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: 2241537
• 负责人: Sydney Martinez
• 金额: $ 4.79万
• 依托单位: University of Oklahoma Health Sciences Center
• 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=2241537&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.

错过，延迟或不准确的医学诊断（即诊断错误）每年造成多达80,000例医院死亡。某些医疗状况并不容易诊断，因为症状模棱两可或容易与其他疾病混淆，这会导致诊断错误。对于医生可能没有太多经验的稀有疾病尤其如此。该项目检查是否边缘化状态（例如，大致和种族少数的人和妇女）以及患者 - 医师网络关系的结构与症状模棱两可的诊断状况下的延迟有关。该项目的发现可能会导致更好的医疗干预措施，从而减少与诊断错误有关的健康差异。该项目确定了具有模棱两可症状的诊断状况中延迟的社会和结构预测指标。基于社会心理学的理论，这项研究假设边缘化的人更有可能在诊断症状模棱两可的疾病中经历更长的延迟，因为诊断过程可能会受到种族，性别和其他刻板印象的影响。这项研究借鉴了网络科学的理论，假设有鼓励暴露于非冗余信息的患者 - 身体网络结构（新的可能的诊断或要考虑的测试），以及医生接受错误和正确诊断的愿意增加的意愿与更快的时间有关，与歧义症状的诊断速度更快有关。该项目将纵向医疗补助分析提取物（MAX）医学索赔数据与美国医学协会（AMA）医师大师数据结合在一起，以定量检验这些假设。通过临床遭遇和医生之间通过共享的患者之间的临床遭遇和间接联系，通过患者和医师之间的直接联系来构建患者 - 医生网络。该项目对于理解社交网络如何影响患者的关系和诊断过程很重要，以及减少美国医疗保健系统中诊断错误的数量。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来支持的。