Viewing Healthcare Through A Geographic Lens

The U.S. healthcare system is a study in complexity. The best medical treatments on the planet exist within a complicated medical marketplace. The most cutting-edge technology is often tethered to heavily siloed patient data that can't be accessed or shared, and the spatial and social determinants shaping our local behaviors — by far the greatest contributors to our overall well-being — receive far less attention than the clinical and financial components accompanying them. How to transform the U.S. healthcare ecosystem to benefit all stakeholders is key for reform.

Enter Haven Healthcare, a collaboration between Berkshire Hathaway, JPMorgan and Amazon that began in January 2018 to reinvent employer-based insurance and address the high costs of healthcare for more than 1 million employees scattered across the country. It's the perfect example of the growing need for location-based intelligence to become fully integrated into the mindset of how we view healthcare solutions and successes.


The healthcare industry watched to see how the joint venture would work through the complexities of healthcare and transform the system. Will they get it right? And will this be the viable model for the rest of the country? Yet after just 36 months of existence, Haven ceased operations just as 2021 began.

No Safe Haven

Much of the press that showcased Haven's demise painted the nonprofit with a broad brush of naiveté about taking on the complex and inefficient Goliath that is U.S. healthcare. However, despite Haven's failure to exist, an important truth emerged from the experiment: Geography and local health-related behaviors pose significant challenges yet also play a critical role in the solution to ensure successful outcomes.


Healthcare stakeholders must understand the "geo-significance" of the relationships, patterns and trends present within a local market that drives costs and risks for that population.

Did Haven Really Fail? 

Haven did not shut down due to a lack of vision. Haven was doomed from the beginning because it wasn't able to bridge what should happen and what could realistically be changed.

It's a recurring problem in healthcare today, where programming and strategy are misaligned. A business-forward strategy that is calibrated for financial success often doesn't align with community-wide health-related needs. Strategies often only address the last mile of the healthcare journey rather than the first mile, where changes stand to make the most impact.

These complex problems demand a new way of thinking about solutions. That new way of thinking increasingly emanates from understanding how much geography affects healthcare in all respects.

By integrating the data analytics that spatial intelligence provides, all of the health characteristics at play at the local level are clearly identified and contextualized. Understanding how these different characteristics affect one another can furnish us with the knowledge that works in a predictive and preventive context. Health outcomes improve. Costs go down. Everybody wins.

Local Data Will Drive Us Toward Solutions

Healthcare in the U.S. is often defined as an interlocking system of payers and providers, hospitals and health systems, specialized facilities, and institutes that revolve around the needs of patients and members. However, the inefficiencies, lack of transparency and difficulty navigating between these systems restrict healthcare from acting like a cohesive, efficient whole. It is within this construct that we look to evaluate or develop new programs. It's also where any hopes of developing something innovative often quickly fizzle out. 

Just as Haven appears to have hit the geographic walls of U.S. healthcare being "market to market," we must take note of how aspects of that geography continue to arc. The Haven project didn't work because the companies' employees were scattered across the U.S., yet the collaborative's goals were centered around unifying costs, streamlining access and delivering innovative solutions. Location and the resulting relationships were some of the biggest challenges it faced, and it didn't possess the tools, visibility or capability to work through it.

Overcoming these challenges could've made all the difference. Looking at 814 ZIP codes in Florida, for example, we tested the impact of more than 50 contributing factors to diabetes rates in the working-class population using spatial intelligence tools. Each of these factors was tied to a geographic location. We found that areas with more blue-collar workers had a higher rate of diabetes. Populations living in areas close to convenience stores — which have a higher ratio of unhealthy food to healthy food — showed a consistently elevated risk of diabetes, whereas other areas near gyms and with access to pharmacies showed a significant decrease in the prevalence of diabetes.

Considering the diverse employee group at Haven, as well as most other companies, what works in one area for blue-collar workers likely won't work in another area made up of mostly white-collar workers.

This example drives home the point that before jumping in with expensive implementations that provide marginal improvements while wasting precious resources, we should frame problems correctly using available data tied to geography to guide us toward viable solutions.

When you start looking at healthcare through a geographic lens, you are able to collect relevant data that helps explain different healthcare structures and their consequences. Using clinical data, location data and spatial modeling, alternative healthcare delivery options can be uncovered and implemented, and we believe cost savings between 17% and 19% can be realized.

Lessons Learned From Haven

In the end, it's likely that Haven's three-year experiment will drive healthcare stakeholders to take a closer look at how geography plays a significant role in U.S. healthcare markets. Location-based visualizations and predictive modeling based on data will be used to reveal how to optimize healthcare access and delivery and how to best evaluate and mitigate risk.

This shifting paradigm must be able to use data to identify the factors at play, to measure those factors and how they influence each other, and to gauge the outcomes that exist in a given location.

"Healthcare is local" will help define this new paradigm, one that demands new thinking and new tools to connect the dots from point-of-care access to the organizational level and back again.


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