MERIT: Multi-agent EHR-grounded Risk Identification and Tailored Care for Early Cognitive Decline in Primary Care

Background: Dementia is frequently underdiagnosed in primary care, with fewer than half of cases identified and only 8-20% detected at early stages. Our objective is to develop an Agentic AI-powered clinical decision support systems (CDSS) maintaining the patient's virtual cognitive status and provide physicians with ready-to-use summaries and next step suggestions. Methods: We developed MERIT (Multi-agent EHR-grounded Risk Identification and Tailored Care for Early Cognitive Decline in Primary Care) via participatory co-design with primary care provider. It maintains a virtual cognitive status based on the patient's electronic health records (EHR) and conversation with the patient and provide clinical decision support based on it. Retrieval augmented generation (RAG) was employed by the system to retrieve dementia-related risk factors from multimodal EHR. Additionally, we used reflection Agentic AI design pattern to improve quality of AI Agent generated text. We used one PCP's patient data to optimize the system, then used patients from another four PCPs for testing. We mainly used expert manual evaluation based on 5-point Likert-Scales. For readability of patient summary and personalized education material, besides manual evaluation, we also employed widely used Flesch Reading Ease and Flesch We-Kincaid Grade Level. Results: MERIT has four modulars: 1) patient conversation agent; 2) patient virtual cognitive status maintainer; 3) personalized education provider; and 4) decision support provider. Our evaluation focused on the provider-facing part, which relies on EHR without involving any patient conversation. The evaluation on the testing data demonstrates good performance. On a 5-point Likert scale, in the test set, patient virtual cognitive status summaries received average scores of 4.0 (SD 0.74) for correctness, 4.2 (SD 0.59) for completeness, 4.3 (SD 0.59) for ...