Biomedical Informatics Center, Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, United States.
JMIR Med Inform. 2024 Oct 15;12:e56343. doi: 10.2196/56343.
Electronic health records (EHRs) commonly contain patient addresses that provide valuable data for geocoding and spatial analysis, enabling more comprehensive descriptions of individual patients for clinical purposes. Despite the widespread use of EHRs in clinical decision support and interventions, no systematic review has examined the extent to which spatial analysis is used to characterize patient phenotypes.
This study reviews advanced spatial analyses that used individual-level health data from EHRs within the United States to characterize patient phenotypes.
We systematically evaluated English-language, peer-reviewed studies from the PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases from inception to August 20, 2023, without imposing constraints on study design or specific health domains.
A substantial proportion of studies (>85%) were limited to geocoding or basic mapping without implementing advanced spatial statistical analysis, leaving only 49 studies that met the eligibility criteria. These studies used diverse spatial methods, with a predominant focus on clustering techniques, while spatiotemporal analysis (frequentist and Bayesian) and modeling were less common. A noteworthy surge (n=42, 86%) in publications was observed after 2017. The publications investigated a variety of adult and pediatric clinical areas, including infectious disease, endocrinology, and cardiology, using phenotypes defined over a range of data domains such as demographics, diagnoses, and visits. The primary health outcomes investigated were asthma, hypertension, and diabetes. Notably, patient phenotypes involving genomics, imaging, and notes were limited.
This review underscores the growing interest in spatial analysis of EHR-derived data and highlights knowledge gaps in clinical health, phenotype domains, and spatial methodologies. We suggest that future research should focus on addressing these gaps and harnessing spatial analysis to enhance individual patient contexts and clinical decision support.
电子健康记录（EHR）通常包含患者地址，这些地址为地理编码和空间分析提供了有价值的数据，使我们能够更全面地描述患者的个体情况，从而满足临床需求。尽管 EHR 在临床决策支持和干预中得到了广泛应用，但尚无系统评价研究检查空间分析在多大程度上用于描述患者表型。
本研究回顾了美国使用 EHR 中的个体健康数据进行患者表型特征描述的高级空间分析。
我们系统地评估了从PubMed/MEDLINE、Scopus、Web of Science 和 Google Scholar 数据库中获取的英语同行评审研究，检索时间截至 2023 年 8 月 20 日，对研究设计和特定健康领域均无限制。
超过 85%的研究仅限于地理编码或基本映射，而没有实施高级空间统计分析，仅有 49 项符合入选标准的研究。这些研究使用了各种空间方法，主要侧重于聚类技术，而时空分析（频率论和贝叶斯）和建模则较少。自 2017 年以来，发表的论文数量显著增加（n=42，86%）。这些论文涉及多种成人和儿科临床领域，包括传染病、内分泌学和心脏病学，使用了一系列数据领域（如人口统计学、诊断和就诊）定义的表型。研究的主要健康结局是哮喘、高血压和糖尿病。值得注意的是，涉及基因组学、影像学和病历的患者表型研究有限。
本综述强调了对 EHR 衍生数据进行空间分析的兴趣日益浓厚，并突出了临床健康、表型领域和空间方法学方面的知识差距。我们建议未来的研究应集中于解决这些差距，并利用空间分析来增强个体患者背景和临床决策支持。
