ArticleJournal of the American Medical Informatics Association : JAMIA2024
Efficient healthcare with large language models: optimizing clinical workflow and enhancing patient care.
Article in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
What it found
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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.
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Who cites it
43 citing papers in PubMed.
- Association Between Large Language Model-Derived Mobility Functional Status Assessment and Clinical Outcomes.Journal of the American Geriatrics Society · 2026Article
- Bridging primary care with artificial intelligence: toward a global 1.5-Tier Healthcare System.npj health systems · 2026Review
- A Secure, Scalable Large Language Model-Based System (CIDER) for High-Throughput Clinical Data Extraction From Medical Reports: Retrospective Validation Study.Journal of medical Internet research · 2026Article
- Multimodal integrated knowledge transfer to large language models through preference optimization with biomedical applications.Patterns (New York, N.Y.) · 2026Article
- Artificial Intelligence Can Direct Patients Toward a Complaint-specific Musculoskeletal Provider.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2026Article
- Measuring health professional students' willingness to use AI chatbots in learning: A fuzzy-set qualitative comparative analysis.Anatomical sciences education · 2026Article
- Mapping the Reliability-Readability Gap in the Education of Patients With Age-Related Macular Degeneration Across 6 Large Language Models: Comparative Evaluation Study.JMIR medical informatics · 2026Article
- Charting Tomorrow: A Practical Review of Artificial Intelligence in Medical Documentation.Plastic and reconstructive surgery. Global open · 2026Article
- Language-Specific Differences in Large Language Model Diagnostic Reasoning: A Translation-Controlled Clinical Vignette Study.Journal of clinical medicine · 2026Article
- Applicable Scenarios, Desired Features, and Risks of AI Psychotherapists in Depression Treatment From the Patient's Perspective: Exploratory Qualitative Study.JMIR formative research · 2026Article
- Large language models are poor clinical administrators: An evaluation of structured queries in real-world electronic health records.PLOS digital health · 2026Article
- PsychiatryBench: a multi-task benchmark for LLMs in psychiatry.NPJ digital medicine · 2026Article
- Potential of large language models for rapid clinical information support: evidence from acute kidney injury knowledge testing.Scientific reports · 2026Article
- Advancing trauma scoring through large language models: automated estimation of injury severity.BMC emergency medicine · 2026Article
- Article
- Uses of generative AI by non-clinician staff at an academic medical center.npj health systems · 2026Article
- GraphRAG-Enabled Local Large Language Model for Gestational Diabetes Mellitus: Development of a Proof-of-Concept.JMIR diabetes · 2026Article
- Large Language Models and Primary Care: A Scoping Review.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Clinician and patient perspectives on digital technology platforms for healthcare delivery.Frontiers in digital health · 2026Article
- Quality assessment of large language model-generated prior authorization letters in nephrology.Frontiers in digital health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis article explores the potential of large language models (LLMs) to automate administrative tasks in healthcare, alleviating the burden on clinicians caused by electronic medical records. POTENTIAL: LLMs offer opportunities in clinical documentation, prior authorization, patient education, and access to care. They can personalize patient scheduling, improve documentation accuracy, streamline insurance prior authorization, increase patient engagement, and address barriers to healthcare access. CAUTION: However, integrating LLMs requires careful attention to security and privacy concerns, protecting patient data, and complying with regulations like the Health Insurance Portability and Accountability Act (HIPAA). It is crucial to acknowledge that LLMs should supplement, not replace, the human connection and care provided by healthcare professionals.
conclusionBy prudently utilizing LLMs alongside human expertise, healthcare organizations can improve patient care and outcomes. Implementation should be approached with caution and consideration to ensure the safe and effective use of LLMs in the clinical setting.
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.