Mayo Clinic Proceedings. Digital health最新文献

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Medication Extraction and Drug Interaction Chatbot: Generative Pretrained Transformer-Powered Chatbot for Drug-Drug Interaction 药物提取和药物相互作用聊天机器人：生成式预训练变换器驱动的药物交互聊天机器人

Mayo Clinic Proceedings. Digital health

Pub Date : 2024-10-09 DOI: 10.1016/j.mcpdig.2024.09.001

Won Tae Kim MD, PhD , Jaegwang Shin , In-Sang Yoo , Jae-Woo Lee MD, PhD , Hyun Jeong Jeon MD, PhD , Hyo-Sun Yoo MD , Yongwhan Kim MD , Jeong-Min Jo , ShinJi Hwang , Woo-Jeong Lee , Seung Park PhD , Yong-June Kim MD, PhD

Objective

To assist individuals, particularly cancer patients or those with complex comorbidities, in quickly identifying potentially contraindicated medications when taking multiple drugs simultaneously.

Patients and Methods

In this study, we introduce the Medication Extraction and Drug Interaction Chatbot (MEDIC), an artificial intelligence system that integrates optical character recognition and Chat generative pretrained transformer through the Langchain framework. Medication Extraction and Drug Interaction Chatbot starts by receiving 2 drug bag images from the patient. It uses optical character recognition and text similarity techniques to extract drug names from the images. The extracted drug names are then processed through Chat generative pretrained transformer and Langchain to provide the user with information about drug contraindications. The MEDIC responds to the user with clear and concise sentences to ensure the information is easily understandable. This research was conducted from July 1, 2022 to April 30, 2024.

Results

This streamlined process enhances the accuracy of drug-drug interaction detection, providing a crucial tool for health care professionals and patients to improve medication safety. The proposed system was validated through rigorous evaluation using real-world data, reporting high accuracy in drug-drug interaction identification and highlighting its potential to benefit medication management practices considerably.

Conclusion

By implementing MEDIC, contraindicated medications can be identified using only medication packaging, and users can be alerted to potential drug adverse effects, thereby contributing to advancements in patient care in clinical settings.

患者和方法在本研究中，我们介绍了药物提取和药物相互作用聊天机器人（MEDIC），这是一种人工智能系统，通过 Langchain 框架集成了光学字符识别和聊天生成预训练变换器。药物提取和药物交互聊天机器人首先接收患者提供的 2 张药物袋图像。它使用光学字符识别和文本相似性技术从图像中提取药物名称。然后，通过聊天生成预训练变换器和 Langchain 对提取的药物名称进行处理，为用户提供药物禁忌信息。MEDIC 会用简洁明了的句子回复用户，确保信息通俗易懂。这项研究从 2022 年 7 月 1 日开始，到 2024 年 4 月 30 日结束。结果这一简化流程提高了药物相互作用检测的准确性，为医护人员和患者提高用药安全提供了重要工具。通过使用真实世界的数据进行严格评估，验证了所提议的系统，报告了药物相互作用识别的高准确性，并强调了该系统对药物管理实践大有裨益的潜力。结论通过实施 MEDIC，可以仅使用药物包装识别禁忌药物，并提醒用户潜在的药物不良反应，从而促进临床环境中患者护理的进步。

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引用次数: 0

Use of a Head-Mounted Assisted Reality, High-Resolution Telemedicine Camera and Satellite Communication Terminal in an Out-of-Hospital Cardiac Arrest 在院外心脏骤停中使用头戴式辅助现实系统、高分辨率远程医疗摄像机和卫星通信终端

Mayo Clinic Proceedings. Digital health

Pub Date : 2024-10-09 DOI: 10.1016/j.mcpdig.2024.09.002

Christopher S. Russi DO , Sarayna S. McGuire MD , Aaron B. Klassen MD , Kate M. Skeens MD , Kate J. Arms NREMT-P , Lindsey D. Kaczmerick NREMT-P , Patrick J. Fullerton DO, MHCM , Louis M. Radnothy DO , Anuradha Luke MD

Mayo Clinic Ambulance Service is testing a novel combination of technologies to enhance the ability to provide prehospital telemedicine connecting physicians with paramedics. Mayo Clinic Ambulance Service partnered with start-up company OPTAC-X to field test a novel head-mounted video camera connected with a satellite communications terminal to bring medical control emergency medicine physicians to the patient and paramedic by video. The authors believe this is the first report of a physician providing medical guidance to paramedics resuscitating an out-of-hospital cardiac arrest using these technologies.

