Contextualizing the healthcare analytics captured at the local level is critical to driving desired health outcomes. We’re all familiar with Social Determinants of Health (SDoH), and they should be considered a useful, descriptive way to assess what has already happened. But looking backward isn’t enough to improve future health and control costs.
Projections made because of SDoH are generally based upon subjective interpretations of the data obtained, data that is often used to steer policy initiatives rather than tie social determinants to the underlying connections significantly affecting human health. It’s neither efficient at being predictive, nor is it designed to be. We need to rethink which variables we measure individually and locally across demographics, and integrate those variables into tech models that properly execute this new way of thinking about healthcare.
Optimal health results require a successful transition from descriptive to predictive healthcare analytics. Spatially Health is redefining the approach to capturing, analyzing, and utilizing meaningful data by presenting Spatial Determinants of Health (SpDH) as a necessary layer atop SDoH. In doing so, we can truly evaluate the deterministic relationships responsible for health outcomes in target populations.
We construct the proprietary AI/ML tools needed to model localized, real-world phenomena accurately; this, in turn, allows us to generate transformative healthcare insights across whole communities and in a whole new way.
Spatially Health’s proprietary data models, built specifically for healthcare, present multi-variable, block-by-block data visualizations far more nuanced than simply cataloging zip codes. This fusion of multiple sources of location intelligence is needed to unlock and properly contextualize demographics for optimal healthcare outcomes, revealing the patterns, trends, and localized vulnerabilities affecting target populations.
Learn more about how spatial analysis drives innovative solutions in healthcare.
Spatially Health’s proprietary tool for risk-based healthcare applications. The Spatial Risk Score operates as a metric built upon SpDH. It can be segmented so that specific conditions or sets of conditions are isolated in order to correlate with specific problems based upon client need (such as disease mapping or highlighting comorbidities within targeted populations).
The Spatial Risk Score can be thought of as a subset of SpDH, where an unique data visualization tool that’s built logically and scales dynamically is required to optimize forecasting risk.
Spatial Determinants of Health present a logical evolution of SDoH, a way of removing ambiguity to better reveal certainty. The integration of spatial analytics opens the door to more comprehensive modeling: by measuring the relationships and underlying connections between social determinants and health, SDoH then becomes substantial in a way that can help drive desired outcomes.
Alongside a new way of thinking about the construction of demographic models, Spatial Determinants of Health also incorporate far more variables in its calculations, and at increasingly granular levels powered by machine learning. The result is transformative. Identifying, describing and quantifying real world phenomena is now possible, and the ability to generate insight and apply it in the context of human personas becomes real.
