Characterizing Patient Phenotypes and Emerging Trends in Application of Spatial Analysis in Individual-Level Health Data

Abstract

Objectives: Health information systems (HIS) commonly contain patient addresses that provide valuable data for geocoding and spatial analysis, enabling more comprehensive descriptions of individual patients in biomedical studies. Despite the widespread adoption of HIS, no systematic review has examined the extent to which spatial analysis is used in characterizing patient phenotypes. Materials and Methods: We systematically evaluated English-language peer-reviewed articles from PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases from inception to August 20, 2023, without imposing constraints on time, geography, or specific health domains. Results: Only 62 articles met the eligibility criteria. These articles utilized diverse spatial methods, with a predominant focus on clustering techniques, while spatiotemporal analysis (frequentist and Bayesian) and modeling were relatively underexplored. Geographically, the use was limited, involving only nine countries, with over 80% of studies conducted in the United States. Moreover, a noteworthy surge (82.3%) in publications was observed post-2017. The publications investigated various clinical areas, including infectious disease, endocrinology, and cardiology, using phenotypes defined over a range of data domains, such as demographics, diagnoses, and visit. The primary health outcomes investigated were asthma, hypertension, and diabetes. Notably, patient phenotypes involving genomics, imaging, and notes were rarely utilized. Discussion and Conclusion: This review underscores the growing interest in spatial analysis of HIS-derived data and highlights knowledge gaps in clinical health, phenotype domains, geospatial distribution, and spatial methodologies. Additionally, this review proposes guidelines for harnessing the potential of spatial analysis to enhance the context of individual patients for future biomedical research.

Identifying Long COVID Definitions, Predictors, and Risk Factors using Electronic Health Records: A Scoping Review

“…Electronic health records (EHR) can provide a reliable, cost-effective, and comprehensive overview of the medical histories of a large population of patients, including previous infections and pre-existing conditions, thus enabling the tracking of symptoms and conditions [20]. Within EHR systems, a valuable resource for obtaining disease etiology is the International Classi cation of Diseases, Tenth Revision (ICD-10), used to code and classify all symptoms, procedures, and diagnoses [21].…”