Enhancing AI Accessibility in Veterinary Medicine: Linking Classifiers and Electronic Health Records
This addresses the challenge of improving diagnostic accuracy and patient care in veterinary healthcare, though it appears incremental as it focuses on a specific software tool for existing data integration.
The paper tackled the problem of integrating machine learning classifiers with electronic health records in veterinary medicine by developing Anna, a freely-available software solution that provides real-time ML classifier results for EHR laboratory data.
In the rapidly evolving landscape of veterinary healthcare, integrating machine learning (ML) clinical decision-making tools with electronic health records (EHRs) promises to improve diagnostic accuracy and patient care. However, the seamless integration of ML classifiers into existing EHRs in veterinary medicine is frequently hindered by the rigidity of EHR systems or the limited availability of IT resources. To address this shortcoming, we present Anna, a freely-available software solution that provides ML classifier results for EHR laboratory data in real-time.