Social Determinants of Health Patterns Determined by Satellite Imagery and Data Analytics.

Advances in AI technology gain traction as healthcare providers attempt to make their services more equitable, accessible, patient-centered, and cost-effective. Increased computer power and storage capacity are ushering in a new era of AI-driven health care.

The Emergence of Geospatial Artificial Intelligence

The combination of artificial intelligence (AI), machine learning, data mining and Geographic Information System (GIS), Remote Sensing (RS), and Global Positioning System (GPS) creates Geospatial Artificial Intelligence (Geo AI). Geo AI is multidisciplinary, connecting several scientific domains. Its development stems in part from its applicability to solving real-world problems where location has a crucial role.??

Geo AI technology is fast becoming a powerful domain within healthcare analytics to connect where patients live, work, travel, and other location-specific information to actionable insights on health risks and the types of services that can mitigate these risks and improve health outcomes.

Identifying Social Determinants of Health?

Socioeconomic, behavioral and environmental drivers of health, collectively called Social Determinants of Health (SDOH), are now recognized as a critical contributor to poor health outcomes and disparities. These determinants include food insecurity, inadequate transportation options, homelessness, social isolation, education, and job opportunities. The link between these SDOH factors and the location where an individual was born, lives, or works is now well-established and any attempt to improve the health of our communities and reduce health inequities must incorporate location and address data into the overall analysis of both health risks and impactful care services allocation.??

Researchers use environmental data to assess several Social Determinants of Health (SDoH) issues such as health vulnerability, food security, and public transit. Geospatial AI analyzes such data, shedding light on the root reasons for persistent disparities in health outcomes amongst individuals and populations. We identify and report patterns in utilization to proactively address risks and barriers to care.

Extracting Meaningful SDOH Information from Health Records

Electronic Health Records (EHRs) are the primary tool for collecting, storing, and managing patient health information. There is, however, no defined structure for storing SDOH information, and even the most well-designed documentation tools and processes for tracking SDOH data are usually inadequate. Using Natural Language Processing (NLP), unstructured SDOH data within provider and care manager notes can be analyzed and extracted and can greatly improve the completeness and accuracy of SDOH data capture and analytics. Further collaboration with local information networks, such as community-information systems and public open-source records will help increase SDOH data collection completeness and accuracy and lead to enhanced patient-centered care services.?

Geospatial SDoH Data Analytics: The next step toward maximizing the impact of SDOH data is to add location data for patients into the mix and provide a platform that combines SDOH data, medical data, and patient location information to understand the geographical variability in clinical and social risk factors.

Foresight Health’s geospatial health analytics has the potential to transform patient care by tailoring patient-centered interventions that take into account a patient's service history, mapping valuable information about the social environment in which they live and work, identifying community-level barriers they face, or measuring the patient's distance to the nearest healthcare center and/or food distribution center. We use color-coded maps to identify SDoH requirements in relation to service usage or quality indicators, allowing you to visualize and understand the impact of SDoH factors on health and their implications for communities, populations, and business performance. We assist in recognizing trends in SDoH need distribution, forecasting future risk and health outcomes, and enabling equal resource allocation to (disadvantaged) populations, with the ultimate goal of uncovering and addressing disparities.

Foresight Health Solutions’ Mission through Geospatial Integration?

This strategy will yield an enhanced identification of patient risks to deliver evidence-based actionable insights at the geographic level. When appropriate performance indicators and metrics are in place, users can map care quality, outcomes, and care performance among many various stakeholders. This promotes improved care management and coordination at both the individual and population level across the care continuum.?

The vision of Foresight Health Solutions is to provide the best AI-powered analytic solutions to predict, promote, and preserve optimal health for vulnerable groups. We aim to enable community-based care as the initial point of care for people, especially those with the burden of socioeconomic health factors.

We are the driving force behind mission-driven, goal-oriented, value-based care.?

Justin Jones, the COO of Foresight Health Solutions; is a senior IT leader, strategist, and software engineer with 20+ years leading large organizations. As an entrepreneur, he is passionate about working with teams to develop innovative products that promote a future of ubiquitous computing, intelligent automation, decentralized services, and increased privacy.