梅奥诊所救护车服务公司正在测试一种新颖的技术组合，以提高提供院前远程医疗的能力，将医生与护理人员连接起来。梅奥诊所救护车服务公司与初创公司 OPTAC-X 合作，实地测试了一种新型头戴式摄像机，该摄像机与卫星通信终端相连，可通过视频将医疗控制急诊科医生带到病人和救护人员身边。作者认为，这是首次报道医生利用这些技术为急救人员抢救院外心脏骤停患者提供医疗指导。

引用次数: 0

Challenges and Limitations of Human Oversight in Ethical Artificial Intelligence Implementation in Health Care: Balancing Digital Literacy and Professional Strain 在医疗保健领域实施合乎伦理的人工智能时，人工监督所面临的挑战和局限性：兼顾数字素养和专业压力

Mayo Clinic Proceedings. Digital health

Pub Date : 2024-09-07 DOI: 10.1016/j.mcpdig.2024.08.004

Roanne van Voorst PhD

引用次数: 0

Embedding Internal Accountability Into Health Care Institutions for Safe, Effective, and Ethical Implementation of Artificial Intelligence Into Medical Practice: A Mayo Clinic Case Study 将内部问责制嵌入医疗机构，以便在医疗实践中安全、有效、合乎道德地实施人工智能：梅奥诊所案例研究

Mayo Clinic Proceedings. Digital health

Pub Date : 2024-09-06 DOI: 10.1016/j.mcpdig.2024.08.008

Brenna Loufek MS , David Vidal JD , David S. McClintock MD , Mark Lifson PhD , Eric Williamson MD , Shauna Overgaard PhD , Kathleen McNaughton JD , Melissa C. Lipford MD , Darrell S. Pardi MD

Health care organizations are building, deploying, and self-governing digital health technologies (DHTs), including artificial intelligence, at an increasing rate. This scope necessitates expertise and quality infrastructure to ensure that the technology impacting patient care is safe, effective, and ethical throughout its lifecycle. The objective of this article is to describe Mayo Clinic’s approach for embedding internal accountability as a case study for other health care institutions seeking modalities for responsible implementation of artificial intelligence–enabled DHTs. Mayo Clinic aims to enable and empower innovators by (1) building internal skills and expertise, (2) establishing a centralized review board, and (3) aligning development and deployment processes with regulations, standards, and best practices. In 2022, Mayo Clinic established the Software as a Medical Device Review Board (The Board), an independent body of physicians and domain experts to represent the organization in providing innovators regulatory and risk mitigation recommendations for DHTs. Hundreds of digital health product teams have since benefited from this function, intended to enable responsible innovation in alignment with regulation and state-of-the-art quality management practices. Other health care institutions can adopt similar internal accountability bodies using this framework. Opportunity remains to iterate on Mayo Clinic’s approach in alignment with advancing best practices and enhance representation on The Board as part of standard continuous improvement practices.

医疗机构正在以越来越快的速度构建、部署和自我管理数字医疗技术（DHT），包括人工智能。这一范围需要专业知识和高质量的基础设施，以确保影响患者护理的技术在其整个生命周期内都是安全、有效和合乎道德的。本文旨在介绍梅奥诊所嵌入内部问责制的方法，作为其他医疗机构寻求负责任地实施人工智能 DHT 的方法的案例研究。梅奥诊所的目标是通过以下方式为创新者提供支持和授权：（1）培养内部技能和专业知识；（2）建立集中审查委员会；（3）使开发和部署流程与法规、标准和最佳实践保持一致。2022 年，梅奥诊所成立了 "软件作为医疗器械审查委员会"（简称 "委员会"），这是一个由医生和领域专家组成的独立机构，代表组织为创新者提供 DHT 的监管和风险缓解建议。自此，数百个数字医疗产品团队从这一职能中受益，该职能旨在使负责任的创新与法规和最先进的质量管理实践保持一致。其他医疗机构也可以利用这一框架建立类似的内部问责机构。作为标准持续改进实践的一部分，梅奥诊所仍有机会不断改进其方法，并提高董事会的代表性。

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引用次数: 0

Echocardiographic Diagnosis of Hypertrophic Cardiomyopathy by Machine Learning 通过机器学习诊断肥厚型心肌病的超声心动图

Mayo Clinic Proceedings. Digital health

Pub Date : 2024-09-03 DOI: 10.1016/j.mcpdig.2024.08.009

Nasibeh Zanjirani Farahani PhD , Mateo Alzate Aguirre MD , Vanessa Karlinski Vizentin MD , Moein Enayati PhD , J. Martijn Bos MD, PhD , Andredi Pumarejo Medina MD , Kathryn F. Larson MD , Kalyan S. Pasupathy PhD , Christopher G. Scott MS , April L. Zacher MS , Eduard Schlechtinger MS , Bruce K. Daniels RDCS , Vinod C. Kaggal MS , Jeffrey B. Geske MD , Patricia A. Pellikka MD , Jae K. Oh MD , Steve R. Ommen MD , Garvan C. Kane MD , Michael J. Ackerman MD, PhD , Adelaide M. Arruda-Olson MD, PhD

Objective

To develop machine learning tools for automated hypertrophic cardiomyopathy (HCM) case recognition from echocardiographic metrics, aiming to identify HCM from standard echocardiographic data with high performance.

