Machine learning: Patterns and pathways to link administrative health records

Linking routinely collected administrative health records has the potential to significantly increase our understanding of diseases, health outcomes, health service utilization and health expenditure. State and national databases contain health records from general practice visits, emergency department presentations, hospital admissions, outpatient care and elective surgery lists. To protect patient privacy and ensure information security, these databases are created and stored in silos, with no unique patient identifiers to link them together and describe the patients journey through the health system.
There are no standard protocols to link disparate databases. While some of this is due to limitations in the quality and comparability of datasets, there is a lack of automated processes for data cleaning and sequencing once patient pathways have been identified. Artificial intelligence provides an opportunity to maximize the use and capacity of administrative health records in research.