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Documentation in long-term care facilities and home healthcare settings is crucial for ensuring continuous, coordinated, and comprehensive care for patients. Each setting has its specific documentation processes and tools:
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Principles of Disease Surveillance01:26

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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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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 sharing: A Long COVID perspective, challenges, and road map for the future.

Sunday O Oladejo1, Liam R Watson1,2, Bruce W Watson1

  • 1School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa.

South African Journal of Science
|September 26, 2024
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Long COVID, a prolonged illness after SARS-CoV-2 infection, affects millions globally. This study explores data science and sharing to accelerate understanding and relief for Long COVID patients.

Keywords:
Long COVIDdata sciencedata sharing

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Area of Science:

  • Epidemiology
  • Genomics
  • Medicine
  • Data Science
  • Computer Science

Background:

  • Long COVID, characterized by persistent symptoms post-COVID-19 infection, impacts a significant global population.
  • The COVID-19 pandemic highlighted the power of interdisciplinary collaboration and data sharing in addressing health crises.
  • Currently, there is no universal agreement on the definition, diagnosis, or treatment strategies for Long COVID.

Purpose of the Study:

  • To investigate the potential of data sharing and data science in advancing the understanding of Long COVID.
  • To identify challenges and opportunities in current Long COVID research data sharing practices.
  • To expedite relief for individuals suffering from prolonged SARS-CoV-2 symptoms.

Main Methods:

  • Literature review of existing research on Long COVID.
  • Analysis of current data sharing practices and their limitations in Long COVID studies.
  • Exploration of data science methodologies applicable to Long COVID research.

Main Results:

  • The study identifies a critical need for standardized data sharing protocols in Long COVID research.
  • Data science offers powerful tools for analyzing complex datasets to uncover patterns in Long COVID.
  • Significant challenges remain in data accessibility, interoperability, and ethical considerations.

Conclusions:

  • Enhanced data sharing and advanced data science approaches are crucial for unraveling the complexities of Long COVID.
  • Collaborative efforts are essential to establish consensus on Long COVID definition, diagnosis, and treatment.
  • Harnessing data science can accelerate the development of effective interventions and support for Long COVID patients.