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Harnessing the Power of Large Language Models (LLMs) for Electronic Health Records (EHRs) Optimization

This editorial discusses the potential benefits of integrating large language models (LLMs), such as GPT-4, into electronic health records (EHRs) to optimize patient care, improve clinical decision-making, and promote efficient healthcare management. Artificial intelligence (AI)-driven LLMs can revo...

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Detalles Bibliográficos
Autores principales: Nashwan, Abdulqadir J, AbuJaber, Ahmad A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cureus 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10461074/
https://www.ncbi.nlm.nih.gov/pubmed/37644945
http://dx.doi.org/10.7759/cureus.42634
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author Nashwan, Abdulqadir J
AbuJaber, Ahmad A
author_facet Nashwan, Abdulqadir J
AbuJaber, Ahmad A
author_sort Nashwan, Abdulqadir J
collection PubMed
description This editorial discusses the potential benefits of integrating large language models (LLMs), such as GPT-4, into electronic health records (EHRs) to optimize patient care, improve clinical decision-making, and promote efficient healthcare management. Artificial intelligence (AI)-driven LLMs can revolutionize healthcare practices by streamlining the data input process, expediting information extraction from unstructured narratives, and facilitating personalized patient communication. However, concerns related to patient privacy, data security, and potential biases must be addressed to ensure equitable healthcare for all. Therefore, we encourage healthcare professionals and researchers to explore innovative solutions that leverage AI capabilities while addressing the challenges associated with privacy and equity.
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spelling pubmed-104610742023-08-29 Harnessing the Power of Large Language Models (LLMs) for Electronic Health Records (EHRs) Optimization Nashwan, Abdulqadir J AbuJaber, Ahmad A Cureus Quality Improvement This editorial discusses the potential benefits of integrating large language models (LLMs), such as GPT-4, into electronic health records (EHRs) to optimize patient care, improve clinical decision-making, and promote efficient healthcare management. Artificial intelligence (AI)-driven LLMs can revolutionize healthcare practices by streamlining the data input process, expediting information extraction from unstructured narratives, and facilitating personalized patient communication. However, concerns related to patient privacy, data security, and potential biases must be addressed to ensure equitable healthcare for all. Therefore, we encourage healthcare professionals and researchers to explore innovative solutions that leverage AI capabilities while addressing the challenges associated with privacy and equity. Cureus 2023-07-29 /pmc/articles/PMC10461074/ /pubmed/37644945 http://dx.doi.org/10.7759/cureus.42634 Text en Copyright © 2023, Nashwan et al. https://creativecommons.org/licenses/by/3.0/This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Quality Improvement
Nashwan, Abdulqadir J
AbuJaber, Ahmad A
Harnessing the Power of Large Language Models (LLMs) for Electronic Health Records (EHRs) Optimization
title Harnessing the Power of Large Language Models (LLMs) for Electronic Health Records (EHRs) Optimization
title_full Harnessing the Power of Large Language Models (LLMs) for Electronic Health Records (EHRs) Optimization
title_fullStr Harnessing the Power of Large Language Models (LLMs) for Electronic Health Records (EHRs) Optimization
title_full_unstemmed Harnessing the Power of Large Language Models (LLMs) for Electronic Health Records (EHRs) Optimization
title_short Harnessing the Power of Large Language Models (LLMs) for Electronic Health Records (EHRs) Optimization
title_sort harnessing the power of large language models (llms) for electronic health records (ehrs) optimization
topic Quality Improvement
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10461074/
https://www.ncbi.nlm.nih.gov/pubmed/37644945
http://dx.doi.org/10.7759/cureus.42634
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