What is it about?
This study uses advanced statistics to analyze 318 microbial surface samples from 28 locations in a Class C healthcare cleanroom. Instead of just checking if the room passes or fails, the goal was to create a precise map of contamination risk, identify the dirtiest areas (hotspots), and see if contamination was spreading between locations.
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Why is it important?
The analysis proved that contamination wasn't random but concentrated in specific areas. It pinpointed one location, CP C 11 W (a specific part of the area code), as the primary target for better sanitation. This data-driven approach allows facilities to focus cleaning efforts where they are needed most, improving patient safety and efficiency instead of cleaning everything equally.
Perspectives
This study's perspective is focused on risk management within a single, specific environment (a Class C cleanroom). It aims to move beyond simple compliance checks to provide actionable intelligence for sanitation efforts. By using statistical analysis to identify a "definitive hierarchy of risk" and pinpoint the most contaminated "hotspots," like location CP C 11 W, the goal is to help staff target their cleaning resources more effectively to improve patient safety.
Independent Researcher & Consultant Mostafa Essam Eissa
Read the Original
This page is a summary of: A NON-PARAMETRIC FRAMEWORK FOR ANALYZING SPATIAL HETEROGENEITY AND CONTAMINATION PATHWAYS IN HEALTHCARE ENVIRONMENTS, Universal Journal of Pharmaceutical Research, September 2025, Society of Pharmaceutical Tecnocrats,
DOI: 10.22270/ujpr.v10i4.1390.
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