Clinical outcomes for asynchronous teledermatology

背景：存储转发远程皮肤病学是退伍军人事务部的一个重要组成部分， 远程保健组合。虽然有相当多的证据支持远程皮肤病学的潜力， 专家皮肤科护理，其在实现临床结果的有效性，相当于通常在- 由于缺乏对许多皮肤的客观结果测量， 疾病临床医生通常使用非标准化的定性语言记录皮肤疾病。手动 从相对非结构化的文本中提取有意义的结果数据的审查通常是禁止的。 意义/影响：自然语言处理（NLP）提供了一种以前未探索过的方法， 客观系统地识别电子病历中的相关文本， 反应后，无论是在人的皮肤科和异步远程皮肤科咨询。这 该项目将利用NLP来跟踪医疗记录中重要皮肤状况的临床过程， 比较远程皮肤病学与普通办公室皮肤病学的结果和有效性 协商它还将作为对其他结果指标的测试，例如访问时间， 被认为是退伍军人护理质量的代表。结果可能有助于影响VA远程医疗策略 以及加强患者获得高质量皮肤护理和改善患者安全的政策。 创新：该项目代表了NLP方法的新应用，以了解关键临床医生如何 记录皮肤状况，并提供大规模，系统和严格的评估， 远程皮肤科的有效性，在照顾退伍军人与各种皮肤病。该项目还将 导致NLP系统，这可能是可翻译的，以创建实用的操作质量管理工具， 监测VA中皮肤科和远程皮肤科患者的随访护理质量。 具体目标：目标1将调查专家和非专家临床医生，以了解每组如何评估和 记录了五种常见皮肤诊断类别的临床变化。我们将测试新的注释方法， 并在注释的调查响应中识别临床医生组之间的差异。目标2将使用我们的注释 数据集来训练和验证NLP模型，为我们的五个诊断提取概念和关系。 实际VA临床记录的类别。这些信息将用于创建一个文档分类器， 将临床变化状态分配给随访记录。Aim 3将整合NLP工具的输出， 将总体临床结果分配给皮肤科和远程皮肤科转诊。其他重要临床 作为结构化数据可用的事件和活动将与NLP结果相关联，以进一步解释其 意义还将比较常用的获取结果措施，以检验其有效性。 方法：目标1将调查皮肤科医生和初级保健提供者，以注释和比较他们的 应答目标2和目标3将创建经过培训和验证的NLP工具， 真正的临床记录目标3将使用我们的工具来比较远程皮肤病学和 皮肤科咨询，并将利用VA公司数据仓库，以获得结构化的数据， 其他关键临床事件和访问措施。 实现/后续步骤：从这个项目中产生的NLP模型可以扩展到常规之外 现场和远程皮肤科护理，以普遍跟踪与其他形式的 远程保健，如皮肤科电子咨询和视频远程保健。此外，这些模型可以适用于 创建一个实用的仪表板工具，使供应商和质量管理人员能够监测有效性 和远程皮肤科的质量。

Background: Store-and-forward teledermatology is a significant part of Department of Veterans Affairs’ telehealth portfolio. While considerable evidence supports teledermatology’s potential to provide timely access to expert dermatologic care, its effectiveness in achieving clinical outcomes that are equivalent to usual in- person care has not been as well documented due to the lack of objective outcome measures for many skin diseases. Clinicians typically document skin diseases using non-standardized qualitative language. Manual review to extract meaningful outcomes data from relatively unstructured text is typically prohibitive. Significance/Impact: Natural language processing (NLP) offers a previously unexplored approach to objectively and systematically identify relevant text in the electronic medical record to gauge patients’ clinical responses following either in-person dermatology and asynchronous teledermatology consultation. This project will leverage NLP to follow clinical courses of important skin conditions in the medical record and to compare the outcomes and effectiveness of teledermatology relative to usual office-based dermatology consultation. It will also serve as a test for other outcome measures such as access times that are often assumed to be proxies for quality of care for Veterans. The results may help influence VA telehealth strategy and policies to enhance access of patients to high quality skin care and to improve patient safety. Innovation: This project represents a novel application of NLP methods to understand how key clinicians document skin conditions and to provide a large-scale, systematic and rigorous assessment of teledermatology’s effectiveness in caring for Veterans with a variety of skin diseases. The project will also result in NLP systems which may be translatable to create practical operational quality management tools for monitoring the quality of follow-up care of both dermatology and teledermatology patients in VA. Specific Aims: Aim 1 will survey expert and non-expert clinicians to learn how each group evaluates and documents clinical change in five common skin diagnostic categories. We will test novel annotation methods, and identify differences between clinician groups in annotated survey responses. Aim 2 will use our annotated data sets to train and validate NLP models to extract concepts and relationships for our five diagnostic categories from actual VA clinical notes. This information will be used to create a document classifier capable of assigning a clinical change status to follow-up notes. Aim 3 will integrate output from our NLP tools to assign an overall clinical outcome to dermatology and teledermatology referrals. Other important clinical events and activities available as structured data will be correlated with NLP outcomes to further interpret their significance. Commonly used access outcome measures will also be compared as a test of their validity. Methodology: Aim 1 will survey dermatologists and primary care providers to annotate and compare their responses. Aims 2 and 3 will create trained and validated NLP tools to assign condition and outcome status to actual clinical notes. Aim 3 will use our tools to compare clinical outcomes following teledermatology and dermatology consultation and will will utilize the VA Corporate Data Warehouse to obtain structured data on other key clinical events and access measures. Implementation/Next Steps: The NLP models that result from this project may be extendable beyond routine in-person and teledermatology care to generally track clinical course outcomes related to other forms of telehealth such as dermatology e-consults and video telehealth. In addition, the models may be adaptable to create a practical dashboard tool to allow providers and quality management staff to monitor the effectiveness and quality of teledermatology delivered to Veteran patients.