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.

医学诊断是有效医疗保健的关键组成部分，但诊断延迟，诊断延迟和分类不正确 特别是在紧急或紧急护理环境中常见，也是不良临床事件的主要贡献者。尽管 医疗保健的改善对于解决诊断的问题很重要，患者可以通过认识到潜在的关键作用 严肃的象征和及时寻求照顾。这些问题对于需要的患者特别重要 紧急或紧急护理，可能威胁生命的疾病，例如短暂的缺血性攻击（TIA），中风或 心肌梗塞。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护理， 导致缺失的治疗和较差的结果。通常必须在4小时内治疗中风的患者 取得良好的回应，公共教育运动并没有重大帮助。医疗智能手机应用 诊断为症状检查器（SC）广泛可供美国和全球患者使用。他们曾经 证明是患者可用的，可能会影响患者的决策和护理寻求行为。我们工作的证据 其他人则表明，如果正确使用，SC可以实现诊断的准确性，而分类可以接近医生 （依靠症状数据）。但是，大多数研究是基于医生创建的案例摘要，SC应用不是 患者使用，缺乏证据表明患者特征的影响或SC对患者决策的影响。 为了解决这些差距，我们将评估领先症状检查器的安全性，可用性，诊断和分流的准确性 患者在急诊科或紧急初级保健中使用，以及SC产出对患者决定的影响 寻求护理。在AIM 1中，我们将招募700名患者使用ADA Health的SC应用 护理或罗德岛医院（RIH）的紧急部门，包括可能出现TIA或中风症状的部门。 这是我们以前对ADA SC的研究，在这些位置招募的241名患者。紧迫程度 他们打算寻求的关心将在使用ADA之前和之后评估有关应用程序可用性的问卷。 诊断和分类准确性将与看过患者的物理学的评估和ADA结果相比 与查看ADA收集的症状数据的独立医生相比，均与至关重要的相比。我们也会 评估诊断和分类数据不同表现对患者决策的影响。 在AIM 2中，我们将利用2个独特的数据集，其中包括2300例可能在RIH的ED中看到的TIA或中风的患者，并使用 机器学习技术可创建新算法以改善早期诊断和风险分层。性能 与现有的有关这些疾病的准确诊断和有效管理的算法和指南进行比较。 我们还将分析一个在社区中使用ADA应用的158,000例TIA或中风患者的数据集 设置和评估患者特征的影响，包括年龄，性别，种族，种族，国家和社会 - 经济群体关于其症状，将其与RIH数据集进行比较。这些研究的结果将改善 我们对症状检查器安全的理解，能够识别高风险患者并指示他们寻求护理的能力，以及 对卫生服务使用的潜在影响，包括包括TIA或中风的患者在内的广泛患者。