This systematic review was performed using the protocols outlined by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) to identify the studies that satisfy the eligibility criteria for subsequent data extraction and synthesis (Multimedia Appendix 1).

A comprehensive search for peer-reviewed studies was carried out using abstracts and titles screening within the PubMed/MEDLINE, Scopus, and Web of Science databases using the search terms in Table 1. The search was conducted on August 29, 2023, without limitations on study design or specific health domains.

The initial search comprised 2 main categories. The first category included a broad set of key terms related to spatial analysis. The second category used the key terms associated with EHR. Henceforth, our reference to EHRs will also encompass electronic medical records (EMRs), electronic patient records (EPRs), enterprise data warehouses (EDWs), and research data warehouses (RDWs). The Boolean operator AND was applied to synthesize the 2 categories.

For PubMed/MEDLINE, Scopus, and Web of Science, we used a consistent search strategy tailored to the specific features and functionalities of each platform. We used the advanced search options available on these databases to input the key terms from Table 1. The search was conducted across titles and abstracts. For Google Scholar, due to its distinct search engine and more limited filtering options compared to the other databases, we conducted broad search queries with the same key terms. We then manually reviewed the results to identify and include relevant studies that met our criteria.

The retrieved abstracts and titles were imported into Covidence systematic review software (Veritas Health Innovation), where duplicate records between original databases are automatically eliminated. Two reviewers (AM and BH) independently assessed the eligibility of the studies based on the following inclusion and exclusion criteria.

The studies were eligible for primary inclusion if they (1) were composed in English; (2) were original peer-reviewed studies; (3) used individual-level patient data derived from EHRs, EMRs, EPRs, EDWs, or RDW; and (4) incorporated at least 1 form of spatial methods. Conversely, the studies were excluded if they (1) were not peer-reviewed (eg, letters, editorials, reviews, case reports, abstracts, and grey literature), (2) solely geocoded addresses or generated basic visualizations (eg, dot map and choropleth map) without any spatial analysis, and (3) not based on the US data.

The reviewers (AM and BH) independently reviewed the full texts of all remaining studies. The studies also were excluded if they lacked phenotype characteristics. Further, we manually checked the references for all the selected studies for possible inclusion. A third reviewer (AVA) was consulted to break ties.

Upon identifying studies that satisfied all inclusion criteria, two reviewers (AM and BH) extracted the following items for each study: title, publication year, country and region, sample size, study period, spatial methodologies, and key findings from the spatial methods. Moreover, studies were assessed to identify clinical domains (including primary and secondary when applicable), health conditions or problems, and themes (including social determinants of health [SDOH], environmental factors, ecological aspects, climate, microbiome, genomics, and clinical phenotypic characteristics). Previous publications have emphasized the importance of data domain sources in phenotyping, underscoring the need for validating the created phenotype [13] and using multiple data sources. Thus, in cases where the included publications did not provide details of data sources but instead referenced previously published works, referenced publications were reviewed. Additionally, we cataloged the types of EHRs that served as the sources.

There is no universally accepted classification for spatial analysis methods. In this review, we have adopted and refined a classification framework based on the study of Nazia et al [14], which initially categorized methods into frequentist and Bayesian approaches and spatial and spatiotemporal methods. This classification was further broken down into descriptive, clustering, and modeling techniques [15]. Therefore, following data extraction, the studies were categorized into the following spatial methodology classifications: descriptive, clustering, modeling (frequentist), spatiotemporal (frequentist), and Bayesian. The phenotype characteristics were extracted and recorded as free text. It should be noted that the categories were not mutually exclusive.

The quality appraisal of the studies was not feasible due to the substantial heterogeneity in spatial methodologies and health domains. The geospatial distribution of the included studies was visualized using ArcGIS Pro software (version 3.0; ESRI).

The initial search yielded 1758 references. After removing duplicate records, we identified 952 studies for abstract and title screening, from which 375 were selected for full-text review. Of these, 322 studies were excluded as they only contained geocoding or basic mapping without any spatial analysis. Additionally, 15 studies were omitted due to the absence of patient phenotype characteristics (n=2) or were not based on US data (n=13). We further manually searched references and Google Scholar and found 11 new studies that met the eligibility criteria. Therefore, 49 studies that fulfilled the inclusion criteria were retained for data extraction and synthesis. Figure 1 depicts the PRISMA flowchart for the study selection process.

