Clinical outcomes for asynchronous teledermatology

异步远程皮肤病学的临床结果

• 批准号: 10426001
• 负责人: DENNIS H OH
• 金额: --
• 依托单位: VETERANS AFFAIRS MED CTR SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10426001
• 关键词: Adopted; Area; Artificial Intelligence; Caring; Catalogs; Categories; Clinic; Clinical; Computerized Medical Record; Consult; Consultations; Data; Data Set; Dermatologic; Dermatologist; Dermatology; Development; Diagnosis; Diagnostic; Diagnostic Procedure; Distant; Eczema; Effectiveness; Electronic Health Record; Event; Healthcare Systems; Language; Learning; Machine Learning; Manuals; Measures; Medical Records; Methodology; Methods; Modeling; Monitor; Natural Language Processing; Outcome; Outcome Measure; Outcomes Research; Output; Pathway interactions; Patients; Performance; Persons; Policies; Primary Health Care; Process; Provider; Proxy; Psoriasis; Quality of Care; Recommendation; Rural Community; Safety; San Francisco; Services; Skin; Skin Care; Skin Neoplasms; Surveys; System; Testing; Text; Therapeutic procedure; Time; Training; Translating; Triage; United States Department of Veterans Affairs; Veterans; Veterans Health Administration; Work; base; care providers; clinical outcome measures; clinically significant; dashboard; data warehouse; follow-up; forest; improved; innovation; novel; patient health information; patient safety; programs; relative effectiveness; response; skin disorder; structured data; teledermatology; telehealth; tool; treatment as usual

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.

背景：存储转发远程皮肤病学是退伍军人事务部的重要组成部分 远程医疗产品组合。虽然相当多的证据支持远程皮肤病提供及时访问的潜力 对于皮肤科专家来说，它在实现与通常情况下相当的临床结果方面的有效性-- 由于缺乏对许多皮肤的客观结果衡量标准，个人护理没有得到很好的记录 疾病。临床医生通常使用非标准化的定性语言记录皮肤病。人工 从相对无结构的文本中提取有意义的结果数据的审查通常是令人望而却步的。 重要性/影响：自然语言处理(NLP)提供了一种以前未曾探索过的方法 客观、系统地识别电子病历中的相关文本，以评估患者的临床 面对面皮肤科会诊和异步远程皮肤科会诊后的反应。这 项目将利用NLP跟踪医疗记录中重要皮肤病的临床过程，并 比较远程皮肤科与普通办公室皮肤科的结果和效果 咨询。它还将作为对其他结果衡量标准的测试，例如访问时间通常 被认为是退伍军人护理质量的代理。这一结果可能有助于影响退伍军人管理局的远程医疗战略 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 远程皮肤科在照顾患有各种皮肤病的退伍军人方面的有效性。该项目还将 产生可翻译的NLP系统，以创建实用的可操作的质量管理工具 监测退伍军人事务部皮肤科和远程皮肤科患者的随访护理质量。 具体目标：Aim 1将对专家和非专家临床医生进行调查，以了解每个小组如何评估和 记录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， 并在带注释的调查答复中确定临床医生组之间的差异。Aim 2将使用我们的注释 用于训练和验证NLP模型的数据集，以提取用于五项诊断的概念和关系 根据实际退伍军人管理局的临床记录进行分类。此信息将用于创建能够 将临床变更状态分配给后续记录。AIM 3将把我们的NLP工具的输出整合到 为皮肤病和远程皮肤病转诊分配总体临床结果。其他重要的临床 以结构化数据形式提供的事件和活动将与NLP结果相关联，以进一步解释其 意义。还将比较常用的准入结果衡量标准，以检验其有效性。 方法：Aim 1将对皮肤科医生和初级保健提供者进行调查，以注释和比较他们的 回应。AIMS 2和3将创建经过培训和验证的NLP工具，以将条件和结果状态分配给 实际的临床记录。AIM 3将使用我们的工具来比较远程皮肤病和 皮肤科咨询，并将利用退伍军人管理局公司数据仓库获得结构化数据 其他关键临床事件和准入措施。 实施/下一步：该项目产生的NLP模型可能会在常规之外进行扩展 面对面和远程皮肤病护理，一般跟踪与其他形式的 远程保健，如皮肤科电子会诊和视频远程保健。此外，这些模型可能适用于 创建实用的仪表板工具，使供应商和质量管理人员能够监控有效性 以及向退伍军人患者提供远程皮肤科的质量。