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Predicting the Unpredictable: The Power of Geospatial Data in Healthcare Planning

June 9, 2020
Evan Barry

Researchers from CDC already are using SafeGraph data to understand the impact of COVID-19. Here’s how the healthcare industry can further use geospatial data for demand planning and resource allocation.

The unforeseen COVID-19 pandemic has placed a major strain on healthcare systems and providers across the globe. Healthcare clinics, hospital systems, medical device producers, and pharmaceutical companies alike are facing a new reality, where their services have become more essential though simultaneously less predictable. 

Given the uncertain nature of the virus, healthcare systems have needed to make numerous adjustments on a daily basis. In some cases, this has involved canceling elective procedures and restricting medical facilities to essential procedures. Even when these procedures have been reinstated, patients have been unwilling to bear the risk of entering a healthcare facility prone to viral spread. A Washington Post study found that healthcare providers have been devastated by COVID-19, as hospital visits have plummeted and revenue from elective procedures has dried up.

Washington Post found that foot traffic to hospitals, as measured via SafeGraph Patterns, is down across the country.

How Can Geospatial Data Assist With Healthcare Planning?

The common theme in this pandemic has been unpredictability. How does a hospital system know which facilities to open, and when to open them? How can a medical device manufacturer foresee their production and supply chain needs, when procedure rates change every week? To answer these questions, healthcare organizations can benefit from using data sources to strategically plan for future demand and allocate resources efficiently. 

Especially moving into the recovery process and inevitable later waves of the virus, organizations that leverage alternative data sources like geospatial data will be best-prepared to tackle the pandemic’s unpredictable challenges. As such, data will play a key role in shaping healthcare resource allocation, dynamic decision-making, and demand planning as healthcare organizations continue to serve their part in tackling the virus. 

Resource Allocation - Predicting the Unpredictable

The first step to effective healthcare planning involves knowing when to allocate resources and how much to allocate at any given time. Healthcare companies have suffered financial losses from improperly investing in real estate, manufacturing, and human capital that has proved unnecessary in the pandemic-induced healthcare slump.

To effectively plan and distribute capital, healthcare organizations must be able to anticipate changes in patient volume. This involves analyzing patient trends to understand how quickly patients are returning to hospitals and where these trends are most prevalent. Healthcare providers and hospital systems can use foot-traffic counts and Point-of-Interest (POI) data for this. By understanding which areas are seeing a faster resurgence in patient populations, these organizations can determine where to reopen facilities and how to efficiently distribute staff and equipment. 

Hospital Foot Traffic from the Re-Opening The Economy Dashboard

A similar use case applies for medical device and pharmaceutical companies, who can use data like SafeGraph’s Neighborhood Patterns data to determine where to allocate their sales force as economies begin to open. Finally, some larger companies may wish to create internal risk dashboards to guide Business Development and Risk initiatives. To accomplish all of this, however, they need rigorous raw data that can inform their own models. This is where data like SafeGraph’s anonymized mobile location data steps in - helping provide a clean, easy-to-use dataset that can fit into any company’s internal business planning.

Dynamic Decision-Making - No Two States Are the Same

No two regions are the same when it comes to their pandemic response. The impact of COVID-19 on a given region is largely informed by state policies, population density, and general compliance with social distancing. As such, a national healthcare organization may deem reopening safe in some areas and unsafe in others. To accomplish this, data like SafeGraph’s Social Distancing Metrics can help identify areas of a potential outbreak where extra precaution is necessary. Similarly, this data can highlight regions where stay-at-home orders are effective. The CDC’s Morbidity and Mortality Weekly Report (MMWR) incorporated research using SafeGraph data, finding that stay-at-home orders were effective in cities like NYC, SF, Seattle, and New Orleans.

CDC used SafeGraph Social Distancing Data to measure stay-at-home compliance
Percentage of households staying-at-home in NYC as measured by SafeGraph social distancing data.

Beyond the pandemic, this data can continue to be leveraged for general site selection purposes, as companies decide where to prop up their next healthcare facility based on predicted traffic in that area.

SafeGraph's Social Distancing and shelter-in-place dataset mapped out
County and state-level social distancing metrics to inform outbreak predictions

Finally, the use of Patterns data can help healthcare companies define trade areas with greater precision. This involves aggregating visitor demographic profiles of these locations to optimize sales intelligence and go-to-market strategies.  This data can be especially valuable for pharmaceutical and healthcare device companies whose products are often tailored to specific demographics. While anonymized prescription data often comes with a 2-week to 2-month lag, foot-traffic data is constantly updated, allowing for dynamic decision-making. In a time where healthcare trends change drastically from one week to the next, this regularly updated data is vital in shaping effective business decisions.

Demand Planning - Preparing for the Uncertain Future

When the pandemic initially broke out in the US, the entire healthcare system was deeply underprepared. This inadequate preparation involved a lack of sufficient ICU beds, ventilators, and respiratory medicines, along with few isolated beds to accommodate care for COVID patients alongside traditional healthcare. Moving into the future stages of the pandemic, it’s anyone’s guess how trends will play out; many predict a second, third, and even a fourth wave to hit the nation while we wait for a vaccine. No matter what these future trends look like, healthcare companies must learn from the initial setbacks of the pandemic to better plan for the potential demand fluctuations in the coming years.

Using Social Distancing Metrics data, these organizations can identify areas of potential outbreak beforehand, enabling them to plan for the subsequent spikes in healthcare demand. Similarly, healthcare companies in one state can observe trends in another state further ahead in the reopening process, to better predict future demand trends in their own state. As a result, healthcare providers and hospital systems will be better prepared to address the inevitable uncertainty of the pandemic, helping ensure that the following waves are properly addressed and their impact is mitigated.

What Comes Next For Healthcare?

Healthcare needs to be data-driven. The industry serves an inherently essential role, more so now than ever. As such, all key players in the healthcare space - whether they focus on pharmaceutical production or clinic real estate development - ought to harness data solutions to address the uncertainty of today’s healthcare environment. If you are an academic researcher or non-profit organization, please join our Data Consortium to get access to SafeGraph data for free. And if you are with a for-profit healthcare business, please contact our team today to learn how these datasets can help your business. In these uncertain times, we are working flexibly with companies to help them navigate this unfolding health and economic crisis.

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