Patients and Methods

Four different random forest machine learning models were developed using a case-control cohort composed of 5548 patients with HCM and 16,973 controls without HCM, from January 1, 2004, to March 15, 2019. Each patient with HCM was matched to 3 controls by sex, age, and year of echocardiography. Ten-fold crossvalidation was used to train the models to identify HCM. Variables included in the models were demographic characteristics (age, sex, and body surface area) and 16 standard echocardiographic metrics.

Results

The models were differentiated by global, average, individual, or no strain measurements. Area under the receiver operating characteristic curves (area under the curve) ranged from 0.92 to 0.98 for the 4 separate models. Area under the curves of model 2 (using left ventricular global longitudinal strain; 0.97; 95% CI, 0.95-0.98), 3 (using averaged strain; 0.96; 95% CI, 0.94-0.97), and 4 (using 17 individual strains per patient; 0.98; 95% CI, 0.97-0.99) had comparable performance. By comparison, model 1 (no strain data; 0.92; 95% CI, 0.90-0.94) had an inferior area under the curve.

Conclusion

Machine learning tools that analyze echocardiographic metrics identified HCM cases with high performance. Detection of HCM cases improved when strain data was combined with standard echocardiographic metrics.

目标开发用于从超声心动图指标自动识别肥厚型心肌病（HCM）病例的机器学习工具，旨在从标准超声心动图数据中高效识别 HCM。根据性别、年龄和超声心动图检查年份，每名 HCM 患者与 3 名对照者配对。使用十次交叉验证来训练识别 HCM 的模型。模型中的变量包括人口统计学特征（年龄、性别和体表面积）和 16 项标准超声心动图指标。4 个独立模型的接收者操作特征曲线下面积（曲线下面积）从 0.92 到 0.98 不等。模型 2（使用左心室整体纵向应变；0.97；95% CI，0.95-0.98）、模型 3（使用平均应变；0.96；95% CI，0.94-0.97）和模型 4（使用每位患者 17 个单个应变；0.98；95% CI，0.97-0.99）的曲线下面积表现相当。相比之下，模型 1（无应变数据；0.92；95% CI，0.90-0.94）的曲线下面积较低。当应变数据与标准超声心动图指标相结合时，对 HCM 病例的检测效果更好。

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引用次数: 0

Transforming Large Language Models into Superior Clinical Decision Support Tools by Embedding Clinical Practice Guidelines 通过嵌入临床实践指南，将大型语言模型转化为卓越的临床决策支持工具

Mayo Clinic Proceedings. Digital health

Pub Date : 2024-09-01 DOI: 10.1016/j.mcpdig.2024.05.018

Yanshan Wang PhD , Xiaoxi Yao PhD, MPH , Xizhi Wu

引用次数: 0

Validating a Portable, Camera-Based System to Scale the Clinical Gait Assessment as a Tele-Health Solution 验证基于摄像头的便携式系统，将临床步态评估扩展为远程保健解决方案

Mayo Clinic Proceedings. Digital health

Pub Date : 2024-09-01 DOI: 10.1016/j.mcpdig.2024.05.020

Kevin A. Mazurek Ph.D. , Leland Barnard Ph.D. , Hugo Botha M.B., Ch.B. , Teresa Christianson , Jonathan Graff-Radford M.D. , David T. Jones M.D. , David S. Knopman M.D. , Ronald C. Petersen M.D., Ph.D. , Prashanthi Vemuri Ph.D. , Clifford R. Jack Jr. M.D. , Farwa Ali M.B.B.S.

引用次数: 0

The Proceedings of the Inaugural Mayo Clinic Digital Health Research Symposium 梅奥诊所首届数字健康研究研讨会论文集

Mayo Clinic Proceedings. Digital health

Pub Date : 2024-09-01 DOI: 10.1016/j.mcpdig.2024.05.011

Bart M. Demaerschalk MD, MSc, Barbara J. Copeland, Christopher M. Wittich MD

引用次数: 0

Utilization of Emergency Medicine Telehealth Support for Pediatric Patients in Community Emergency Departments 社区急诊室利用急诊医学远程医疗支持治疗儿科患者

Mayo Clinic Proceedings. Digital health

Pub Date : 2024-09-01 DOI: 10.1016/j.mcpdig.2024.05.021

Elizabeth C. Fogelson MD , Jacob A. Klinger , Aidan F. Mullan MS , David M. Nestler MD MS , M. Fernanda Bellolio MD MS , Laura E. Walker MD MBA

引用次数: 0

Impact of an Automated Digital Navigation Program on Colonoscopy No-Show Rates: A Study in an Underserved Population 自动数字导航程序对结肠镜检查未显示率的影响：一项针对未获服务人群的研究

Mayo Clinic Proceedings. Digital health

Pub Date : 2024-09-01 DOI: 10.1016/j.mcpdig.2024.05.019

Morish Shah B.S. , Shashank Garg M.S. , Sarthak Kakkar M.S. , Dr. Ashish Atreja M.D., M.P.H.

引用次数: 0

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