Using Geographic Information Systems (GIS) to Identify Communities in Need of Health Insurance Outreach: An OCHIN Practice-based Research Network (PBRN) Report

Abstract

Background: Our. practice-based research network (PBRN) is conducting an outreach intervention to increase health insurance coverage for patients seen in the network. To assist with outreach site selection, we sought an understandable way to use electronic health record (EHR) data to locate uninsured patients.Methods: Health insurance information was displayed within a web-based mapping platform to demonstrate the feasibility of using geographic information systems (GIS) to visualize EHR data. This study used EHR data from 52 clinics in the OCHIN PBRN. We included cross-sectional coverage data for patients aged 0 to 64 years with at least 1 visit to a study clinic during 2011 (n ‫؍‬ 228,284).Results: Our PBRN was successful in using GIS to identify intervention sites. Through use of the maps, we found geographic variation in insurance rates of patients seeking care in OCHIN PBRN clinics. Insurance rates also varied by age: The percentage of adults without insurance ranged from 13.2% to 86.8%; rates of children lacking insurance ranged from 1.1% to 71.7%. GIS also showed some areas of households with median incomes that had low insurance rates.Discussion: EHR data can be imported into a web-based GIS mapping tool to visualize patient information. Using EHR data, we were able to observe smaller areas than could be seen using only publicly available data. Using this information, we identified appropriate OCHIN PBRN clinics for dissemination of an EHR-based insurance outreach intervention. GIS could also be used by clinics to visualize other patient-level characteristics to target clinic outreach efforts or interventions. (J Am Board Fam Med 2014;27:804 -810.)

Geographic and area-level socioeconomic variation in cardiometabolic risk factor distribution: a systematic review of the literature

“…Geocoding and mapping of existing large population based datasets has become feasible with newer computational tools through linking location data; such as map co-ordinates, addresses or postcodes [42]. These tools have the capacity to visually display area based factors, in contrast with traditional table and graph methods, and this has the potential to enhance impact on subsequent area level health care policy development and resource allocation [43][44][45]. In addition, systematic quantitative analyses are possible with these spatial tools which create opportunities to investigate the role of environmental factors in explaining any geographic aggregations beyond random effects [46].…”

The pharmacist's role in overdose: Using mapping technologies to analyze naloxone and pharmacy distribution

“…GIS mapping visually represents geographic data, such as the distribution of certain events across ZIP Code Tabulation Areas (ZCTAs) and census tracts. [16][17][18] Visual indicators can be used to show location, distribution, and volume of these events across a geographic area. Historically, health care researchers have used GIS to understand the distribution of disease; however, as this technology has evolved and become accessible, its uses have extended to mapping the location of health care resources, health insurance coverage, and overdose interventions, such as pharmacies that distribute naloxone, provide drug drop-off boxes, and host drug take-back events.…”

“…Historically, health care researchers have used GIS to understand the distribution of disease; however, as this technology has evolved and become accessible, its uses have extended to mapping the location of health care resources, health insurance coverage, and overdose interventions, such as pharmacies that distribute naloxone, provide drug drop-off boxes, and host drug take-back events. 16,[18][19][20] Additionally, GIS mapping has been used to track patterns and clustering of prescription opioid abuse and model the impact of individual and place factors on methadone treatment outcomes. 21,22 Researchers can also layer multiple events in a single geographic area on one map and thereby analyze the geographic interaction of one or more events of interest.…”

Progress towards using community context with clinical data in primary care

“…Community-level factors and clinical EHR data were combined to identify areas with high uninsurance rates to identify where an outreach programme could be implemented to increase health insurance coverage for patients seen in a practice-based research network of safety net clinics 40. Geraghty et al , demonstrated how community-level and EHR data can be used together to understand and improve chronic disease management for primary care clinics 41.…”