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Related Concept Videos

Health Information Technology and Healthcare Information System01:30

Health Information Technology and Healthcare Information System

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Health Information Technology (HIT)
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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
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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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A Semantic-Based Approach for Managing Healthcare Big Data: A Survey.

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Summary
This summary is machine-generated.

Semantic web technologies offer solutions for managing healthcare big data challenges like volume and variety. This review explores how these technologies convert complex health data into actionable knowledge for improved healthcare systems.

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Area of Science:

  • Health Informatics
  • Computer Science
  • Data Science

Background:

  • Healthcare generates vast, heterogeneous data from diverse sources (EHRs, labs, wearables).
  • Traditional data processing struggles with the volume, velocity, and variety of healthcare big data.
  • Existing systems face challenges in extracting meaningful insights from complex health information.

Purpose of the Study:

  • To review the state-of-the-art applications of semantic web technologies in the healthcare industry.
  • To explore how semantic web can address the challenges of healthcare big data.
  • To identify techniques, standards, and viewpoints for converting healthcare data into knowledge.

Main Methods:

  • Comprehensive literature review of semantic web applications in healthcare.
  • Analysis of existing research on healthcare big data challenges.
  • Synthesis of semantic web techniques and standards relevant to health data.

Main Results:

  • Semantic web technologies show promise in managing and processing healthcare big data.
  • These technologies facilitate the transformation of raw health data into valuable knowledge and intelligence.
  • Various semantic web approaches can effectively address the 'big data' challenges in healthcare.

Conclusions:

  • Semantic web technologies are crucial for unlocking the potential of healthcare big data.
  • Adoption of semantic web standards and techniques can enhance healthcare information systems.
  • Further research and implementation are needed to fully leverage semantic web for improved healthcare outcomes.