Duangmanee (Fon) Seedam1, Adithi Ramachandra1, Mr Philip Rawson-Harris1,2, Kirsty Sim1, Rosaleen (Rosie) Kehoe1, Shirley Leong1, Danielle Karanfilovska1, Kaye Bellis1, Lisa Dominguez1, Christine Sharp1, Daniele La1, Denise Delrosario-Kelly1, Megan Gritt1, Pauline Bass1, Dr Andrew Stewardson1,2
1Alfred Health, Melbourne, Australia, 2Monash University, Melbourne, Australia
Biography:
Fon is a Clinical Audit Data Officer at Alfred. She has worked for the HIV Netherlands Australia Thailand Research Project in Thailand and has an interest in SQL programming.
Adithi is a Clinical Audit Data Officer at Alfred. She has postgraduate qualifications in biostatistics and interests in data in healthcare
Abstract:
Problem: Surveillance is a resource intensive process where Infection Prevention (IP) teams face many challenges, including manual data entry, review of microbiological isolates, complex surveillance rules, and fragmented, siloed data storage systems. These issues create inefficiency, are prone to human error, and demonstrate an opportunity cost to time-poor IP nurses.
Solution: Research Electronic Data Capture (REDCap) databases are free, secure, and have growing familiarity and ubiquity whilst providing a centralised store for data extraction to dashboards and reporting. Data teams can support automated surveillance data loads from the Electronic Medical Record. The Epidemiology and Data Management Service in collaboration with IP have implemented three REDCap databases for surveillance automating and reporting – Clostridioides difficile, Staphylococcus aureus bacteraemia (SAB) and Surgical Site Infection (SSI) surveillance.
Results: All isolates and surgeries that meet the surveillance criteria, along with demographics, epidemiology, and SSI antibiotic prophylaxis and swab orders are loaded and reviewed. All data is stored in a centralised location, in a standardised format, and reported via dashboards tailored to multiple audiences. This includes source and ward attribution and nurse auditing data. For cases requiring escalation, letters are automatically sent to the home teams along with supporting documentation to facilitate clinical investigation and identify opportunities for improvement. The SAB database was also successfully shared with another organisation.
Conclusions: Centralising surveillance data through REDCap improved workflow efficiency and reporting. Infection Prevention needs are likely similar statewide for these critical infections and sharing of these REDCap databases is simple, achievable and has been implemented