Proposed Context-of-Use Evaluation Framework for Medication Management Tasks Completed by Generative Artificial Intelligence

Background: Standardized evaluation of agentic artificial intelligence (AI) for medication management is lacking. Given the potential lethality of medication errors endorsed or missed by AI, performance evaluation constructs are essential. The purpose of this evaluation was to develop a standardized grading framework for performance evaluation of medication management tasks. Methods: A mixed-methods approach was undertaken that included literature evaluation for standards and best practices of comprehensive medication management (CMM), panel discussions, and iterative application to set of cases. The goal was to develop a grading framework that effectively evaluated domains like safety, factuality, and clinical relevance that can be employed for a broad range of medication domains (i.e., electrolyte replacement, antibiotic selection). Inter-rater reliability with intraclass Krippendorffs Alpha was the primary outcome. Results: A total of 5 panelists developed the CMM Evaluation Framework, which includes 4 dimensions: safety, factuality, completeness, and preference. These dimensions are applied to three CMM skills: collecting patient data, analyzing information, and designing regimens. Each dimension is rated from 1-5. An additional dimension evaluated the presence of hallucinations and errors with high harm scores (i.e., absolute failure criteria regardless of an overall score). The Krippendorffs Alpha was highest in the medication therapy problem and medication therapy format categories, for 50 pneumonia cases, run in triplicate (150 total). Conclusions: This framework is informed by national standards for CMM and the healthcare professionals dedicated to the provision of this service. These domains allow for the possibilities of practice variation via the preference domain while also having strong guardrails against the commission of medication errors. Further analyses beyond pilot testing are necessary.