Contextualizing the healthcare analytics captured at the local level is key 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 simply isn’t enough if our aim is 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 not 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 the 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 accurately model localized, real-world phenomena; this, in turn, allows us to generate transformative healthcare insights across whole communities, and in a whole new way.
Built specifically for healthcare, Spatially Health’s proprietary data models present multi-variable, block-by-block data visualizations far more nuanced than simply cataloguing zip codes. This fusion of multiple sources of location intelligence is the needed unlock to properly contextualize demographics for optimal healthcare outcomes, revealing the patterns, trends and localized vulnerabilities affecting target populations.
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The CDC defines Social Determinants of Health as “[the] conditions in the places where people live, learn, work, and play that affect a wide range of health and quality of life risks and outcomes.” The WHO uses similar language, but goes a bit further, stating, “these circumstances are shaped by the distribution of money, power, and resources at global, national, and local levels,” and contends that social determinants are in fact mostly responsible for health inequities across societies and between countries.
This is a good place to start.
SDoH is a necessary foundational tool, albeit one loaded with ambiguity. Interpreting the data is subjective, and people are often catalogued as demographics or variables that correlate heavily with policy decision-making — in other words, its limited applications do not explain health outcomes.
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.
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.
The comprehensive (and growing) list of variables used in assessing risk, calculating vulnerabilities and predicting outcomes. Built upon bedrock socio-demographics found in SDoH, the Spatial Index adds contextual layers of relevant data found at the local level. This fusion of multiple sources of location intelligence is key to providing the scalable insight needed to drive desired results.