An Event of our ‘Unsolved Puzzles’ Round Table Series on 30 July 2026 at 4pm – 5:30pm AEST
Electronic Health Records (EHRs) are often viewed as the ideal foundation for artificial intelligence (AI) in healthcare because they contain vast amounts of patient information collected during routine clinical care.
However, despite their size and richness, EHR datasets are among the most challenging forms of data for AI development.
Clinical information is fragmented across structured fields and free text, recorded by different clinicians with varying documentation styles, influenced by workflow rather than research protocols, and frequently incomplete or biased. These complexities limit model performance, generalisability, and clinical adoption.
This presentation explores the hidden challenges within EHR data, explains why developing trustworthy healthcare AI is substantially more difficult than many expect, and discusses emerging approaches – including interoperability standards, multimodal AI, and digital twins – which may help overcome these limitations.
Dr Verghese is a Chief Medical Information Officer (CMIO) and Intensive Care Specialist with a strong interest in data analytics, artificial intelligence (AI), and electronic medical record (EMR) implementation across South Australia.
At SA Health, he leads digital health initiatives that focus on integrating advanced data solutions, enhancing EMR systems, and improving healthcare outcomes through technology.
His dual expertise in clinical practice and informatics enables him to design and implement strategies that meet the complex demands of both critical care and healthcare data systems. As an intensivist, he prioritises the delivery of safe, high-quality, patient-centred care, leveraging EMR systems, predictive analytics, and AI to streamline clinical workflows and support timely, data-driven decision-making.
Dr Verghese’s work in EMR implementation across South Australia has strengthened interoperability, clinical documentation, and care coordination, driving consistency and efficiency in how care is delivered statewide.