Mr Philip Rawson-Harris1,2, Katherine Berry1, Adithi Ramachandra1, Matt Hacket1,2, Kirsty Sim1, Fiona Holzer1, Sue Mclellan1, Pauline Bass1, Dr Andrew Stewardson1,2
1Alfred Health, Melbourne, Australia, 2Monash University, Melbourne, Australia
Biography:
Katherine is a Clinical Audit Data Officer in the Infectious Diseases Department of Alfred Health. She has a graduate degree in Podiatry and many years of experience in healthcare data.
Philip leads the Epidemiology and Data Management Service Infectious Diseases at Alfred Health with interests in data visualisation and automation.
Abstract:
Introduction
The Staff Immunisation and Exposure Management (SIEM) team manages Health Care Worker immunisations by assessing the complex interactions between serology, vaccination and other demographic details. This provides a final immunisation status per disease. We aimed to assess the effectiveness of implementation of automated immunisation assessments on improving data quality.
Methods
The Epidemiology and Data Management Service (EDMS) in collaboration with SIEM automated the data loads from the electronic medical record (EMR) to the immunisation database and built the intricate disease specific programming required to produce automated status assessments for six key vaccine preventable diseases and TB exposure assessments. The data was taken for >10,000 staff and up to 70,000 data points checked daily. The seven key diseases were reviewed before and after automation: Hepatitis B, Measles, Mumps, Rubella (MMR), Pertussis, Tuberculosis and Varicella Zoster. Data was categorised into 5 categories: Correct, Status Missing, Status Date Missing, Vaccination/serology data missing and Incorrect.
Results
Pre- (Nov 2023) and post-automation (May 2025) immunisation records were analysed. Pre-automation, 81% (58,692/72,226) of records had correct statuses. Post-automation, this increased to 95% (76,107/80,283). Pertussis increased the most from 51% (5335/10318) to 99.6% (11428/11469) alongside tuberculosis from 81% (8375/10318) to 93.5% (10722/11469) post automation. MMR and Hepatitis B improved from ~90% to 94% and 97% respectively.
Conclusion
Automation of immunisation status demonstrates a valuable increase in cleanliness and completeness of data and increases the efficiency of SIEM and reliance on this information for exposure management.