Reproducible Analytical Pipeline for Sustained Improvement in MRSA Admission Screening Compliance: A Quality Improvement Study

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Authors: Daniel Weiand, Peta le Roux, Jamie Lupton, Julie R. Samuel, Chris Plummer
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Abstract

Abstract Introduction Methicillin-resistant Staphylococcus aureus (MRSA) admission screening is recommended to prevent healthcare-associated infection (HCAI), yet compliance rates vary significantly. Manual audit of admission screening compliance (ASC) lacks the timeliness required for effective quality improvement (QI). We aimed to automate weekly surveillance of MRSA-ASC at a large tertiary NHS provider to enable sustained feedback-driven performance improvements. Methods Using fully open-source software (SQL, R and Quarto), we developed a reproducible analytical pipeline (RAP) integrating electronic patient record and laboratory data to systematically evaluate MRSA-ASC. Weekly ward-specific, patient-level reports were automatically generated and published to a secure intranet hub, enabling targeted QI interventions. Statistical process control analysis assessed temporal trends. Results Over 55 weeks, 56504 inpatient records were analysed across approximately 79 locations. Mean MRSA-ASC increased from 59.5% (SD 5.8%) in the first 27 weeks to 64.2% (SD 3%) in the final 27 weeks (difference: 4.7%). The coefficient of variation (8.3%) indicated sustained, stable improvement. Conclusion RAP-generated feedback on MRSA-ASC constitutes an effective mechanism for achieving sustained performance improvements. The RAP operationalised the reflexive monitoring construct central to Normalisation Process Theory (NPT), enabling front-line teams to improve MRSA-ASC without simultaneous educational or process redesign interventions. Automated surveillance through a RAP created using open-source software achieved sustained improvements in MRSA-ASC, providing a time and resource efficient, scalable approach to embedding evidence-based practices.

Fields of Study & Subjects

Infection Control in Healthcare Healthcare Quality and Management Electronic Health Records Systems

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
3 February 2026
Language
EN