A conversational artificial intelligence agent for medication reconciliation and review

Abstract

Abstract Medication reconciliation, the process of creating an accurate medication list for a patient, is critical to patient safety and care quality but requires clinical expertise and time. Large language models (LLMs), with the ability to generate language and respond to diverse prompts, oVer the potential to automate medication reconciliation and review, including via spoken conversation. We developed AMREC, the Atman Medication REconciliation Conversational AI agent. AMREC uses a fine-tuned version of Llama-3.1-8B-Instruct to standardize a patient’s medication list by extracting 18 elements from each prescription. A voice agent then follows two dialogue flows: 1) iterating through the medication list with identification and correction of discordances and 2) collecting and clarifying requisite details on any additional medications taken. The extraction model achieved an accuracy rate of 98.3% across prescription elements, and user testing demonstrated the conversational AI agent’s ability to confirm, correct, remove, and add new medications to a candidate list. With additional development, AMREC could be deployed in the context of frequent medication reconciliation, thereby improving patient care outcomes and reducing the high-cost burden of medication errors on the healthcare system.

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