e.g. mhealth
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Because EHRs often contain patient addresses, spatial analysis can enable value addition via high-resolution geocoding. The simplest of such analyses may be mapping, which can promote a better understanding of health disparities. Further, patient geocoding can link external data such as environmental, demographic, and socioeconomic factors for more refined patient phenotyping and a more profound understanding of patient exposures for targeted interventions [3].
JMIR Med Inform 2024;12:e56343
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The middle box shows the geocoding and R computing happening within Docker. The top box shows the internet layer where ACMT downloads public data sets (eg, ACS) data from but ACMT would not upload sensitive information to the internet. The geocoding and context measure computing only happens within the docker container which is on the user’s local computer. In a typical workflow, from the RStudio/RShiny web interface, a user sends R codes to Docker.
JMIR Form Res 2024;8:e56510
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Geocoding a patient’s address (ie, converting to geographic coordinates) is often an intermediate step in secondary data analyses; it is either used to link the patient to external geographic units (eg, census tracts to obtain neighborhood socioeconomic status from the United States Census Bureau) or to calculate distance from other entities, such as health care providers and facilities.
Online J Public Health Inform 2024;16:e54958
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