ArticleCureus2023
Harnessing the Power of Large Language Models (LLMs) for Electronic Health Records (EHRs) Optimization.
Article in Cureus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Article
- A review for navigating the trade-offs: evaluating open-source and proprietary large language models for clinical and biomedical information extraction.Frontiers in digital health · 2026Review
- Digital therapeutics into geriatric cardiovascular emergency care.Frontiers in digital health · 2026Review
- Leveraging Large Language Models for Automating Outpatients' Message Classifications of Electronic Medical Records.Healthcare (Basel, Switzerland) · 2025Article
- Artificial intelligence for detecting anaphylaxis in electronic medical records.Asia Pacific allergy · 2025Article
- [Performance of a prompt engineering method for extracting individual risk factors of precocious puberty from electronic medical records].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2025Article
- Evaluation and Bias Analysis of Large Language Models in Generating Synthetic Electronic Health Records: Comparative Study.Journal of medical Internet research · 2025Article
- Applications of Artificial Intelligence in Vasculitides: A Systematic Review.ACR open rheumatology · 2025Review
- Eyes on Google's NotebookLM: using generative AI to create ophthalmology podcasts with a single click.Eye (London, England) · 2025Article
- Understanding and training for the impact of large language models and artificial intelligence in healthcare practice: a narrative review.BMC medical education · 2024Review
- Large Language Model Prompting Techniques for Advancement in Clinical Medicine.Journal of clinical medicine · 2024Review
- Transforming Hospital Quality Improvement Through Harnessing the Power of Artificial Intelligence.Global journal on quality and safety in healthcare · 2024Article
- Perceptions and attitudes of nurse practitioners toward artificial intelligence adoption in health care.Health science reports · 2024Article
- Digital health delivery in respiratory medicine: adjunct, replacement or cause for division?European respiratory review : an official journal of the European Respiratory Society · 2024Review
- Article
- Towards Optimizing LLM Use in Healthcare: Identifying Patient Questions in MyChart Messages.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Advancing rheumatology with natural language processing: insights and prospects from a systematic review.Rheumatology advances in practice · 2024Review
- Review
- Harnessing Artificial Intelligence for Qualitative and Mixed Methods in Nursing Research.Cureus · 2023Article
- Opportunities and Challenges for Large Language Models in Primary Health Care.Journal of primary care & community healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
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What 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.