Fri, 08/16/2019

One of my daughter’s favorite movies to watch (and re-watch) right now is “Alpha”.  Set in Europe 20,000 years ago, it illustrates the similarities between social determinants of health (SDoH) then and now. The story is about a young man who, separated from his tribe, struggles to return home.

The common themes of community, opportunity, spirituality, environment, shelter, nourishment, and medicine in the movie play an important role in our health now and always have over the years.  While today we may not worry about being snatched in the night by a sabre tooth tiger, the probability of encountering an intoxicated evening driver in a large urban environment can be just as harrowing.  

The Struggle

In modern healthcare, there is little doubt that SDoH are important. However, one of the greatest challenges to understanding the extent and relevance of the impact is in the collection of associated data.  Consistently gathering these data in an efficient and standardized approach is difficult to achieve. As my colleague Santosh Padhiari recently pointed out in his blog, our Omni-HealthData (OHD) platform has helped customers address this. There are several subjects defined in OHD across different domains that capture key SDoH elements, and the platform has a set of subjects specifically designed to capture surveys and assessments, such as PHQ-9 or BIMS. This tremendously expedites the process of systematically capturing and correlating SDoH factors with the many other data in healthcare provider and payer domains.

An Eye in the Sky

OHD also provides an immediate way to view SDoH through geospatial intelligence. Because OHD validates every address that it integrates and then enriches it with longitude and latitude, our customers can easily see their patients, members, clinics and facilities in the context of location with many detailed SDoH overlays relevant to their proximity.

Imagine the insights that can be gained from being able to look your own populations and cohorts through a readily accessible lens of factors such as:

  • Socio-economics/neighborhood fabric
  • Average healthcare spend
  • Percent with Access to Exercise Opportunities
  • Percent – Limited Access to Healthy Foods
  • Average Daily Particulate Matter
  • People per Primary Physician
  • Percent Receiving Mammograms
  • Preventable Hospital Stays Rate 

In part 2 of An Eye in the Sky, we’ll look at examples of how this works, and in part 3, we will explore the usefulness of geospatial analytics in the framework of traditional visuals and predictive model integration.  Until then, keep an eye out for sabre toothed evening drivers!