{"title":"Efficient healthcare with large language models: optimizing clinical workflow and enhancing patient care.","authors":"Satvik Tripathi, Rithvik Sukumaran, Tessa S Cook","doi":"10.1093/jamia/ocad258","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>This 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.</p><p><strong>Potential: </strong>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.</p><p><strong>Caution: </strong>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.</p><p><strong>Conclusion: </strong>By 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.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11105142/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the American Medical Informatics Association","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1093/jamia/ocad258","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose: This 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.
Conclusion: By 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.
期刊介绍:
JAMIA is AMIA''s premier peer-reviewed journal for biomedical and health informatics. Covering the full spectrum of activities in the field, JAMIA includes informatics articles in the areas of clinical care, clinical research, translational science, implementation science, imaging, education, consumer health, public health, and policy. JAMIA''s articles describe innovative informatics research and systems that help to advance biomedical science and to promote health. Case reports, perspectives and reviews also help readers stay connected with the most important informatics developments in implementation, policy and education.