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: 2241535
• 负责人: Katie Corcoran
• 金额: $ 17.61万
• 依托单位: West Virginia University Research Corporation
• 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=2241535&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.

遗漏、延迟或不准确的医疗诊断(即诊断错误)每年在美国造成多达80,000人在医院死亡。一些疾病不容易诊断，因为症状含糊不清或容易与其他疾病混淆，这导致诊断错误。对于医生可能没有太多经验的罕见疾病来说，情况尤其如此。该项目审查被边缘化的地位(例如，被种族和族裔划分为少数群体的人和妇女)和医患网络的结构是否与延误诊断症状不明确的医疗状况有关。这个项目的发现可以带来更好的医疗干预措施，可以减少与诊断错误相关的健康差距。这个项目确定了对症状不明确的医疗条件延误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，假设被边缘化的人在诊断症状不明确的疾病时更有可能经历更长的延迟，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。借鉴网络科学的理论，这项研究假设，鼓励接触非冗余信息(要考虑的新的可能诊断或测试)的患者-医生网络结构，以及医生更愿意接受错误和纠正错误诊断，与诊断具有模糊症状的医疗条件的时间更快相关。该项目将纵向医疗补助分析摘录(MAX)医疗索赔数据与美国医学会(AMA)医生主文件数据结合起来，以定量测试这些假设。患者-医生网络是通过基于临床接触的患者和医生之间的直接联系以及医生之间通过他们共享的患者的间接联系来构建的。该项目对于了解社交网络如何影响医患关系和诊断流程，以及减少美国医疗保健系统中的诊断错误数量非常重要。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。