Enhancing AI Accessibility in Veterinary Medicine: Linking Classifiers and Electronic Health Records

Background: 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. Results: To address this shortcoming, we present Anna, a freely-available software solution that provides ML classifier results for EHR laboratory data in real-time. Anna is a standalone platform developed in Python, designed to host ML classifiers, retrieve patient-specific data from an EHR system, generate classifier results and return these results to the EHR for display. Anna merges results from different diagnostic tests according to user-defined temporal criteria and determines whether the data are sufficient for a given classifier. Because Anna is a stand-alone platform, it does not require substantial modifications to the existing EHR, allowing for easy integration into existing computing infrastructure. To demonstrate Anna's versatility, we implemented three previously published ML classifiers to predict a diagnosis of hypoadrenocorticism, leptospirosis, or a portosystemic shunt in dogs. Conclusion: Anna is an open-source tool designed to improve the accessibility of ML classifiers for the veterinary community. Its flexible architecture supports the integration of classifiers developed in various programming languages and with diverse environment requirements.