Of the 49 included studies, a limited number (n=7, 14%) were published prior to 2017. The earliest study included in this study was published in 2011, and the publication frequency has experienced a significant upsurge since 2017 (n=42, 86%), likely due to increased adoption of EHR systems and growing familiarity with spatial analysis techniques among researchers. There was only one study [16] at the national level. General characteristics of the included studies are presented in Table 2. Most studies were concentrated in North Carolina (n=8, 16%), Pennsylvania (n=6, 12%), California (n=6, 12%), and Illinois (n=4, 8%). Figure 2 illustrates the geospatial distribution of studies at the state level in the United States.

This systematic review is the first comprehensive investigation of spatial methodologies within EHR-derived data in the United States. The findings reveal that a considerable portion of studies predominantly focus on basic mapping or geocoding, with a limited use of advanced spatial analysis methods. Spatial clustering and descriptive analysis were the most used methods, while space-time modeling, either frequentist or Bayesian, was not widely applied. The diverse use of spatial analysis for EHR-derived data in different health domains highlights the potential to incorporate spatial methods to enhance the context of individual patients for future biomedical research. We found limited use of EHR-derived data for spatial analysis, probably due to the challenge of safeguarding patient privacy. Address data, crucial for spatial analysis, is highly confidential and often restricted from sharing. Researchers and institutions often use geographic masking techniques [6,80] to balance data use and privacy protection by altering the precise geographic coordinates while preserving the overall spatial characteristics of data. Encouraging the adoption of spatial analysis could promote biomedical knowledge sharing and collaboration.

The use of EHRs data for spatial analysis can present several challenges, particularly in accurately geocoding patient addresses. Issues, such as address formatting errors, incomplete or outdated addresses, and potential inaccuracies in geocoding services, can influence the outcome of spatial analysis [81]. Advanced geocoding algorithms and manual verification processes can mitigate these issues. For instance, Goldberg et al [82] developed a web-based system for rapid manual intervention of previously geocoded data, significantly improving the match rate and quality of individual geocodes with minimal time and effort. Additionally, when addresses are only available at the zip code level, additional nuances arise as zip code boundaries are often not well-defined and can change over time [83]. Spatial smoothing techniques and zip code centroids can mitigate some of these challenges. We recommend standardizing address formats before geocoding (using tools like the US Postal Service address verification), using advanced geocoding services, leveraging higher-resolution geographical data when possible, and integrating multiple spatial scales to enhance the accuracy and reliability of spatial analysis using EHRs data.

We acknowledge that not all patient phenotypes are inherently suited for spatial analysis, and integrating genomics, imaging, and clinical notes phenotypes can be particularly challenging. However, evidence suggests that spatial techniques can provide valuable insights even in these areas where their application may initially appear challenging. For instance, Baker et al [84] demonstrated the effectiveness of spatial analysis in genomics by combining single-nucleotide polymorphism genotyping with geospatial K-function analysis. Their study of typhoid in Nepal found significant geographic clustering of cases. Canino [85] developed a robust framework that integrated biological data with geographic information from EMRs. Their system identified correlations between patient profiles and geographic factors such as environmental exposures related to pollution. Future interdisciplinary studies can explore developing frameworks that integrate genomics or notes with geospatial datasets to reveal complex relationships and patterns.

The application of spatiotemporal analysis of EHR-derived data was mainly limited to exploring spatiotemporal clusters with no spatiotemporal modeling. This might be due to the technical expertise required for analysis, data complexity, availability of longitudinal data, and computational challenges. The Bayesian framework offers a more adaptable framework to handle complex spatial and temporal dependencies, control confounding variables [86], and incorporate prior information, such as existing medical literature and expert opinions, resulting in more interpretable results [87,88]. Moreover, spatiotemporal Bayesian modeling can aid in understanding disease trends and progressions, seasonality, and long-term shifts at the local levels [89]. Bayesian modeling can also account for uncertainty in parameter estimates and predictions to assess the reliability of findings before implementing interventions [90]. Thus, future research should delve into spatial and spatiotemporal modeling, focusing on Bayesian approaches. Moreover, ignoring spatial dependence in modeling can bias parameter estimates [9,91,92]. Additional state-of-the-art methods, such as space-time autoregressive models and generalized additive models for location scale and shape, also provide flexibility in modeling complex relationships. Spatiotemporal point process models also contribute by analyzing the distribution of health events and underlying states over space and time.

