Markatou Marianthi, Kennedy Oliver, Brachmann Michael, Mukhopadhyay Raktim, Dharia Arpan, Talal Andrew H
Department of Biostatistics (CDSE Program), University at Buffalo, Buffalo, NY, United States.
Department of Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, United States.
Front Med (Lausanne). 2023 Mar 2;10:1076794. doi: 10.3389/fmed.2023.1076794. eCollection 2023.
Deriving social determinants of health from underserved populations is an important step in the process of improving the well-being of these populations and in driving policy improvements to facilitate positive change in health outcomes. Collection, integration, and effective use of clinical data for this purpose presents a variety of specific challenges. We assert that combining expertise from three distinct domains, specifically, medical, statistical, and computer and data science can be applied along with provenance-aware, self-documenting workflow tools. This combination permits data integration and facilitates the creation of reproducible workflows and usable (reproducible) results from the sensitive and disparate sources of clinical data that exist for underserved populations.
从服务不足人群中推导健康的社会决定因素是改善这些人群福祉以及推动政策改进以促进健康结果积极变化过程中的重要一步。为此目的收集、整合和有效使用临床数据存在各种具体挑战。我们断言,将医学、统计学以及计算机与数据科学这三个不同领域的专业知识结合起来,并与具有出处感知、自我记录功能的工作流程工具一起应用。这种结合允许进行数据整合,并有助于从服务不足人群所存在的敏感且不同的临床数据源创建可重复的工作流程和可用(可重复)的结果。