Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY, 10029, United States, 1 2122416500.
Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
JMIR Med Inform. 2024 Oct 30;12:e57124. doi: 10.2196/57124.
Social determinants of health (SDOH) are critical drivers of health disparities and patient outcomes. However, accessing and collecting patient-level SDOH data can be operationally challenging in the emergency department (ED) clinical setting, requiring innovative approaches.
This scoping review examines the potential of AI and data science for modeling, extraction, and incorporation of SDOH data specifically within EDs, further identifying areas for advancement and investigation.
We conducted a standardized search for studies published between 2015 and 2022, across Medline (Ovid), Embase (Ovid), CINAHL, Web of Science, and ERIC databases. We focused on identifying studies using AI or data science related to SDOH within emergency care contexts or conditions. Two specialized reviewers in emergency medicine (EM) and clinical informatics independently assessed each article, resolving discrepancies through iterative reviews and discussion. We then extracted data covering study details, methodologies, patient demographics, care settings, and principal outcomes.
Of the 1047 studies screened, 26 met the inclusion criteria. Notably, 9 out of 26 (35%) studies were solely concentrated on ED patients. Conditions studied spanned broad EM complaints and included sepsis, acute myocardial infarction, and asthma. The majority of studies (n=16) explored multiple SDOH domains, with homelessness/housing insecurity and neighborhood/built environment predominating. Machine learning (ML) techniques were used in 23 of 26 studies, with natural language processing (NLP) being the most commonly used approach (n=11). Rule-based NLP (n=5), deep learning (n=2), and pattern matching (n=4) were the most commonly used NLP techniques. NLP models in the reviewed studies displayed significant predictive performance with outcomes, with F1-scores ranging between 0.40 and 0.75 and specificities nearing 95.9%.
Although in its infancy, the convergence of AI and data science techniques, especially ML and NLP, with SDOH in EM offers transformative possibilities for better usage and integration of social data into clinical care and research. With a significant focus on the ED and notable NLP model performance, there is an imperative to standardize SDOH data collection, refine algorithms for diverse patient groups, and champion interdisciplinary synergies. These efforts aim to harness SDOH data optimally, enhancing patient care and mitigating health disparities. Our research underscores the vital need for continued investigation in this domain.
社会决定因素健康（SDOH）是健康差距和患者结果的关键驱动因素。然而，在急诊部（ED）临床环境中，获取和收集患者层面的 SDOH 数据在操作上具有挑战性，需要创新的方法。
本范围综述探讨了人工智能和数据科学在 ED 内建模、提取和纳入 SDOH 数据的潜力，进一步确定了需要改进和研究的领域。
我们对 2015 年至 2022 年期间在 Medline（Ovid）、Embase（Ovid）、CINAHL、Web of Science 和 ERIC 数据库中发表的研究进行了标准化搜索。我们专注于确定使用人工智能或数据科学与紧急护理环境或条件下的 SDOH 相关的研究。两名专门从事急诊医学（EM）和临床信息学的审查员独立评估每篇文章，通过迭代审查和讨论解决分歧。然后，我们提取了涵盖研究细节、方法、患者人口统计学、护理环境和主要结果的数据。
在筛选出的 1047 篇论文中，有 26 篇符合纳入标准。值得注意的是，26 篇研究中有 9 篇（35%）专门针对 ED 患者。研究的病症涵盖了广泛的 EM 投诉，包括败血症、急性心肌梗死和哮喘。大多数研究（n=16）探索了多个 SDOH 领域，其中以无家可归/住房不安全和邻里/建筑环境为主。26 项研究中有 23 项使用了机器学习（ML）技术，其中自然语言处理（NLP）是最常用的方法（n=11）。在综述研究中使用的 NLP 技术包括基于规则的 NLP（n=5）、深度学习（n=2）和模式匹配（n=4）。NLP 模型在研究中的预测性能显著，其 F1 分数在 0.40 到 0.75 之间，特异性接近 95.9%。
尽管还处于起步阶段，但人工智能和数据科学技术，特别是 ML 和 NLP，与 EM 中的 SDOH 相结合，为更好地将社会数据应用于临床护理和研究提供了变革性的可能性。鉴于 ED 的显著重点和显著的 NLP 模型性能，有必要标准化 SDOH 数据的收集，为不同的患者群体改进算法，并倡导跨学科协同作用。这些努力旨在优化 SDOH 数据的使用，改善患者护理并减轻健康差距。我们的研究强调了在这一领域继续进行研究的重要性。
