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Almanac Copilot: Towards Autonomous Electronic Health Record Navigation

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arxiv 2405.07896 v2 pith:JCLMNLGK submitted 2024-04-30 cs.AI cs.HCcs.IRcs.LG

classification cs.AIcs.HCcs.IRcs.LG
keywords almanacautonomousclinicianscopilotdocumentationtasksclinicianelectronic
verification ladder T0 review T1 audit T2 compute T3 formal
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Clinicians spend large amounts of time on clinical documentation, and inefficiencies impact quality of care and increase clinician burnout. Despite the promise of electronic medical records (EMR), the transition from paper-based records has been negatively associated with clinician wellness, in part due to poor user experience, increased burden of documentation, and alert fatigue. In this study, we present Almanac Copilot, an autonomous agent capable of assisting clinicians with EMR-specific tasks such as information retrieval and order placement. On EHR-QA, a synthetic evaluation dataset of 300 common EHR queries based on real patient data, Almanac Copilot obtains a successful task completion rate of 74% (n = 221 tasks) with a mean score of 2.45 over 3 (95% CI:2.34-2.56). By automating routine tasks and streamlining the documentation process, our findings highlight the significant potential of autonomous agents to mitigate the cognitive load imposed on clinicians by current EMR systems.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Smart Multimodal Healthcare Copilot with Powerful LLM Reasoning

    cs.AI 2025-06 reject novelty 2.0 of 10

    A multimodal healthcare copilot using KG-elicited RAG is described, but the claimed superiority over existing systems is not demonstrated by the reported evaluation.

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