Improving dermatology access by direct-to-patient teledermatology and computer-assisted diagnosis

背景：获得皮肤科仍然是退伍军人事务部的一个重大问题 (Va)，特别是在COVD-19大流行期间。为了满足这一需求，退伍军人管理局将部署一个异步 远程皮肤病移动应用-My VA Images-允许新皮肤病患者安全地提交历史记录 以及他们皮肤的照片以供评估。这款应用最终可能还会为患者提供一个提交 通过人工智能(AI)驱动的计算机视觉对皮肤图像进行随意分析和分类 皮肤科医生。 意义：该项目解决了以下差距：1)直接与患者进行远程皮肤病治疗的影响 获得皮肤科的机会以及患者和卫生保健提供者对这种护理的满意度 没有系统地研究；2)目前还没有开发出人工智能支持的计算机视觉工具 针对患者生成的图像进行验证；3)大型医疗保健组织(如退伍军人管理局)的就绪性；以及 他们的利益相关者从事直接面向患者的远程皮肤病和人工智能尚不清楚。 创新和影响：将测试两项相关创新：1)新的直接到患者的远程皮肤病 患者和2)使用人工智能驱动的计算机视觉对患者提交的皮肤图像进行评估。这两个分别 有可能改变对退伍军人管理局专家皮肤护理的远程访问，并且共同具有潜在的协同作用。 在项目结束时，我们期望对如何直接治疗患者有一个系统的了解 技术表现以及退伍军人管理局在此之前需要解决的运营差距 技术可以在整个企业范围内实施。目标是建立一个具有批判性、学术性和可操作性的 为安全地迈向变革性愿景奠定基础，使退伍军人不再受固定时间的束缚 和护理场所，但将选择自我指导、方便和快速地进入专家级别 皮肤科随时随地提供他们需要的护理。 具体目标：1.评估直接接触患者的远程皮肤病对获取和卫生系统的影响 利用率。2.评估、改进和增强对患者提交的图像的计算机辅助评估。 3.评估退伍军人是否愿意实施直接对患者的护理。 方法：目标1将使用类型I混合语用研究设计来比较直接到- 相对于普通面对面和普通会诊远程皮肤病的患者远程皮肤病干预 转介，主要通过退伍军人管理局中央数据仓库的数据衡量访问情况。目标1和目标3将衡量 使用调查工具和访谈，患者的满意度和对变化的准备情况。AIM 2将包括这两项内容 测试、培训和改进人工智能支持的计算机视觉和测量一致性 皮肤科医生。人群：退伍军人在退伍军人管理局的三个医疗机构看皮肤科。干预： 符合条件且符合医疗条件的患者将可以选择将病历和图像提交至 使用我的退伍军人图像应用程序的皮肤科。比较：干预将与常规护理进行比较(在- 个人和咨询远程皮肤病)小组。我们还将比较两种人工智能驱动的计算机视觉 皮肤科医生诊断的模特。5年内的结果：1)时间和时间的多种衡量标准 获得皮肤科护理的地理位置；2)患者满意度；3)人工智能与皮肤科医生的一致性 诊断；4)组织和患者对远程和计算机辅助皮肤科护理的准备情况；以及 5)直接面向新患者的远程皮肤科流程的实施和可持续性。 下一步/实施：项目的成功完成将为退伍军人管理局的互联护理办公室提供 以及其他负责利用关键数据加强获得特殊护理的办公室，这些数据将证明进一步 扩大直接对患者的异步远程皮肤病计划。该项目还将提供退伍军人 使用关键数据来评估人工智能支持的计算机视觉在未来远程护理战略中的作用。

Background: Access to dermatology remains a significant problem in the Department of Veterans Affairs (VA), particularly during the COVD-19 pandemic. To address this need, VA will deploy an asynchronous teledermatology mobile app-My VA Images-which allows new dermatology patients to securely submit history and photos of their skin for evaluation. The app may also eventually provide a conduit for patients to submit skin images at will for analysis and triage by artificial intelligence (AI)-powered computer vision to a dermatologist. Significance: This project addresses the following gaps: 1) The impact of direct-to-patient teledermatology on access to dermatology and on the satisfaction with such care by both patients and health care providers has not been systematically studied; 2) Currently no AI-powered computer vision tool has been developed and validated for patient-generated images; 3) The readiness of large healthcare organizations, such as VA, and their stakeholders to engage in direct-to-patient teledermatology and AI is unknown. Innovation and Impact: Two related innovations will be tested: 1) Direct-to-patient teledermatology for new patients and 2) Evaluation of patient-submitted skin images by AI-powered computer vision. These separately have the potential to transform remote access to expert skin care in VA and together are potentially synergistic. At the conclusion of the project, we anticipate having a systematic understanding of how direct-to-patient technologies perform and of the operational gaps that will need to be addressed by VA before these technologies can be implemented enterprise-wide. The goal is to establish a critical scholarly and operational foundation to safely move toward a transformative vision where Veterans will no longer be tied to a fixed time and place for care, but instead will have the choice of self-directed, convenient and rapid access to expert-level dermatology care wherever and whenever they need it. Specific Aims: 1. Assess the impact of direct-to-patient teledermatology on access and health system utilization. 2. Assess, refine and augment computer-assisted evaluation of patient-submitted images. 3. Assess readiness of VA and Veterans' acceptance to implement direct-to-patient care. Methodology: Aim 1 will use a Type I hybrid pragmatic study design to compare the impact of the direct-to- patient teledermatology intervention relative to usual in-person and usual consultative teledermatology referrals, measuring access chiefly by data from VA's Central Data Warehouse. Aims 1 and 3 will measure patient satisfaction and readiness for change using survey instruments and interviews. Aim 2 will include both testing, training and refinement of the AI-powered computer vision and measure concordance with dermatologists. Population: Veterans referred to Dermatology at three VA medical facilities. Intervention: Eligible and medically appropriate patients will be offered the option to submit history and images to Dermatology using the My VA Images app. Comparison: The intervention will be compared to usual care (in- person and consultative teledermatology) groups. We will also compare two AI-powered computer vision models with dermatologist diagnoses. Outcomes over a 5-year period: 1) Multiple measures of temporal and geographic access to dermatologic care; 2) Patient satisfaction; 3) Concordance of AI with dermatologist diagnoses; 4) Organizational and patient-readiness for remote and computer-assisted dermatologic care; and 5) Implementation and sustainability of the direct-to-new patient teledermatology process. Next Steps/Implementation: Successful completion of the project will provide VA’s Office of Connected Care and other offices tasked with enhancing access to specialty care with critical data that will justify further expansion of the direct-to-patient asynchronous teledermatology program. The project will also provide VA with critical data to evaluate the role of AI-powered computer vision in future remote care strategies.