessible data and citizen-centric governance models like HDCs.