Collaborative Research: HNDS-R Networks and Health Disparities in Delays in Diagnosis of Medical Conditions with Ambiguous Symptoms

合作研究：HNDS-R 网络和症状不明确的医疗状况诊断延迟造成的健康差异

• 批准号: 2241536
• 负责人: Diego Leal Castro
• 金额: $ 17.97万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2241536&HistoricalAwards=false
• 关键词: Collaborative; Research; HNDS; Networks; Health

Missed, delayed, or inaccurate medical diagnoses (i.e., diagnostic errors) are responsible for up to 80,000 hospital deaths in the United States annually. Some medical conditions are not easy to diagnose because symptoms are ambiguous or easy to confuse with other conditions, which contributes to diagnostic errors. This is especially the case for rare diseases with which physicians may not have much experience. This project examines whether marginalized status (e.g., racially and ethnically minoritized people and women) and the structure of patient-physician network ties are associated with delays in diagnosis of medical conditions with ambiguous symptoms. The findings from this project can lead to better healthcare interventions that could reduce health disparities related to diagnostic errors.This project determines the social and structural predictors of delays in diagnosis of medical conditions with ambiguous symptoms. Drawing on theories from social psychology, this study hypothesizes that marginalized people are more likely to experience longer delays in diagnosis of diseases with ambiguous symptoms because the diagnostic process can be affected by racial, gender, and other stereotypes. Drawing on theories from network science, this study hypothesizes that patient-physician network structures that encourage exposure to non-redundant information (new possible diagnoses or tests to consider), together with physicians’ increased willingness to accept mistakes and correct misdiagnoses are associated with faster times to diagnoses of medical conditions with ambiguous symptoms. The project combines Longitudinal Medicaid Analytic eXtract (MAX) medical claims data with the American Medical Association (AMA) Physician Masterfile data to quantitatively test these hypotheses. Patient-physician networks are constructed through direct links between patients and physicians based on clinical encounters and indirect links between physicians through the patients they share. This project is important for understanding how social networks influence the patient-physician relationship and diagnostic processes, and for reducing the number of diagnostic errors in the U.S. healthcare system.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在美国，漏诊、延迟或不准确的医疗诊断（即诊断错误）每年导致多达 80,000 例医院死亡。一些医疗状况不易诊断，因为症状不明确或容易与其他状况混淆，这也是造成这种情况的原因之一。对于诊断错误尤其如此，医生预计可能没有太多经验，这会检查是否处于边缘化状态（例如，种族和民族少数群体和妇女）和结构。患者与医生的网络联系与症状不明确的医疗状况的诊断延迟有关。该项目的研究结果可以带来更好的医疗干预措施，从而减少与诊断错误相关的健康差异。该项目确定了延迟诊断的社会和结构预测因素。这项研究借鉴了社会心理学的理论，认为孤独的人更有可能在诊断症状不明确的疾病时经历更长的延迟，因为诊断过程可能受到种族、性别和其他刻板印象的影响。 .借鉴理论根据网络科学，这项研究开创了鼓励接触非冗余信息（需要考虑的新的可能诊断或测试）的患者-医生网络结构，以及医生接受错误和纠正误诊的意愿增强，与更快的诊断时间相关该项目将纵向医疗补助分析提取 (MAX) 医疗索赔数据与美国医学会 (AMA) 医师主文件数据相结合，以定量检验这些假设。网络是通过患者和医生之间基于临床接触的直接联系以及医生之间通过他们共享的患者建立的间接联系构建的。该项目对于理解社交网络如何影响患者与医生的关系和诊断过程以及减少社交网络的数量非常重要。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。