Among the health conditions studied, chronic and infectious diseases emerged as the most frequently investigated domains compared to others. This disparity may be attributed to the pressing public health concerns posed by diseases with immediate impacts that often attract more funding and resources for research initiatives [93,94]. The historically high mortality rates of these conditions likely led to continuous research. Furthermore, the nature of spatial contamination and the spread of infectious diseases has historically driven the development of spatial analysis for clinical purposes, exemplified by John Snow’s seminal cholera investigation. Surprisingly, despite the plethora of funding in cancer research, we only found a small number of studies within the cancer domain, which may likewise be attributed to and indicative of the pressing needs of other domains such as infectious disease.

We observed recurring and prominent themes related to the SDOH. This emphasis may result from the growing maturity and increased awareness within the biomedical informatics community regarding the significant influence of social, economic, and environmental factors on health outcomes. Understanding the roles of SDOH in health disparities will likely lead to the implementation of integrative health interventions that address the needs of individuals affected by these health disparities. These interventions can likewise be enhanced by incorporating spatial perspectives.

Another missed opportunity is the limited use of computable phenotypes—automated algorithms designed for characterizing diseases and enrolling patients in studies. Most studies primarily depended on the manual application of inclusion and exclusion criteria to define phenotypes. While this method may be suitable in certain scenarios, it often necessitates greater depth and granularity to consistently and accurately capture the intended patient cohorts. The accuracy and precision of the manual approach can vary depending on the data sources and clinical domains. Notably, only 2 of the studies in this review used computable phenotypes, indicating a limited adoption of this essential and potentially transformative approach, highlighting a noteworthy area for growth. Furthermore, only 5 studies carried out any form of chart review validation. Validation methods, including chart reviews, genetic markers, and clinical variables, are indispensable in phenotyping to guarantee the accurate characterization of the desired cohorts. This applies even to computable phenotypes within specific medical domains [95].

This study has several main limitations. First, we only considered English-language studies, possibly introducing language bias. Additionally, selection bias is possible due to database availability. However, we mitigated these limitations by searching Google Scholar and conducting backward reference checking to identify relevant studies that might yet be identified through our initial search strategy. Finally, we used a query search strategy with limited keywords, which inherently restricted the scope of studies we could retrieve, potentially omitting studies that did not use these specific terms in their abstract or title.

Our rationale to focus exclusively on US data was driven by our familiarity with the reliability and availability of EHR-based systems within the country. Moreover, we recognize that spatial analyses of health data in regions, such as Europe, Asia, Australia, and Canada, use different terminologies and labels for their systems, which might not align with our search terms for EHRs or EMRs. For instance, Canada’s national administrative databases and electronic discharge records could encompass significant work not captured by our key terms, a situation that can be generalized to other countries. To avoid inconsistencies arising from varying data labeling and storage systems across different regions, we opted to concentrate on the United States. Nevertheless, future research should endeavor to include and explore contributions from these regions to provide a more comprehensive understanding of emerging trends in spatial analysis in characterizing patient phenotypes.

This systematic review provided a comprehensive overview of the current use of spatial analysis in EHR-based research in the United States and underscored the pivotal role that spatial analysis can play in clinical decision support and interventions. The use of EHR-derived spatial analysis is on an upward trajectory, parallel with the widespread adoption of EHR systems. The volume of studies on this topic is anticipated to continue to grow. The primary health outcomes investigated were asthma, hypertension, and diabetes. Notably, patient phenotypes involving genomics, imaging, and notes that are notoriously high-dimensional and add to the computational intensity of spatial methods were limited. This review also highlighted the need for additional exploration of spatial analysis techniques, including but not limited to spatiotemporal Bayesian analysis and modeling, particularly in computable phenotypes or patient phenotypes involving genomics, imaging, and notes.