Autocoding to Support Adverse Drug Event Surveillance
Description
Manual coding of adverse drug event reports is time-consuming and slows down the production of prevalence estimates. The AI model will automate and speed up the coding process for surveillance epidemiologists.
Detailed example
The model takes a de-identified free-text description of a patient's emergency department visit, along with other pre-coded variables, and outputs the probability that the encounter meets the surveillance case definition for an adverse drug event.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
b) Pilot – The use case has been deployed in a limited test or pilot capacity.
Expected benefit
The AI model will help epidemiologists quickly determine whether reported adverse drug events meet surveillance case definitions, speeding up the coding process and enabling faster, more accurate prevalence estimates for the surveillance system.
Controls / human review
ATO: No; PIA: Not published