Automating detection of diagnostic error of infectious diseases using machine learning (7 June 2024)

Summary

Diagnostic error is largely discovered and evaluated through self-reporting and manual review, which is costly and not suitable for real-time intervention. AI presents new opportunities to use electronic health record data for automated detection of potential misdiagnosis, executed at scale and generalised across diseases. The authors of this study propose a new, automated approach to identifying diagnostic divergence considering both diagnosis and risk of mortality.

The aim of this study was to identify cases of misdiagnosis of infectious disease in the emergency department by measuring the difference between predicted diagnosis and documented diagnosis, weighted by mortality. Two machine learning models were trained for prediction of infectious disease and mortality using the first 24 hours of data. Charts were manually reviewed by clinicians to determine whether there could have been a more correct or timely diagnosis.

This year’s World Patient Safety Day on 17 September 2024 (WPSD 2024) is focused on the theme “Improving diagnosis for patient safety”. Find out more.