Evaluating diagnostic decision support systems for patients requiring urgent primary or emergency care or with stroke

评估需要紧急初级或紧急护理或中风患者的诊断决策支持系统

• 批准号: 10720028
• 负责人: Hamish SF Fraser
• 金额: $ 38.93万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10720028
• 关键词: Evaluating; diagnostic; decision; support; systems

Medical diagnosis is a critical component of effective health care but misdiagnosis, delayed diagnosis and incorrect triage is common especially in urgent or emergency care settings, and a major contributor to adverse clinical events. While improvements in health care are important in addressing misdiagnosis, patients have a key role by recognizing potentially serious symptoms and seeking care in a timely manner. These concerns are of particular importance for patients requiring urgent or emergency care with potentially life-threatening diseases, such as transient ischemic attacks (TIA), stroke or myocardial infarction. They often fail to recognize the seriousness of their symptoms and may fail to seek care promptly, resulting in missed treatments and poorer outcomes. Patients with stroke typically must be treated within 4 hours to achieve a good response, and public education campaigns have not significantly helped. Smartphone apps for medical diagnosis termed Symptom Checkers (SCs) are widely available to patients in the US and worldwide. They have been shown to be usable by patients, and can affect patient decision making and care seeking behavior. Evidence from our work and others has shown that, if used correctly, SCs can achieve accuracy of diagnosis and triage close to that of physicians (relying on symptom data). However, most studies are based on case summaries created by physicians, SC apps are not used by patients, and lack evidence on the effects of patient characteristics, or SCs influence on patient decision making. To address these gaps we will evaluate the safety, usability, diagnostic and triage accuracy of a leading symptom checker in use by patients in an emergency department or urgent primary care, and the effect of SC outputs on patients’ decisions to seek care. In aim 1 we will recruit 700 patients to use a SC app from Ada Health when they are seen in urgent primary care or the emergency department at Rhode Island Hospital (RIH), including those with possible symptoms of TIA or stroke. This builds on our previous studies of the Ada SC with 241 patients recruited in these locations. The level of urgency of care they intend to seek will be assessed before and after use of Ada, along with a questionnaire on app usability. Diagnostic and triage accuracy will compared to the assessment of the physician who saw the patient, and the Ada results both compared with, and critiqued by, independent physicians viewing the symptom data collected by Ada. We will also evaluate the effects of different presentations of diagnosis and triage data on patient decision making. In aim 2 we will utilize 2 unique data sets of 2300 patients with possible TIA or stroke seen in the ED at RIH, and use machine learning techniques to create new algorithms to improve early diagnosis and risk stratification. Performance will be compared with existing algorithms and guidelines on accurate diagnosis and effective management of these conditions. We will also analyze a data set of 158,000 patients with possible TIA or stroke who used the Ada app in a community setting, and evaluate the influence of patient characteristics, including age, sex, race, ethnicity, country, and socio- economic group on their symptoms, comparing this to the RIH data set results. The results of these studies will improve our understanding of symptom checker safety, ability to recognize high risk patients and direct them to seek care, and the potential impact on health service use, for a broad range of patients including those with TIA or stroke.

医疗诊断是有效医疗保健的重要组成部分，但误诊、延迟诊断和不正确的分诊 尤其是在紧急或紧急护理环境中很常见，并且是不良临床事件的主要原因。 医疗保健的改善对于解决误诊问题非常重要，患者通过认识到潜在的疾病发挥着关键作用 症状严重并及时寻求护理，这些问题对于需要治疗的患者尤为重要。 可能危及生命的疾病的紧急或急救护理，例如短暂性脑缺血发作 (TIA)、中风或 他们常常无法认识到症状的严重性，并且可能无法及时寻求治疗， 导致中风患者通常必须在 4 小时内接受治疗，从而导致错过治疗和预后较差。 取得了良好的反响，而公共教育活动并没有对医疗智能手机应用程序产生显着帮助。 被称为症状检查器 (SC) 的诊断已广泛应用于美国和世界各地的患者。 我们的工作证据表明，它可供患者使用，并且可以影响患者的决策和寻求护理的行为。 等人已经表明，如果使用得当，SC 可以实现接近医生的诊断和分诊准确性 （依赖于症状数据）但是，大多数研究都是基于医生创建的病例摘要，SC 应用程序不是。 被患者使用，并且缺乏关于患者特征的影响或 SC 对患者决策影响的证据。 为了弥补这些差距，我们将评估领先症状检查器的安全性、可用性、诊断和分类准确性 急诊科或紧急初级保健患者使用的情况，以及 SC 输出对患者决策的影响 在目标 1 中，我们将招募 700 名患者在紧急初级护理时使用 Ada Health 的 SC 应用程序。 罗德岛医院 (RIH) 的护理或急诊室，包括那些可能有 TIA 或中风症状的患者。 这是建立在我们之前对 Ada SC 在这些地点招募的 241 名患者进行的研究的基础上的。 他们打算寻求的护理将在使用 Ada 之前和之后进行评估，以及有关应用程序可用性的调查问卷。 诊断和分诊准确性将与看诊患者的医生的评估以及 Ada 结果进行比较 我们还将与查看 Ada 收集的症状数据的独立医生进行比较和批评。 评估诊断和分诊数据的不同呈现方式对患者决策的影响。 在目标 2 中，我们将利用 2 个独特的数据集，其中包含 2300 名在 RIH 急诊室看到的可能患有 TIA 或中风的患者，并使用 机器学习技术创建新算法以改善早期诊断和风险分层性能。 与现有算法和指南进行比较，以准确诊断和有效管理这些病症。 我们还将分析社区中使用 Ada 应用程序的 158,000 名可能患有 TIA 或中风的患者的数据集 设置，并评估患者特征的影响，包括年龄、性别、种族、民族、国家和社会 经济组对其症状进行比较，将其与 RIH 数据集结果进行比较，这些研究的结果将会有所改善。 我们对症状检查器安全性的理解、识别高风险患者并指导他们寻求护理的能力，以及 对包括 TIA 或中风患者在内的广泛患者的医疗服务使用产生潜在影响。