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This study analyzed the COVID-19 vaccination process in primary care using Resilience Engineering. Findings identified critical variability points and proposed system improvements to enhance vaccination resilience and efficiency.

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Area of Science:

  • Public Health
  • Healthcare Management
  • Resilience Engineering

Background:

  • Vaccination is essential in primary care and for societal well-being.
  • Understanding the COVID-19 vaccination process is key to identifying system vulnerabilities.
  • Resilience Engineering offers a framework to analyze and improve complex healthcare systems.

Purpose of the Study:

  • To map and analyze the COVID-19 vaccination process in a primary care setting.
  • To apply Resilience Engineering concepts to identify failure points and variability.
  • To recommend improvements for enhancing the resilience of vaccination services.

Main Methods:

  • A case study was conducted in a Primary Health Care unit.
  • Data were collected through semi-structured interviews in a cross-sectional study design.
  • The Functional Resonance Analysis Method (FRAM) was used to model the vaccination process.

Main Results:

  • The study identified critical couplings between functions that can lead to high variability.
  • Analysis revealed specific bottlenecks within the vaccination procedural flow.
  • Variability in functional couplings was recognized as a key factor impacting system performance.

Conclusions:

  • The Functional Resonance Analysis Method (FRAM) effectively models healthcare processes and identifies variability.
  • Engineered improvements can enhance system resilience by addressing identified bottlenecks.
  • Proposing countermeasures for critical variability points is crucial for optimizing vaccination services.