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Managing Data in Screening Programs: Challenges and Solutions.

Hugo Monteiro1, Mariana Oliveira1, Ricardo Martinho2

  • 1Faculdade de Medicina. Universidade do Porto. Porto. Portugal.

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Summary
This summary is machine-generated.

Big data analytics can streamline population health screening programs by improving data management, from archiving to real-time processing and predictive modeling. This framework enhances efficiency, resource optimization, and patient care in public health initiatives.

Keywords:
Big DataData ManagementDiagnostic Screening ProgramsPublic Health

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Area of Science:

  • Public Health Informatics
  • Health Data Science
  • Healthcare Management

Background:

  • Population-based screening programs are crucial for early disease detection and cost reduction.
  • Expanding programs generate vast amounts of data, necessitating structured digital solutions.
  • Effective data management is essential for optimizing screening workflows and patient outcomes.

Purpose of the Study:

  • To present a pragmatic framework for big data analytics in healthcare screening.
  • To guide healthcare professionals and administrators in integrating data analytics into routine workflows.
  • To illustrate the application of big data concepts with real-world examples.

Main Methods:

  • Systematic archiving using cloud-based storage for secure data maintenance.
  • Real-time processing for rapid decision-making and patient management.
  • Data transformation and integration into centralized analytical platforms.
  • Advanced analytics including process mining and predictive modeling.
  • Real-time visualization tools for operational insights.

Main Results:

  • Structured data management significantly improves operational efficiency and healthcare accessibility.
  • Flexible digital infrastructures are vital for adapting to public health crises.
  • Big data analytics can reveal inefficiencies and optimize resource allocation in screening programs.
  • Predictive modeling aids in the early identification of at-risk individuals.

Conclusions:

  • Adoption of big data analytics requires investment in digital infrastructure, professional training, and data governance.
  • A structured approach to data management enhances the effectiveness of preventive healthcare programs.
  • Continuous improvement of screening workflows is achievable through data-driven insights.