ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024
Automating Patient Safety Workflows: The Development and Implementation of LLaMPS, a Secure Large Language Model Application.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
13 authors.
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Abstract
Despite significant advancements in Generative Artificial Intelligence (GenAI), practical adoption in healthcare, particularly patient safety, remains challenging due to concerns regarding data privacy, model transparency, clinical relevance and user engagement. We present LLaMPS (Large Language Model for Patient Safety), a locally deployed GenAI platform designed to enhance patient safety event management and reporting. LLaMPS integrates automated incident classification, harm-level prediction, intelligent search, and an interactive chatbot. The system employs a Retrieval-Augmented Generation (RAG) approach, leveraging secure, institutionally hosted large language models (LLMs) and a vector database to ensure data privacy and regulatory compliance. Developed iteratively with direct input from clinicians and patient safety experts, LLaMPS demonstrates high classification accuracy and improved user satisfaction, underscoring the potential of locally controlled AI solutions to enhance patient safety workflows.
Indexed as
Identifiers
41726484PMC12919502What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.